Verizon Enterprise Solutions today announced the expansion of its asset- tracking capabilities, with the introduction of Verizon Networkfleet Asset Guard. Designed to fit seamlessly on Verizon’s existing Networkfleet vehicle tracking solution, Networkfleet Asset Guard runs on the Verizon Wireless network for fixed and moveable fleets such as trailers, yellow iron machines and generators.
A lightweight device with a built-in wireless antenna that easily attaches to a piece of equipment for wireless tracking, Verizon Networkfleet Asset Guard includes a long-lasting battery that allows for reliable location tracking over multiple years. Using Networkfleet’s online application, business owners can track assets alongside fleet vehicles to determine exact GPS locations. Reports and alerts show if and when an asset has been moved, which asset is closest to a particular location or landmark and when assets are moved outside of a predetermined virtual perimeter, or geofence.
“It is becoming more critical for businesses and other organizations to track all of their vital assets, including equipment and vehicles, to help maintain security and operational efficiency,” said Erik Goldman, group president, Verizon Telematics. “With Networkfleet Asset Guard, businesses and government agencies can easily locate their fleet assets and together with our other Networkfleet solutions make sure they are being used properly and efficiently. The combination of Asset Guard’s long battery life coupled with the variety of reports and alerts will help public- and private-sector organizations improve equipment utilization, better manage fleet operations and control costs.”
GPSTrackIt.com has introduced a new Driver Safety Alert that tracks hard turns. Driver Safety Alerts already track and report driving behaviors like rapid acceleration, hard braking, and seatbelt usage. GPSTrackIt engineers have now added the ability to identify “hard turns” to the alert list.
Driver Safety Alerts are used by businesses across the country to help dispatchers and managers understand how drivers are doing in the field. Businesses ranging from small-to-medium sized service companies to large transportation companies with fleets of hundreds of vehicles are able to help drivers understand the importance of safe driving behaviors.
“Drivers represent their employers to the public,” said Eddie Bermudez, GPSTrackit.com’s product development manager. “A vehicle that is driven badly or, more to the point, dangerously, does not reflect well on that employer. These alerts notify managers and dispatchers via email or SMS text messages when their drivers are driving in a potentially aggressive manner.”
Rapid acceleration and hard braking are indicators of bad driving behaviors that also impact a vehicle’s mileage. Seat belt alerts indicate that the vehicle is moving while the driver’s seat belt is unfastened. Statistics show that wearing a seatbelt dramatically reduces deaths and injuries from collisions.
“Hard turns are another driving behavior that puts the driver and vehicle at additional risk,” continued Bermudez. “It also puts additional wear on a vehicle. We were able to utilize the accelerometer and gyroscope technologies built into the GPS tracking devices in a similar manner to the hard braking alert.”
According to the National Transportation Highway Safety Administration’s 2008 report to Congress, “National Motor Vehicle Causation Crash Survey,” about 36 percent of vehicles involved in collisions were turning or crossing at intersections just prior to the crashes.
“When we’re making a turn, we become more vulnerable,” added Bermudez. “Clearly in a left turn situation you’re putting your vehicle in the path of oncoming traffic. But right turns can be hazardous as well to both pedestrians and drivers. Turn too soon and you clip the curb, which doesn’t do much for your wheel alignment. Turn too late and you could end up making a wide turn. If the device on the vehicle transmits a hard turn event, Fleet Manager checks if an alert is configured for that vehicle. If so, it sends the time, date, and location information to the email and/or text recipients.”
Online analysis of athletes’ tactical, technical, and physical capability is the focus of this year’s newly named Galileo Master, Kinexon GmbH.
The 10th European Satellite Navigation Competition (ESNC) recognized the best products, services, and innovations that facilitate the use of satellite navigation in everyday life. At the 2013 awards ceremony, prizes worth a total of about EUR 1 million were presented in 32 categories. The ceremony helped kick off the European Space Solutions conference, which is taking place November 5-7 at Alte Kongresshalle München.
ESNC 2013 gave participants from all around the world the chance to vie for any one of 25 regional prizes. In addition, topic-specific special prizes were sponsored by the following partners: the European GNSS Agency (GSA), the European Space Agency (ESA), the German Aerospace Center (DLR), and — for the first time this year — the European Patent Office (EPO) and Metaio GmbH. Students and research assistants were also encouraged to submit their ideas to the ESNC University Challenge.
Athletic analysis is playing an increasingly important role in modern sport training. The underlying idea — known as the Hawthorne effect — is simple: if you can measure your performance, you can also improve it. Following this principle, two research assistants from Technische Universität München (Germany) founded the company Kinexon GmbH at the ESA Business Incubation Centre Bavaria and developed a cloud-based solution for analyzing and visualizing training data on mobile devices.
Kinexon’s solution kits athletes out with a small, portable location sensor and feeds the resulting data into the cloud by means of a stationary base antenna. This enables users to track and analyze performance parameters and tactical movements down to the centimeter in real time.
In particular, however, it was the solution’s user-friendliness during training and relatively low cost (compared to the camera-based systems commonly seen today) that won over the international jury of experts in the European Satellite Navigation Competition. So far, the high price of such systems has limited their use to professional sport; Kinexon’s system will now give amateur clubs the chance to benefit from adding online analysis to their training activities, as well.
Along with the sport sector, this flexible satellite-based localization system also exhibits huge potential in tapping into further markets, including healthcare, logistics, and unmanned aerial vehicles (UAVs). “We’re pleased to be supporting Kinexon at ESA BIC Bavaria,” affirms Thorsten Rudolph, CEO of Anwendungszentrum GmbH Oberpfaffenhofen. The Kinexon system, the first version of which is set for market launch in November 2013, managed to edge out more than 400 other ESNC entries from nearly 50 countries.
Gerd Gruppe, member of the Executive Board, German Aerospace Center (DLR), conferred the EUR 20,000 grand prize on Kinexon GmbH founders Oliver Trinchera and Alexander Hüttenbrink.
“DLR sets great store in technology transfer,” Gruppe said. “After all, innovations form the basis of economic success and hold considerable potential for society. The ESNC has developed into a driving force behind the innovative use of satellite navigation technologies and a starting point for numerous successful start-ups in Germany, Europe, and the rest of the world.”
Winners of the 10th European Satellite Navigation Competition
In addition to the overall winner, the Galileo Master, the 10th European Satellite Navigation Competition rewarded Special Prizes in seven different categories and 25 prizes to regional winners.
Kipo, a GPS mobile location technology company targeting businesses and families, and Tigo Business Guatemala, a telecommunications company, announced their partnership to launch Localizador Tigo. Localizador Tigo is a smart, user-friendly location platform that allows any type of mobile device to connect to the web and trigger actions by predetermined rules, the companies said.
Triggers include location (arrival, exit, stay) and device events such as battery consumption and speed. Rules determine how the event will trigger and the action to take — send an email, post on social networks, mark a calendar, send an SMS, among others. For example, a company can create a rule that notifies a manager when his messenger delivers paperwork. Every time the messenger arrives to a specific location, the manager would receive an automatic email from the software. The sales manager could also create a rule to identify every time the sales team members have less than 10 percent battery. Once a device runs low on battery the manager would receive an automatic SMS to his mobile device.
The technology also lets team members check in and leave comments when they arrive with clients, information that can be seen in real-time on the web alongside a complete suite of reports to make cell phone devices more useful than ever before. The product can work with any mobile device — smartphones will be able to access the service through native applications and feature phones will rely on cell-towers to report location.
Localizador Tigo gives customers full ownership and control over their data and eliminating contracts, giving them the liberty to cancel service at any time. The product will be launched initially for corporate clients of Tigo Business and will be offered to individual customers on a later phase.
“We believe GPS location technology should be smarter and easier to use. This alliance with Tigo Business makes real-time GPS technology accessible to millions of Tigo customers, which is great because we are allowing more people than ever before receive the benefits this technology provides,” said Rodrigo Blanco, founder and co-CEO of Kipo, Inc.
“In Tigo Business we are always looking to provide our clients with the best and the most advanced tools so they can operate efficiently. We had been searching globally for the leading geolocation platform to incorporate to our mobile devices, and to our surprise we found it in a Guatemalan startup. The Kipo platform is functional, targets the needs of Tigo Business clients and is easy to use. The partnership with Kipo is a perfect next step to continue innovating and offering preeminent products to our clients,” said Hector Jimenez, category manager of Tigo Business Guatemala.
Linx Technologies announces its launch of the high-performance, low-cost RM GPS receiver modules. Using the built-in MediaTek MT3337 chipset, the RM module can simultaneously acquire on 66 channels and track on up to 22 channels, providing standard NMEA data messages through a UART interface. A simple serial command set can be used to configure optional features.
According to the company, the RM receiver module is a cost-effective GPS solution that offers no-frills, basic operation in a compact 15 x 13 millimeter package. The MediaTek MT3337-based RM Series is self-contained and only requires an antenna. It powers up and outputs position data without any software set-up or configuration, making the RM Series easy to integrate, the company said.
The receiver operates down to 3.0 volts and has a low tracking current of 12mA. The module has built-in receiver duty cycling that can be configured to periodically turn off the module for added power savings. This low-power consumption helps maximize runtimes in battery powered applications, such as consumer recreational positioning, marine, location and tracking, cargo tracking, and other asset monitoring systems.
In addition, the available GPS Master Development System connects a RM Series Evaluation Module to a prototyping board with a color display that shows coordinates, a speedometer and compass for mobile evaluation. A USB interface allows simple viewing of satellite data and Internet mapping, as well as custom software application development.
By Miguel Torroja, Steve Malkos, and Christophe Verne
Users of smartphones, tablets, and other devices expect position with the highest level of accuracy, always available, with the least amount of power consumed. One recent improvement fulfilling this demand involves operating-system services for location on smartphones, and the evolution towards lower power solutions.
“Please connect to a charger — The battery is getting low: less than 15 percent remaining.”
Handsets are battery-supplied devices, and a user’s tolerance for features is driven by battery consumption. There are many examples of technologies where users do not run certain hardware or features because it will consume the battery and make the phone useless within a short period of time.
The application processor (AP) of a handset device is very powerful, and is the part that consumes most of the battery life. Today’s smartphone multicore application processor is faster than many desktop computers that are just a few years old. Whatever the application, when it uses the AP, it can draw up to hundreds of milliamperes (mAs).
For the last few years, the trend for GNSS has been host-based positioning. Host-based designs have less logic on the GNSS integrated circuit (IC) and employ the host AP for a portion of the positioning computation. This strategy has three advantages:
Shares memory and code resources with the application processor.
Reduces the cost of the dedicated GNSS hardware.
Sharing the processor makes sense since it is already running.
Traditionally, when the GNSS solution was running, a navigation application that utilized the AP was also running.
However, when we only want to compute GNSS positions in the background, and we do not need a third-party application running on the AP, a host-based IC architecture is not the optimal solution with regard to system power consumption. This article explains some of the technologies used to compute a GNSS position using an ultra-low power (ULP) hybrid solution that combines the classic host-based GNSS architecture with a host-offload architecture that minimizes the use of the AP.
We discuss here two applications that benefit from a host-offload architecture: geofencing and position batching.
We will review the requirements for a platform to support a new hybrid GNSS positioning solution. Different host-offload technologies for geofence, such as GNSS, Wi-Fi, and Cell-ID, will be compared. Broadcom’s ultralow-power host-offload GNSS solution supports any operating system. We focus here on Android’s operating system because it is the most open OS.
Always-on Applications
Geofencing is an application that sends reports or triggers alarms when a predefined area is crossed. For example, users can be alerted to discounts with e-coupons when walking through a mall, or to “don’t forget the milk” — users can set their own reminder notifications based off of location; also, social networking. One example of location-based reminders is through Google Keep, which uses Android’s Geofence APIs on platforms that support hardware geofencing; this application will automatically take advantage of the hardware geofence solution.
Geofencing applications run in the background for long periods of time, and their main task is to compute positions (fixes) without the need of assistance from other applications. An ultra-low-power GNSS position solution, or always-on positioning solution, is desirable for these scenarios. Typical applications require notifications when entering or exiting a geofence area, or require periodic reporting of user positions relative to the fence.
Geofencing is not something new. API support has been provided in mobile OS for many years, but only now can it be used without draining the battery, thanks to this new host-offload architecture.
Figure 1 shows a circular geofence boundary and an alarm. In that example, the alarm was triggered when entering the fence.
Figure 1. Alarm when the vehicle enters a geofence area.
Breadcrumbing or position batching pertains to storing of positions, referred to as crumbs, which are accumulated for a certain amount of time and then pushed all at once to the application. Examples would be fleet or asset tracking applications, or people that wants to track their position while they are running.
Currently, Android does not support breadcrumbing as a native feature. There is some ongoing work, and APIs are being defined.
GNSS Positioning Models
Before smartphones, the dominant GNSS hardware architecture employed a system-on-chip solution. The position/velocity/time (PVT) comes directly from the hardware, and all the computations are done in the GNSS IC.
On-Chip Positioning requires two things: a powerful-enough central processing unit (CPU) and lots of memory. The increase in CPU and memory performance are not free; they translate directly into more power and higher manufacturing costs.
The RF block in Figure 2 is intentionally drawn with a similar size to the CPU and memory, to emphasize the need for higher resources for a complete on-chip solution.
Figure 2. On-chip solution.
Host-Based Solution. GNSS positioning requires dedicated hardware, complex software, and protocols. This complexity led GNSS providers to move parts of the software out of the IC to the AP.
Using a mobile phone’s AP for position computation is one method of reducing the CPU and memory power footprint from the GNSS IC. At the same time, it also increases the power consumed by the platform needed to compute GNSS position, since part of the computation is not performed on the host-based IC. APs may consume approximately 100 mA just to be operational.
Figure 3 shows a typical configuration with dedicated GNSS hardware and a generic AP. In host-based mode, both the AP and the GNSS IC run in parallel when computing positions. The AP controls the GNSS hardware.
Figure 3. I/O connections in on-host positioning.
With this type of shared architecture, shown in Figure 4, the CPU and the memory on the GNSS IC are reduced, shrinking the size of the chip and reducing power consumed by the chip. In Figure 4 we see that the AP is communicating with the dedicated hardware, and the final PVT is computed by the AP. This solution fits well in many applications, such as navigation, where the AP has to run a mapping application at the same time.
Figure 4. Host-based solution.
Hybrid Positioning. For geofencing, we need a hybrid model, one which keeps GNSS IC complexity similar to the host-based architecture, but also offloads some of the host-based positioning so that the host can go to sleep.
In Broadcom’s hybrid mode, the AP does not need to run when GNSS positions are computed. Broadcom’s hybrid IC does not invoke the host AP often, and thus achieves an even lower power footprint. The CPU on the GNSS IC used for computing position is a dedicated one. It needs to be carefully chosen because it has to be powerful enough to compute positions and be as power efficient as possible. All this is done while keeping the GNSS IC area size in mind, to control cost.
Detailed analysis and steps were considered to ascertain the minimum requirements for the CPU and other resources to best accomplish the on-chip positioning task.
Other considerations: the GNSS IC must be powered even when the AP is suspended, and the GNSS IC must be capable of waking up the AP. Figure 5 shows a possible implementation using a dedicated I/O signal controlled by the IC to wake up the host AP.
Figure 5. I/O connections in hybrid positioning.
With this architecture, the host AP will still be needed to provide some assistance data to the GNSS IC. The assistance provided allows the GNSS IC to not invoke the host AP often and thus achieve an even lower power footprint.
Geofencing Methods
Certain OS application APIs have been supporting geofencing for many years. Currently, we can find geofencing APIs in most of the mobile OSs in the market.
There are four main types of geofencing: GNSS software geofencing, GNSS hardware geofencing, network software geofencing, and network hardware geofencing (Table 1).
Table 1. Geofencing methods.
GNSS Hardware Geofencing. In this method, the one described in detail in this article, the OS initiates a request and offloads the areas of interest to the hardware. After that, the AP can go to sleep and the hardware is responsible for computing positions and checking the areas of interest. This method basically relies on GNSS hardware to compute positions and check the programmed fences.
GNSS Software Geofencing. Here, the OS initiates regular fixes to a host-based GNSS IC design. Then it invokes both the AP and the GNSS IC at the same time to check against the defined fence areas.
Network Geofencing. In this method, the OS requests network IDs from the hardware (that is, baseband modem Cell-ID and Wi-Fi access points). The OS uses different positioning technologies to compute position. This usually requires a connection to a server to retrieve location information about the different IDs. The position is used to check the geofences.
In network hardware geofencing, a set of network IDs is offloaded from the OS to the network hardware ICs. The hardware can poll for these IDs, and wake up the host when found.
Network versus GNSS Geofencing
A good geofencing solution combines both network and GNSS methods because each solution benefits from each other.
GNSS positioning solutions compute positions in open-sky environments with accuracy to a few meters and have worldwide coverage. However, they cannot work in deep indoor spaces.
Network geofencing using cell IDs is quite inaccurate, but works very well indoors. Network geofencing using a Wi-Fi access point provides reasonable accuracy, but location of the access points is not always known and it does not have full coverage.
Geofencing in Android 4.3. The API for applications supports geofencing. Starting from the first version of Android, the application just initiates a proximity alarm and will get an event when its boundaries are crossed. The OS is responsible for notifying the application when such an event occurs, and can use any technologies it sees fit.
The API that applications use is very simple. The monitoring is handled by the OS and is hidden to the application (for example, technologies, periodicity of checks, and accuracies).
Software Geofence in Android. Software geofencing has been the default method until recently, as there was no native hardware support. In this mode, the host-based GNSS positioning engine is started like any other position request. The Android framework is the one dealing with the monitoring of the geofences, and therefore, the AP must run continuously to handle periodic position checks. That means the software-geofencing logic is mainly in the framework layer of Android (see basic layers diagram shown in Figure 6).
Figure 6. Android framework.
More recent versions of Android dropped the support for software-based geofencing in favor of a host-based GNSS system, likely because of the big impact on the battery. Broadcom developed a low-power GNSS hardware solution for geofencing.
Hardware Geofence in Android. Starting from Android 4.3, a new interface is available to use hardware geofencing. This interface is not visible to the application, and it is only used as a low-level interface. To support the new hardware-geofence interface, the native driver only has to register to a new GNSS interface defined in the native hardware abstraction layer (HAL) of Android.
There are other protocols known to support geofencing. Table 2 provides a short list.
Table 2. Geofencing support on different platforms.
Broadcom Hybrid Positioning
Android defines interfaces to the hardware, referred to as the HAL.
GNSS Host Software. GNSS providers need to comply to the HAL interface, which is at the Java native interface (JNI) level. Below the JNI lies the GNSS host software (Figure 7).
For the host-based solution, the GNSS host software handles most of the heavy computing.
For the hybrid solution, the GNSS host software does some of the heavy computing, but positions are computed inside the GNSS IC.
To support this new hybrid solution, two main changes are required compared to the usual host-based solution, as described below.
First, the hybrid GNSS IC must be autonomous while the host AP is sleeping. This implies that some power domains are maintained when the GNSS is in use. This typically means at least one of the outputs of the power management unit (PMU) should be dedicated to the GNSS only (Figure 8).
Figure 8. Power domains.
Second, the GNSS IC must be able to wake up the host AP so as to send geofence notifications, or to request assistance data. This is usually done through a dedicated pin.
Acquisition and Sleep Period. Most of the power in the GNSS IC is used by the radio and analog part. To reduce power, this part is switched on only during acquisition. As soon as enough measurements are observed, the radio part is switched off while the digital part computes a fix.
After each computed position, the GNSS IC can go into a deep power-saving mode until the next acquisition. The distance to the closest fence in conjunction with the user speed is used to determine when to compute the next position (Figure 9):
Figure 9. Start fix decision logic.
Once the GNSS IC starts computing positions, the AP can go into sleep mode (Figure 10). Total power per position computed is reduced, and the time between fixes is no longer constant, as shown in Figure 11.
Figure 10. Sleep time between fixes.Figure 11. Duty cycling.
In Figure 12, the lower square-shaped pattern corresponds to a position computation from the hardware GNSS IC. Once we have an alarm, the host has to be woken up and we can see the impact in power in the big peaks after a position is computed.
Figure 12. Power graph.
Alarm Triggering
When a geofence area is crossed, the GNSS IC needs to wake up the AP. This is achieved using a dedicated interrupt pin. After asserting it, an alarm and geofence status is sent to the AP.
Power Consumption. We calculate the total average current by splitting it into three components, as shown in the following formula:
Some of these parameters are set by the host: for example, how often the fix should be computed. The extra current drained by the GNSS IC is the one defined by
∆I is the change in current drain when computing positions.
We can also express this formula based on the average number of position attempts:
where Tp is the average time between fixes (the time the GNSS IC stays in sleep).
Table 3 illustrates some theoretical ∆I current savings with respect to Tp.
Conclusion
As APs become faster and faster, their power consumption goes up. A novel hybrid GNSS receiver has been presented, which offloads some of the host-based processing into the GNSS hardware, offering ultra-low system power consumption versus the traditional methods. The new hybrid positioning solution is a good approach for always-on applications that need to have location information always available, without requiring the host to be running, as is the case with geofencing and breadcrumbing.
References
We would like to thank Jason Goldberg, Frank van Diggelen, and Manuel del Castillo, all of Broadcom, who reviewed this article and spent many hours with us discussing the topics point by point.
Miguel Torroja is a principal software developer at Broadcom. He has an M.Sc. in electrical engineering from Ramon Llull University, Barcelona. Since 2011, he has been working on the design and development of algorithms for optimizing power consumption in GNSS host-offload solutions.
Steve Malkos is a senior program manager at Broadcom. He has a B.S. in computer science from Purdue University. He has been active in the development of A-GNSS technologies such as hybrid location services, long-term predicted orbits (LTO), Broadcom’s worldwide reference network (WWRN), and secure user-plane location (SUPL). He has five patents issued and 16 pending.
Christophe Verne is a manager of software engineering at Broadcom. He has an M.S. in electrical engineering from Ecole Centrale, Paris. He has been involved in the development of GNSS and A-GNSS technologies at EADS, Sagem, Global Locate, and Broadcom, where he has been working on low-power host-offload positioning.
Linx Technologies announces its launch of the self-contained, high-performance FM GPS receiver modules. At 15 x 13 millimeters in size, the MediaTek MT3339-based FM Series gives the module fast lock times and high position accuracy even at low signal levels, the company said.
The module’s very low power consumption helps maximize run times in battery powered applications, such as positioning and navigation, location tracking, marine, and asset management, according to Linx Technologies.
Using the built-in MediaTek MT3339 chipset, The FM module can simultaneously acquire on 66 channels and track on up to 22 channels, providing standard NMEA data messages through a UART interface. A simple serial command set can be used to configure optional features.
The GPS receiver is completely self-contained and only requires an antenna. It powers up and outputs position data without any software set-up or configuration. As a result, the FM Series is easy to integrate, the company said.
With built-in hybrid ephemeris prediction technology, the FM Series predicts satellite positions for up to three days and delivers start times of less than 15 seconds under most conditions.
In addition, the available GPS Master Development System connects a FM Series Evaluation Module to a prototyping board with a color display that shows coordinates, speedometer and compass for mobile evaluation. A USB interface allows simple viewing of satellite data and Internet mapping, as well as custom software application development.
Companies like Drawbridge indentify a user’s devices across platforms.
In the location business, we used to talk about tracking — namely, vehicle tracking. We stopped using the term as it sounded too close to Big Brotherism. Vehicle and employee tracking is much more prevalent today, but we have delicately renamed it “mobile resource management.”
Tracking is back in the news, and it is rightfully being called what it is, tracking. You may have seen the New York Times article about new ways people are being tracked via their mobile phones and other devices.
Tracking mobile phone behavior hasn’t been prevalent, because mobile apps don’t use cookies, the small files that can watch our behavior on our desktops and laptops. This has changed. Now Internet advertising companies like Drawbridge are using powerful algorithms to analyze anonymous browsing patterns on devices and look at the dates and times, location and websites visited, and user activities on sites. The companies can determine that a mobile phone, home computer, work computer and tablet belong to the same person. The devices do not need to be connected for the match to be made. In a household full of people and devices, these companies can even distinguish among users.
This isn’t in its infancy. One company alone says it has matched 1.5 billion devices this way. The incentive of the industry is to arm advertisers with behavior knowledge to enable hyper-personalized ads on the device that makes the most sense. The ad may be delivered on one device based on a person’s activity on another device. For instance, Greg is looking at a website for basketball shoes at his computer at work. He goes home and gets an ad for those shoes on his tablet, and it maybe a hyper-local ad for a store where he often shops. The ad may come at a time that he is primed to shop, on the device he will likely be using then. Mobile advertisers that are exploiting this data include Drawbridge, Flurry, Velti and SessionM. Companies that are advertising based on this mobile tracking data include Ford Motor, American Express, Fidelity, Expedia, Quiznos and Groupon.
As we know, phone data is not the sole interest of commercial companies. It is of interest to the government as well. This month, the National Security Agency (NSA) admitted that it was tracking the location of the U.S. population. Between 2010 and 2011, the NSA used cell towers to locate Americans. The NSA claims that it obtained the data, but didn’t use it.
What’s next? There is something left that mobile advertisers still haven’t figured out. They have no sure way to know the results of an ad placed on a mobile phone. Has the person viewed the ad and gone to the website on their computer, or walked into a store and placed an order? It probably won’t be a mystery for long.
ORBCOMM Inc., and Savi Technology have announced a strategic relationship to provide advanced location-based monitoring solutions to government and commercial markets.
ORBCOMM is a global provider of machine-to-machine (M2M) solutions, and Savi Technology is a provider of sensor-based analytics and radio-frequency identification (RFID) solutions.
ORBCOMM and Savi have submitted a proposal in response to the U.S. Army RFID IV project, which will provide both ISO18000-7 RFID tags and a suite of satellite solutions for military logistics support. ORBCOMM’s GlobalTrak division has been a leading player in providing military Enhanced-In-Transit-Visibility (EITV) solutions to the government market since 2008, and Savi has been a market leader in military RFID solutions, enabling it to offer vast market experience with the right blend of technology platforms for this proposal.
“The combination of ORBCOMM’s satellite expertise and broad network service portfolio with Savi’s state-of-the-art RFID technology offers a full spectrum of innovative monitoring solutions to our collective market base with focus on our government and international customers,” said Marc Eisenberg, Chief Executive Officer of ORBCOMM. “Although RFID and satellite tracking have traditionally been divergent technologies, the synergy of these solutions within a common operating environment creates a seamless transition from infrastructure to wireless-based location services for tracking and monitoring high-value assets.”
“By bringing two market leaders with highly complementary technologies together, we have created a best-of-breed solution for our customers in both government and commercial markets,” said Bill Clark, chief executive officer of Savi Technology. “This relationship will support Savi’s operational analytics capabilities by providing additional ways to collect critical data and deliver timely and reliable operational intelligence to our customers. We look forward to partnering with ORBCOMM on RFID IV and other global opportunities in the near future.”
u‑blox has announced the launch of its newest core positioning platform, the u-blox M8. The new chip forms the basis of u-blox’ upcoming line of positioning modules, which are able to acquire and track different satellite systems concurrently to achieve higher accuracy and reliability.
Supporting all deployed as well as upcoming GNSSs, the platform is based on the UBX-M8030 concurrent multi-GNSS receiver IC which is able to track American GPS, European Galileo, Japanese QZSS, Russian GLONASS, and Chinese BeiDou satellites.
Concurrent tracking of GPS (QZSS) and GLONASS or BeiDou, or concurrent tracking of GLONASS and BeiDou satellites increases performance for applications requiring maximum availability and accuracy. The chip is prepared for the European Galileo system through a future firmware upgrade once the constellation is fully available.
The new platform will ultimately support special functions such as Automotive Dead Reckoning and precision timing to support a wide variety of vehicle, industrial and consumer applications.
To further improve acquisition performance, u-blox’ globally available “AssistNow”assisted-GNSS service for accelerated positioning has been extended for u-blox M8 products; the service supports both GPS and GLONASS, and the validity of downloaded assistance data is now able to support offline operation for up to 35 days.
“With the proliferation of multiple new GNSS systems beyond GPS, our u-blox M8 platform is designed to take full advantage of the increasing number of visible satellites to further increase accuracy and availability, particularly in urban and vehicle-based applications,” said Daniel Ammann, executive vice president, head of the Positioning Product Centre, and co-founder of u-blox, “At the same time we realize the ongoing requirement for extremely low-power and cost-sensitive portable applications where operation with a single GNSS system is more than sufficient. That is why we will continue to offer both u-blox M8 and u-blox 7 based products to the market.
The new u-blox M8 chip is at the heart of u-blox’ next generation of positioning modules based on the company’s popular MAX, NEO and LEA module form factors.
u-blox M8 chips feature low power consumption in concurrent reception mode, thanks to an innovative single-die architecture combined with sophisticated software algorithms. The extended supply voltage supply range and 1.8 V/3.0 V I/O compliance supports a wide variety of system architectures. Sophisticated radio architecture and interference suppression using active jamming detection ensure maximum performance even in GNSS hostile environments. UBX-M8030 chips are available in miniature WL-CSP (2.99 x 3.21 x 0.36 mm) and QFN (5.00 x 5.00 x 0.59 mm) packages. The chip is also available in automotive quality grade according to AEC-Q100.
The new platform maintains backwards compatibility with u-blox 7 modules and QFP chip products which remain in the company’s portfolio as the industry’s lowest power standalone satellite positioning receivers. u‑blox’ capability of delivering GNSS technology in both integrated circuit and form-factor consistent modules provides maximum design flexibility and protects customers’ development investments over successive product generations.
First samples of the multi-GNSS receiver chip UBX-M8030 are available for customer evaluation. Soon, module customers can easily migrate to the MAX, NEO, and LEA form factors, u-blox’ popular, industry-standard module form factors.
Spireon, a Mobile Resource Management (MRM) and Business Intelligence Solutions provider, has completed an initial phase of the integration of its industry leading GoldStar GPS solution with Frazer’s Dealer Management System (DMS).
Frazer is a provider of software solutions that auto dealers across the nation use to grow their business and increase their productivity, including its comprehensive DMS, which has functions such as dealer inventory management, credit application processing, electronic contracting, set up bank contracts and BHPH deals, dealer management tools, loan servicing and accounting systems.
Joint customers of GoldStar GPS and Frazer can now experience online access to a single solution to execute critical commands. The integration allows dealers to execute commands directly from their Frazer application, interface saving customers time and improving their ease of use. Dealers can monitor and take action on their collateral within their day-to-day dealer management application. Key features of the initial phase of integration include the ability to conduct an on-demand locate, and disable and re-enable the starter interrupt.
“Spireon’s new partnership with Frazer is another example of Spireon’s ongoing quest to improve the ease of use of our solutions through key partnerships to enhance the customer experience for effective collateral management, vehicle tracking and risk mitigation. GoldStar GPS and Frazer users will experience a platform that will allow them to do more.” Explains David Meyer, Executive Vice President of Spireon.
“We have watched the incredible growth of GoldStar GPS and are very excited about now offering Goldstar GPS as an integrated feature within the Frazer DMS. This will make a lot of dealers’ lives just a little bit easier.” Michael Frazer, President.
If we wish to obtain consistently usable positions indoors using a mobile phone, we can augment its GPS or GNSS receiver with other unfettered sensing technologies such as gyroscopes and accelerometers supplemented by radio signals of opportunity. But is all of this actually feasible? The authors have conducted tests of a multi-system approach to positioning indoors with favorable results.
INNOVATION INSIGHTS by Richard Langley
IS GPS REALY A GLOBAL POSITIONING SYSTEM? Well, that depends on your definition of “global.” If it means that GPS operates well all over the world in environments where it was designed to work, then, yes, it is a global system. But, if you define global as meaning that GPS operates well everywhere not only outdoors with a clear view of the sky but also indoors and in other restricted environments, then (as some have argued), GPS is not truly global.
So why doesn’t GPS work (for the most part) indoors? Our mobile phones do and they use similar bits of the electromagnetic spectrum. The basic problem is that the signals from GPS (and other GNSS) satellites are just too weak to easily penetrate buildings. They are more than strong enough to yield excellent positioning, navigation, and timing (or PNT) results if the antenna connected to the receiver can “see” the satellites unobstructed. But even outdoors, trees, buildings, and mountains can block the signals from one or more satellites at a time. And indoors, the signals are usually attenuated by walls, floors, and ceilings so much that a conventional receiver cannot lock onto them.
Receiver manufacturers have developed more sensitive receivers that can operate, at least to some degree, indoors but with a good antenna. And receiver chips or modules with this more sensitive technology are often found in modern mobile phones. But they don’t typically provide reliable indoor positioning because they are being used with inexpensive, suboptimal antennas. Some potential improvement in indoor positioning capability is possible by supplying the receiver with satellite orbit and clock information through the mobile network rather than having the receiver acquire this information directly from the satellite signals. This assisted-GNSS technique allows a receiver to work with weaker signals. But it is not a panacea. Gaps or holes still exist for positioning indoors or in other obstructed environments, prompting one industry wag to liken GNSS coverage to Swiss cheese.
So, what are we to do if we wish to obtain consistently usable positions indoors using a mobile phone? As we will see in this month’s column, we can augment or bypass its GPS or GNSS receiver with other unfettered sensing technologies such as gyroscopes and accelerometers. These devices can be made very small using microelectromechanical technology and are already included in some mobile phones.
However, there are some issues with these devices for positioning, not the least of which is rapid position drift. We can restrain the drift by using magnetometers, for example – also present in some mobile phones. We can also use radio signals of opportunity to help in the positioning – signals available in the phone such as multi-generation mobile signals, Bluetooth, and Wi-Fi through their signal strength “fingerprints.” But is all of this actually feasible?
The authors of the article in this month’s column have conducted tests of such a multi-system approach to positioning indoors with quite favorable results. Are we at the stage of accurate positioning (and tracking) everywhere? Not quite, but we are getting closer.
“Innovation” is a regular feature that discusses advances in GPS technology and its applications as well as the fundamentals of GPS positioning. The column is coordinated by Richard Langley of the Department of Geodesy and Geomatics Engineering, University of New Brunswick. He welcomes comments and topic ideas.
In recent years, there has been increasing interest in ubiquitous positioning — accurate location fixes in any environment, outdoors and indoors. We have all become used to the availability and performance of global navigation satellite systems (GNSS) for accurate outdoor radio positioning with a reasonable degree of reliability and availability. However, indoor radio positioning is more challenging because GNSS signals do not penetrate buildings well, and we must instead rely on local infrastructure and other available inputs to aid the user.
Indoor radio positioning is, however, available to the general public today through the use of signal strength fingerprint databases managed and provided by third-party providers such as Skyhook. These typically use Wi-Fi and cellular signals because of their ubiquity and the prevalence of appropriate receiver circuits in consumer devices. The user can also access the fingerprint database through these media. These systems, therefore, have two clear constraints: the database must have been previously built via some form of survey process, and the user must have a data connection available to obtain it. A more scalable system would not rely on such constraints, and would instead develop its own database during operation.
The benefits of such a system are significant: it can provide location-based services, situational awareness, and asset tracking in new and unknown environments for consumers, emergency services, the military, lone workers, security personnel, and autonomous vehicles. There is no requirement for a data link to function, nor any prior surveying of the radio environment, nor any other prior knowledge such as a floor plan or map. However, the system can also be used to quickly and easily generate maps of the radio environment or floor plans, which can be beneficial for organizations wishing to provide positioning services to the public using a simpler positioning method; that is, this method can be used to rapidly survey an area and generate a signal fingerprint database for other users to exploit. Best of all, all of this can be achieved today in real time using an app for a consumer smartphone.
The Digital Swiss Army Knife
The last couple of decades have seen steady improvements in a variety of sectors that have led to new and flexible navigation capabilities — and all of these improvements can now be found in the little chunks of silicon, plastic, and glass in our pockets and handbags. Moore’s Law and the miniaturization of electronics have enabled us all to carry handheld programmable supercomputers around with us every day. Microelectromechanical systems technologies and the demand for better gaming and augmented reality experiences on our smartphones mean that any new phone contains the same types of sensors for enhancing user experiences that cruise missiles and smartbombs use to ensure they hit their targets precisely.
Finally, your smartphone contains more radios than you probably realize. GPS (or GNSS); 2G, 3G, and 4G network radios; near field communications, like RFID; Bluetooth; Wi-Fi; and even a VHF FM chip might be tucked away in there somewhere. The near future is likely to bring a “whitespace” radio (using re-assigned vacated spectrum) along with a 60-GHz wireless USB transceiver. We are bathed in a phenomenal number of radio signals as we go about our daily lives, completely oblivious to the rich tapestry we are walking through — an invisible, permanent, detailed map just waiting to be sensed by our smartphones and annotated for our navigation purposes.
So, just what is possible with a commodity smartphone and its arsenal of features?
Pedestrian Motion Modeling
We can begin with the accelerometers, magnetometers, gyroscopes and barometers found in recent smartphones. These sensors collectively form an inertial measurement unit (IMU) that can be used to track the motion of a user through any environment, regardless of the availability of GNSS (at least in theory).
Unfortunately, there are many stumbling blocks in the way for any new navigator starting down this road. The standard approach for inertial navigation involves using the gyroscopes to maintain an estimate of the orientation of the device relative to the Earth, and to integrate the accelerometer measurements to calculate the system velocity and subsequently the change in position with each measurement update. A key aspect of this process is the removal of the effect of gravity, which requires us to estimate the value of the local gravity field strength (which varies with location across the globe) and its direction (which we do based on the estimated orientation of the device according to the gyroscopes). There are inevitably some errors associated with the estimates of both of these quantities.
In addition, the sensors themselves suffer noise, biases, instabilities, non-linearities, and other effects that only decrease the system performance further. These errors accumulate over time because the position and orientation estimates at any moment depend on the cumulative sum of all measurements since the start of the journey. The result is rapid and unbounded growth in position and orientation error. The cost of the sensors is, of course, tightly correlated with their quality, and so the rate at which the navigation performance degrades. The quality of the sensors in smartphones is so low that this approach is rendered useless within the first few seconds of use. To make progress we must apply regular position corrections to the system by applying external constraints or incorporating external sensor measurements.
Alternative. GNSS measurements provide constraints and corrections for inertial navigation systems, but here we are considering operating indoors where these are unavailable or severely degraded. An alternative solution for most smartphone users is to use the inertial sensors in a different manner, within a so-called pedestrian dead- reckoning (PDR) approach. Here, it is assumed that the device being tracked is held by (or attached to) someone walking in a manner that can be modeled. The inertial sensors are not now used to reproduce the full 3D motion of the device at the update rate of the sensors, but instead used simply to detect stepping motions and to infer that the user has moved some number of steps. Looking for patterns in the accelerometer data where minimum and maximum thresholds are exceeded within a certain time window is a surprisingly robust step counter when the user walks “normally” (more complicated actions such as side steps and stumbles require more complex algorithms). The smartphone can estimate its orientation by fusing together its gyroscope (which offers good short-term orientation-tracking) and its magnetic compass (good long-term orientation-tracking with periodic fluctuations from local magnetic anomalies). The step length of the user (a surprisingly consistent quantity) and any bias in the gyro-smoothed compass heading can both be measured and modeled during periods of GNSS availability such that the best possible estimates are available when GNSS is lost.
FIGURE 1 shows the functional flow diagrams for a strapdown inertial navigation system (top) and a PDR system (bottom). Note that the PDR scheme accumulates error more slowly than the INS scheme (involves fewer integrations over lower-rate data) but is heavily dependent on the performance of the gait recognition, floor-change detection, and step-length-estimation algorithms.
FIGURE 1. Functional flow diagrams for a strapdown inertial navigation system (top) and a pedestrian dead-reckoning system (bottom).
However, PDR techniques still accumulate error, resulting in gradual position drift, but with much higher performance than would be achieved by integrating the raw data in the traditional INS manner. Typical PDR schemes can track the user with an accuracy of a few percent of the distance walked, although this performance degrades with any un-modeled motions that confuse the step detector, such as infrequent backward or sidesteps. So how do we deal with this issue?
Machine Learning
The accuracy of PDR schemes is dependent on the validity of the pedestrian motion model. Any un-modeled action has the potential to generate false positive events in the step detector and hence contribute to position error. Users may stoop, crawl, jump, hop, or shake their device while static — motions that are all very difficult to unambiguously discriminate in raw sensor data.
There are many approaches to solving this problem of gait recognition, and most exploit machine learning techniques. The basic principle of supervised machine learning is that a large set of labeled training data (that is, lots of manually categorized data of each type) is analyzed by a computer in order to extract patterns, statistics, or certain measurement sequences from the inertial sensor measurements that reveal the type of step that was taken. In unsupervised learning, the clusters and categories within the data must be found by the algorithms themselves.
The outputs from such algorithms are typically thresholds, signatures, and other learned metrics that can be installed in a smartphone and used to dynamically classify movements. It is also possible to deploy the learning algorithms on the device itself so that it can learn what the particular user’s signatures are to permit better step and gait detection (like training a speech-recognition program to understand your accent). A simple example of this is running an error-state Kalman filter while GNSS signals are available to determine the user step length and to detect any background compass bias that is corrupting the system.
A problem yet to be resolved for PDR schemes is a basic physical one: the laws of physics are the same for an object at rest as for one moving at constant speed. This means that it is theoretically possible for a suitably skilled person to simulate the “already moving at constant velocity” version of any of these motions while static by moving the device in just the right manner, effectively spoofing as many steps or motions as they like. The opening and closing phases of a journey (that is, the very first and last steps) are critical in distinguishing real and spoofed motion if only inertial sensing is used to disambiguate real and spoofed motion through an environment. We will, however, return to this problem in a moment.
Simultaneous Localization and Mapping
The application of machine learning can be extended to the entire indoor navigation problem using a technique called Simultaneous Localization and Mapping (SLAM). A key aspect here is the hypothesis that there are some measurements that can be taken within an indoor environment that vary rapidly on the spatial scale but only slowly on a temporal scale. These opportunistic measurements are typically of radio signal strength (Wi-Fi, cellular, television, VHF FM, and so on) and magnetic field strength, although in principle many other metrics could be used such as light level and temperature. They are deemed to be opportunistic because they already exist in the environment and have not been generated specifically for this positioning system. Moving along a corridor is expected to result in a particular sequence of measurements that is repeatable on the next visit to that corridor with a confidence based on the time since the last visit. Tight agreement is expected within the next few minutes, close agreement within the next few days, and so on. It is not expected that these fingerprints will necessarily be valid for months or years, as objects may move around the environment; for example, large items may be relocated and Wi-Fi access points may be moved. The ability to exploit the expectation of high repeatability over short time periods of a few hours is the key to developing a system that can learn about its environment and improve its performance during use.
As the device moves through the indoor environment (with position estimate driven by the PDR estimation), the opportunistic fingerprints are captured and stored. If the device returns to a region it has been in before, then it will record a sequence of measurements that will agree closely with the previous sequence that was recorded in the past. This provides a constraint to the system: whatever path was taken in between, it has converged with a section of its historical path and “closed a loop.” Any offset in these two path sections at this point reveals the inertial error that has accumulated during this loop. The system can therefore correct its own inertial error growth, allowing extended operations in GNSS-denied areas.
Fingerprint Maps. The gathered opportunistic measurements can also be used to generate fingerprint maps of the areas that can be shared with other users to allow them to accurately position themselves within those areas in the future, reducing everyone’s reliance on PDR schemes and removing the need for environments to be manually surveyed for their environmental maps. The maps are automatically calibrated and corrected by the SLAM process. As more users operate in the environment and more data accumulate it is easier to identify and remove erroneous data that does not fit into the consensus being formed by the “intelligence of crowds.” This opportunistic navigation scheme can also feed back into the PDR scheme to aid with motion detection — as fingerprints are expected to vary on a fine spatial scale as users move through an environment. They can be used to detect when a PDR device is in reality static, but being moved in a manner that is erroneously triggering the step-detection routine.
FIGURE 2 shows a plot of the magnetic-field-strength variations recorded during four walks down the same corridor of a building at four different times of day on four different days. The traces have been manually aligned by the clear drop in field strength at step number 40. A fixed step length was assumed, and the relative stretching evident across the traces is due to small differences in walking speeds across the tests. Step-length changes can be estimated using changes in the stepping frequency, and the typical step length can be observed and calibrated during periods of GNSS availability.
FIGURE 2. Repeatability tests of the magnetic field strength from four walks along an indoor corridor at four different times during the day on four different days.
There are two distinct classes of SLAM algorithm for PDR. The most common class involves an iterative batch process applied after the data have been collected (that is, offline). This process (which might be least-squares fitting or maximum likelihood estimation, for example) identify loop closure points and provide an optimal joint estimation of the path taken by the user that satisfies these constraints and the raw odometry data as much as possible. The
Wi-Fi SLAM approaches, Gaussian Processes Latent Variables and GraphSLAM, both use such schemes. The results are typically robust, but the offline processing stage can be lengthy.
SLAM can, however, be performed in real time, even on a smartphone, by exploiting an efficient multi-hypothesis scheme. As the user moves, we retain multiple hypotheses for their position and, crucially, record the history of each hypothesis. This is typically done using a particle filter, where each particle represents a unique hypothesis. In this context, we must store the tree of ancestors for each particle at each epoch. When we detect a loop closure, we prune the history to remove all hypotheses that did not result in a loop closure at that point and therefore dynamically correct our errors. Note that each particle can even be assigned different parameter values, such as step length or heading bias, and if a gait detection scheme cannot confidently identify the type of step taken, new particles representing every possible user motion at that epoch can be generated.
Occupancy Grid. Rather than running a specific loop closure algorithm, an occupancy grid is used, whereby the environment is defined by a grid of small cells, for example, one meter by one meter squares. As each particle propagates, representing a hypothesis of the user path, it posts its identity and the current step number into the occupancy grid. As the user continues to move, the particles check the grid cells they move through for any previous visits. If a particle has visited a cell before, the current sensor measurements are compared to those recorded at the time of the last visit. If there is close agreement (typically scored using metrics such as the Euclidean or Mahalanobis distances) then that particular particle is given a high weight. Conversely, poor agreement results in a low weighting.
The entire particle cloud can be reweighted accordingly with low-scoring particles being killed and high-scoring particles being duplicated. The result is the particle cloud collapsing towards the region of close agreement between old and new sensor measurements. Because the occupancy grid contains the historical path of each particle stored via their IDs and step-number sequence, when a reweighting of particles occurs, the historical path of the user is updated and improved accordingly along with the current estimate of the user’s location.
The SLAM estimate can be improved by many types of observations, not just loop closures. If the user moves outside and confident GNSS locations become available, these can also be used to reweight the particle cloud. If the user moves into a region where the floor plan of the building is available to the positioning engine, particles can be pruned whenever they try to cross walls. If desired, even direct user interaction such as manually tapping the map on the smartphone display could be used to provide a position estimate and so constrain the particle cloud.
FIGURE 3 shows six stages from a walk around the corridors of a building using an indoor positioning smartphone app to track the user. The red dashed line shows the trace using just the PDR scheme, which exhibits gradual degradation in positioning accuracy. The green solid line shows the trace using SLAM to constrain the PDR error growth using magnetic anomalies and Wi-Fi signal strengths.
A further modern advance is in computer vision: the use of cameras and algorithms to monitor and interpret features in the environment. The movement of features within the field of view from frame to frame can be used to determine the motion of the camera if it is assumed that the majority of the objects tracked through the view are actually static in the environment. Consistency checks between features allow those corresponding to other moving objects to be filtered out.
The result of this visual odometry scheme is the ability to determine the speed and heading changes of the camera by observing the optical flow of the environment. As with PDR approaches, integrating over visual odometry measurements results in motion tracking with much slower reduction in accuracy over time and distance than for systems built upon traditional IMU integration (accelerometers and gyroscopes) alone. If specific objects or features can be uniquely identified and recognized when seen again in the future, then SLAM techniques can also be applied. At the moment, smartphones are powerful enough to apply computer vision techniques and calculations at moderate update rates of a few frames per second. As smartphones become more powerful, or if mobile operating systems will, in future, permit these computer vision algorithms to be deployed on the dedicated graphical processing units, or even perhaps if devices such as Google Glass result in the deployment of dedicated computer vision chips within devices, we will see computer vision coupled with augmented reality move to the forefront of smartphone navigation.
The Future
Our desire for accurate positioning and tracking anywhere will never go away. The availability of cheap, accurate GPS over the last decade has resulted in accurate positioning, navigation, and timing not only being something we take for granted, but something society has come to depend upon. The positioning capabilities of our smartphones will continue to improve, not only because of the new developments and capabilities described above, but because of new infrastructure developments.
The In-Location Alliance is a large consortium of companies including big names like Nokia and CSR who are defining standards for Bluetooth and other beacon-based positioning technologies for dedicated deployments in indoor environments such as shopping centers, airports, and museums. The new 4G LTE signal structure also contains a dedicated ranging signal to permit traditional timing-based positioning schemes to be easily deployed using these new cellular standards. All infrastructure-based schemes incur costs associated with deployment and maintenance that ultimately limit their scope of deployment; opportunistic schemes are the key to truly ubiquitous positioning.
While billions of dollars are being spent worldwide on deploying and maintaining new GNSS, there will always be scenarios and environments where these weak signals are blocked or severely corrupted. In these cases, opportunistic sensing powered by smart algorithms running on consumer devices costing a few hundred dollars will be there to fill those gaps.
Ramsey Faragher is a senior research associate at the University of Cambridge and an associate editor for the journal of the Royal Institute of Navigation. Previously he was a principal scientist at the BAE Systems Advanced Technology Centre, near Chelmsford in the United Kingdom, where he developed the NAVSOP GNSS-denied positioning system. His research interests include opportunistic positioning, sensor fusion, and machine learning.
Robert Harle is a senior lecturer at the University of Cambridge with research interests in positioning, sensor fusion, and wireless sensor networks. He has worked on indoor positioning since 2000, developing a series of infrastructure-based and infrastructure-free solutions.
FURTHER READING
• Simultaneous Localization and Mapping
“SmartSLAM – An Efficient Smartphone Indoor Positioning System Exploiting Machine Learning and Opportunistic Sensing” by R.M. Faragher and R.K. Harle in Proceedings of ION GNSS+ 2013, the 26th International Technical Meeting of the Satellite Division of The Institute of Navigation, Nashville, Tennessee, September 16–20, 2013 (in press).
“Opportunistic Radio SLAM for Indoor Navigation Using Smartphone Sensors,” by R. Faragher, C. Sarno, and M. Newman in Proceedings of PLANS 2012, Institute of Electrical and Electronics Engineers / Institute of Navigation Position, Location and Navigation Symposium, Myrtle Beach, South Carolina, April 23–26, 2012, pp. 120-128.
“Efficient, Generalized Indoor WiFi GraphSLAM” by J. Huang, D. Millman, M. Quigley, D. Stavens, S. Thrun, and A. Aggarwal in Proceedings of 2011 IEEE International Conference on Robotics and Automation, Shanghai, May 9–13, 2011, pp. 1038–1043, doi: 10.1109/ICRA.2011.5979643.
“WiFi-SLAM Using Gaussian Process Latent Variable Models” by B. Ferris, D. Fox, and N. Lawrence in Proceedings of IJCAI-07, the 20th International Joint Conference on Artificial Intelligence, Hyderabad, India, January 6–12, 2007, R. Sangal, H. Mehta, and R. K. Bagga (Eds.), published by Morgan Kaufmann Publishers Inc., San Francisco, California, pp. 2480–2485.
“Simultaneous Map Building and Localization for an Autonomous Mobile Robot” by J.J. Leonard and H.F. Durrant-Whyte in Proceedings of IROS’91, Institute of Electrical and Electronics Engineers / Robotics Society of Japan International Workshop on Intelligence for Mechanical Systems, Osaka, Japan, November 3–5, 1991, pp. 1442–1447, doi: 10.1109/IROS.1991.174711.
• Integrated Indoor Navigation
“A Survey of Indoor Inertial Positioning Systems for Pedestrians” by R. Harle in IEEE Communications Surveys & Tutorials, Vol. 15, No. 3, 2013, pp. 1281–1293, doi: 10.1109/SURV.2012.121912.00075.
Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems, Second Edition, by P.D. Groves, published by Artech House, Boston, Massachusetts, 2013.
• Wi-Fi Positioning
“Wi-Fi Azimuth and Position Tracking Using Directional Received Signal Strength Measurements” by J. Seitz, T. Vaupel, S. Haimerl, J.G. Boronat, and J. Thielecke in Proceedings of 2012 Workshop on Sensor Data Fusion: Trends, Solutions, Applications, Bonn, September 4–6, 2012, pp. 72–77, doi: 10.1109/SDF.2012.6327911.
“Comparison of WiFi Positioning on Two Mobile Devices” by P.A. Zandbergen in Journal of Location Based Services, Vol. 6, No. 1, 2012, pp. 35–50, doi: 10.1080/17489725.2011.630038.
• Step Length and Pedestrian Navigation
“Step Length Estimation Using Handheld Inertial Sensors” by V. Renaudin, M. Susi, and G. Lachapelle in Sensors, Vol. 12, No. 7, 2012, pp. 8507–8525, doi: 10.3390/s120708507.
• Computer Vision and Navigation
“Improving the Accuracy of EKF-Based Visual-Inertial Odometry” by L. Mingyang and A.I. Mourikis in Proceedings of 2012 IEEE International Conference on Robotics and Automation, Saint Paul, Minnesota, May 14–18, 2012, pp. 828–835, doi: 10.1109/ICRA.2012.6225229.
• Machine Learning
Information Theory, Inference and Learning Algorithms by D.J.C. MacKay, published by Cambridge University Press, Cambridge, U.K., 2003.
