PoLTE Corporation has developed technology that harnesses the global long-term evolution (LTE) deployment to provide accurate and reliable location data.
Unlike localized solutions, such as Wi-Fi and Bluetooth, PoLTE’s technology leverages its Positioning over LTE (PoLTE) Macro software to achieve precision of 2 to 6 meters. The technology makes use of the sounding reference signals (SRS) embedded in an LTE handset user’s transmission. Using adapted radar location techniques, it converts portions of the LTE uplink signal into a probe signal.
The technology enables mobile network operators to deliver highly accurate location data to customers in indoor and outdoor environments.
Traditional macro cell location methods require at least three towers to see the user device to locate the device with precision. Historically, single tower deployments were limited in accuracy to the width of the sector created by the 120-degree antenna that was serving the user device. For example, at a distance of 1.5 kilometers from a base station, the cross range precision would be 4,000 meters. PoLTE Macro can improve the precision to less than 2 meters.
The benefits to leveraging network-based positioning include speed, flexibility, accuracy and data analytics. Customers for the technology include machine-to-machine and Internet of Things technologies, mobile advertising, crowd and customer tracking, and public safety.
INRIX Inc., a connected car services and movement analytics company, has released a redesigned version of INRIX Traffic for iOS and Android.
INRIX Traffic is a next-generation navigation and traffic app that learns user preferences to take the guesswork out of driving. The app integrates with a user’s calendar and learns their driving habits to create a personalized itinerary that includes automatic alerts, anticipated trips, favorite destinations and preferred routes.
Available worldwide now in the Apple App Store and Google Play, INRIX Traffic learns routines and preferences as users go about their day. INRIX Traffic adds favorite places automatically instead of requiring users to spend time inputting destinations such as home, work or school.
Based on learned activities, it creates a daily, driver-specific itinerary of anticipated trips, as well as frequent and preferred routes. By accessing calendar information on a mobile device, the app also adds events with addresses to the daily driving itinerary.
Unlike other driving apps that can provide inaccurate traffic and incidents based purely on consumer input, INRIX Traffic uses a massive crowd-sourced network of more than 275 million connected cars and devices to offer accurate map and real-time information.
INRIX Traffic proactively monitors road conditions to alert drivers of ideal departure times, changes to arrival times and optimal routes to frequent or scheduled destinations based on real-time traffic.
“We designed INRIX Traffic with one specific vision: To help drivers move through their daily lives as quickly and efficiently as possible. The app uses our advanced traffic science to make even routine trips easier,” said Bryan Mistele, president and CEO, INRIX. “Users want an app that is accurate, personalized and smart enough to work proactively for them — so we’ve integrated several highly advanced technologies into one all-encompassing app.”
INRIX Traffic uses the crowd-sourced and free OpenStreetMap (OSM) for map data. By leveraging the power of user-generated content around the world, OSM can quickly adapt to the ever-changing road network. Using OSM enables INRIX to bring a high-quality map and turn-by-turn navigation to users at no cost and without advertisements. In addition to reporting incidents along their route including accidents, police activity and road hazards, INRIX Traffic users can send map feedback directly from the app.
INRIX Traffic is powered by the same technologies the company delivers to its automotive customers such as Audi, BMW, Lexus, Mercedes-Benz and Porsche. These connected car services include real-time and predictive traffic, off-street parking information and drive-time alerts. INRIX will continue integrating features from its product portfolio into future versions of INRIX Traffic.
INRIX Traffic is available in eight languages in 16 countries across North America and Europe, including Canada, France, Germany, Spain, United Kingdom and United States, with additional countries coming soon.
The app is built on Autotelligent, the company’s new software development kit and integrated cloud platform that provides machine learning and route monitoring. Autotelligent can be integrated into products in multiple industries such as automotive, enterprise and mobile.
Omata is introducing a new analog GPS speedometer that displays the information most essential to the activity in a classic form.
Starting with cycling, Omata introduces the Omata One speedometer, designed to complement and maintain the purity of the ride as well as look beautiful on the bike.
On the inside of the speedometer is a GPS computer that records with high precision so that cyclists can at download their activity data to their preferred training applications or sites.
On the outside Omata One has a legible and mechanical analog movement that shows riders the speed, distance, ascent and time. The company’s first product displays only these four, core pieces of information so the cyclist can focus on the ride.
“Everything about your bike should be as pure, inspiring and beautiful as the ride itself,” said the Omata team in a press release. “We are a team of ruthlessly dedicated and committed product makers who believe great design and meaningful products come more from what you leave out, rather than what you add in.”
The Omata One speedometer will launch on Kickstarter on April 5 with estimated delivery of the first product in February 2017. Omata One will subsequently be available through omata.com.
Maptiks‘ web map analytics is now integrated with Esri’s ArcGIS platform and available on ArcGIS Marketplace. It is now possible to receive analytics surrounding use of a web-mapping application.
ArcGIS Marketplace is a destination that enables ArcGIS users to search, discover, and get apps and content from qualified providers.
Using Esri’s ArcGIS API for JavaScript and Esri Leaflet, Maptiks now provides user activity analytics for your web maps. It helps answer key design and U/X questions such as how and where are users interacting with a web map.
“Many web map developers are lacking the right metrics and data to see how users are actually converting and using their maps,” reads a Maptiks press release. “Google Analytics provides some insights, but it lacks the capability to really dig into where users are looking on the map, where they are clicking and how they’re actually using it.”
Standard websites, apps and web applications have a slew of developer friendly analytics solutions that help businesses dive into their users’ activities. But, developers building out applications based on web maps such as Uber, Nextdoor and Strava that have GIS dependent products require more detailed metrics into their users.
Will Cadell, CEO of Maptiks, spoke about the integration. “We’re really excited to be integrating with Esri’s platform. The development community around Esri is massive and we’re looking forward to giving them an analytics tool they’ll actually use. We’ve had a lot of requests from web map developers who wanted this integration, and thanks to the Esri Startup Program, we were able to fast track the integration with our development team. The Esri team has been very supportive. We’re looking forward to help Esri development community build even better Esri maps with the help of our analytics.”
Maptiks, is a startup based out of Prince George, B.C., founded by Will Cadell. Maptiks is built by Sparkgeo, a geospatial web company with over a decade of experience in the GIS & Technology industries. Working with clients such as Wildlife Conservation Society, and Map My Fitness, Sparkgeo has built Maptiks to measure how users interact with web maps increasing map conversions so they can build a better map.
Aerial refueling requires highly precise relative navigation. (ILLUSTRATION: Charles Park)
Future UAVs will require relative navigation capability to fulfill a broad range of assisted manned and unmanned missions. A new approach, demonstrated in application to aerial refueling, provides access to accurate relative time-space positioning information (R-TSPI) between platforms.
By Shahram Moafipoor, Jeffrey A. Fayman, Lydia Bock and David Honcik
The advent of unmanned aerial vehicles (UAVs) highlights the importance of precise relative navigation information for safe use of UAVs in many application areas. Future military and civilian UAV applications will increasingly require capabilities such as
sense and avoid
swarming
vehicle-to-vehicle (V2V) platooning
docking
autonomous landing and
autonomous aerial-refueling,
all of which require access to accurate relative time-space positioning information (R-TSPI) between platforms.
In this article, we present the foundation for a generic approach to relative navigation capable of meeting the full range of relative assisted manned and unmanned operations. We present a relative extended Kalman filter (R-EKF) that integrates line-of-sight relative observations from GPS as well as non GPS-based onboard sensors measuring relative bearing and/or relative distance. Multi-sensor fusion provides enhanced system integrity and robustness to partial or total lack of GPS-satellite navigation (GPS-denied). The relative navigation system described here uses these technologies, providing up to 100 Hz R-TSPI with an accuracy of up to ±1.0 m (a function of relative distance), ±0.1 m/s velocity and ±0.5º attitude. The system can be applied to a variety of relative navigation applications; here we focus on its use in aerial refueling.
132d Air Refueling Squadron. A Boeing KC-135R Stratotanker refuels an F-22A Raptor. (Photo: USAF)
AERIAL REFUEL CHALLENGES
Automated aerial refueling for manned and unmanned platforms is a challenging problem requiring accurate R-TSPI. The Geo-RelNAV system provides a key measurement for aerial refueling: the vector closure rate, the differential velocity between the tanker and refueling aircraft. The closure rate is monitored in real time onboard the tanker. The measurement can be used to:
maintain safety-of-flight by ensuring refueling aircraft do not exceed a certain velocity,
determine whether or not a refueling aircraft is approaching the tanker with sufficient velocity, and
provide data to drogue-control engineers to improve control law design.
As a GPS/INS system, Geo-RelNAV can produce a relative navigation solution at a faster sample rate than GPS alone. Solutions are available via serial and/or Ethernet (both TCP and UDP) providing input to external systems as well as the tools for analysis engineers to monitor the data in real time using standard monitoring and recording tools. The system provides R-TSPI in different frames, including the body frame of the platforms, local navigation frame (wander-azimuth) and Earth-fixed frame, as well as transferring the solution to arbitrary points of interest on the aircraft such as the refueling aircraft’s refueling probe.
RELATIVE INERTIAL NAVIGATION
We use the terms primary and secondary in this article to identify the platforms for which R-TSPI data is being generated. R-TSPI is always provided for the primary with respect to the secondary. Referring to Figure 1, the tanker is considered the primary and the refueling aircraft, the secondary (or vice versa, depending on the location of the control segment). Data is always transmitted through the data link from the secondary to the primary. Figure 1 summarizes the geometric relations, where the primary body frame is labeled p-frame and the secondary body frame is labeled s-frame. The body frame fixed to the primary (P) is shown by (xPp,yPp,zPp), and body frame fixed to the secondary (S) is shown by (xSs,ySs,zSs).
Fgure 1. Primary/secondary geometry and corresponding body frames fixed to the vehicle body.
The relative navigation equation is set up for the state of the secondary with respect to the state of the primary in the center of the body frame of the primary, p-frame:
(1)
where xPp is the primary position vector established in the p-frame, and xSp is the secondary position vector defined in the p-frame. Note that these vectors can also be obtained from the primary/secondary strapdown inertial navigation solutions after transferring to the reference (eccentric) point. Equation (1) represents the fundamental equation, from which the relative navigation equations are derived. Once the relative kinematic model of the position and velocity are established, the next step is to develop the relative attitude kinematic model. The relative attitude, denoted by the quaternion qpS, is used to map vectors in the s-frame to vectors in the p-frame:
(2)
where qp and qs are the quaternion attitudes of the primary and secondary with respect to the i-frame, qp* is the conjugate of qp, and is the quaternion multiplication operator.
Hardware for the relative navigation system.
RELATIVE EXTENDED KALMAN FILTER
To establish the R-EKF, we must derive the relative inertial error equations. The R-EKF has 21 basic states including nine for relative position, δΔxpPS , relative velocity, δΔvpPS , and relative attitude, Ψpps, and 12 to model the primary’s gyro and accelerometer bias (non-constant) and non-linear scale factors. Since the relative distance between the secondary and primary is small compared to the radius of the Earth, the gravity terms are negligible. Thus, in the linearized terms, the relative gravitational terms are ignored. It should be noted that the secondary states are assumed to be known for retrieving the absolute primary TSPI information. Since Equations (1) and (2) can only provide the general dynamic model for a nonlinear state model, all these equations must be linearized using Taylor series about nominal values (neglecting the higher-order terms). After perturbation state equations are established, they should be discretized from a continuous-time to a discrete-time sequence. The final solution to the state equation can be expressed as:
(3)
with:
(4)
FPpS is the Jacobian matrix, and the perturbation elements are all related to the primary:
(5)
RELATIVE GPS MEASUREMENT MODEL
When GPS is available, high-accuracy relative positions are derived from the use of carrier-phase differential GPS, a technique commonly used in static positioning applications such as surveying. However, unlike those applications, in this case the reference receiver is not stationary; it is located on a moving platform (secondary) creating a moving baseline. The relative GPS measurement in our system is provided by epoch-by-epoch (EBE) differential carrier-phase processing, which measures accurate relative position between the secondary and primary systems. The EBE relative position has a typical accuracy better than 3 cm (1-sigma horizontal) and 6 cm (1-sigma vertical). Testing of the relative measurement was conducted using two ground vehicles configured with 10-Hz dual-frequency GPS sensors. The mean difference was less than 5 cm. As a conclusion, the GPS relative mode was shown to provide accurate relative positions between the platforms. Once the relative position is measured, the R-EKF observation model can be established as:
(6)
The (ΔxpPS )GPS term is the relative position measured by using GPS data, and the term (ΔxpPS)INS is the relative position, which is predicted by using the last updated inertial solutions. Note that in order to use this relative observation, the lever-arm vector between the GPS and IMU of both the primary and the secondary must be accurately measured and applied (see Figure 2).
Figure 2. Relative observation model.
Here, the observation model is represented on the condition that the vector of observations has yielded certain values based on an assumed linear relationship to:
(7)
Equations (3) and (7) are the fundamental equations of the R-EKF.
SYSTEM ARCHITECTURE
Relative navigation is computed and provided at one of the units, designated the primary unit. This requires data from the secondary unit to be transferred to the primary unit over a data link. The primary unit uses this transmitted data to calculate its position, velocity and attitude relative to the secondary unit. Figure 3 summarizes the architecture and data-flow. Mathematically, the data from the secondary unit used in the relative calculations are assumed to be errorless.
Figure 3. Geo-RelNAV architecture.
OPERATIONAL ENVIRONMENT
We distinguish the following three relative navigation stages, illustrated in Figure 4, where each phase utilizes a unique processing mode.
Fgure 4. Relative navigation phases.
In the Approach phase, the data link between primary and secondary units is not closed. An autonomous navigation solution for both the primary and secondary units is computed on each platform independently. This information will be later used when the system transitions to the Engagement phase to initialize the R-EKF.
In the Engagement phase, the data link between primary and secondary units is closed, and the R-TSPI solution is computed between the platforms. Sensor observations are transmitted across the data link from the secondary unit to the primary unit. The primary unit implements the R‑EKF to produce the R-TSPI solution.
In the Departure phase, the activity requiring R-TSPI (that is, refueling) is complete, and the secondary platform pulls away from the primary platform. In this phase, we transition from the R-EKF back to the autonomous independent navigation system.
The Approach phase is as important as the Engagement phase in attenuating the initialization error in terms of position, velocity and attitude. To initialize the R-EKF, the autonomous TSPI solution from the secondary unit is transferred to the primary unit, where the initial relative position, velocity and attitude are estimated.
There are three conditions under which this initialization must occur:
upon transition from the Approach phase to the Engagement phase,
when in the Engagement phase and the system experiences a data link dropout, and
when there is a large latency in the data link. If the data link latency is too large, the data arriving at the primary can no longer be used.
VALIDATION TESTING
Several system tests were conducted including static bench testing, dynamic ground vehicle testing and flight testing. We discuss the results for the static and bench testing here.
For static bench testing, the system was set up on two points with a measured fixed displacement. The sensor configuration included dual-frequency GPS receivers, ring laser gyro-based IMUs, and a data link operating in the 900-MHz frequency band.
The results show that relative position held to the fixed offset with a standard deviation of less than 0.1 m in North, East and Up. Relative velocity held to zero with a standard deviation less than 0.01 m/s, and relative attitude was also maintained with the accuracy up to the gyro bias stability of the ring laser gyro IMU (1°/hr for a stationary platform).
The overall performance of the system in static bench test confirms the stability of the hardware and software of the system, when it is not exposed to any dynamics, and the sensors are in close proximity (no data link latency or data dropouts).
Dynamic Drive Test. In a more realistic test to simulate the operational phases described in Figure 4, the drive test followed a scripted path. As shown in Figure 5, the two platforms left Geodetics’ facility and drove separately (simulated Approach) until they met each other at the Fiesta Island test site, where the data link was closed for the Engagement phase. The primary and secondary navigation systems operated independently during the Approach phase.
Figure 5. Drive test ground trajectory of the primary (blue) and secondary (red).
Once the data link was closed at the test site, the R-EKF engaged, using initialization information transmitted from the secondary to the primary platform. To provide a “truth source” for evaluating the performance of the relative navigation solution, both autonomous GPS/IMU systems were fed data from an external reference receiver. Table 1 shows the statistical data analysis in the form of mean and standard deviation for the collected data.
Average RMS of fit in the relative position, velocity and attitude of approximately 1.0 m, 0.1 m/s and 0.3º, respectively, were computed for the entire relative navigation period. In this dynamic test, we encountered frequent data link dropouts, data link latency, as well as GPS outages, causing discontinuity in the R-EKF measurement updates until GPS was reacquired. During these periods, the R-EKF prediction model, updated with the last calibrated IMU data, provided the R-TSPI. This test help confirm that system performance is at the expected levels, even in the presence of real-world data link and GPS problems.
Table 1. Statistical analysis of the R-TSPI solution.
GPS-DENIED OPERATIONS
Over-reliance on GPS has exposed vulnerabilities associated with this technology. For example, GPS is easily jammed and spoofed. While spoofing can be addressed with Selective Availability Anti-Spoofing (SAASM) technology, and advances such as M-code will mitigate other vulnerabilities, systems of the future must be robust to partial or total lack of GPS. Advanced sensor-fusion technologies are necessary to provide capabilities in conjunction with, and in the absence of, GPS.
In the context of aerial refueling, sensors such as active and passive vision systems can be used as complimentary observations by the system, providing a GPS-free relative distance observation in situations where GPS is blocked due to airframe masking, jamming, and so on.
Data from both active (lidar) and passive (camera) vision sensors were added to the system, providing significant advantages in the process flow. The use of vision sensors provides the relative distance observation in GPS-denied conditions for continuity in R-EKF updating. In addition, vision-based relative distance allows for the detection of outliers by evaluating the redundancy contribution of the measured GPS-based relative distance, and enables the transfer of the R-TSPI solution from the secondary refueling center to the on-the-fly probe-drogue system, as shown in Figure 6.
Figure 6. Vision sensor aiding increasing the integrity
For the active vision system, we leveraged a fully integrated lidar mapping payload as shown in Figure 7 (left). For the passive sensor, we utilize a stereo camera. Figure 7 (right) shows the test area and the simulated drogue. Imagery observations from the passive camera and the lidar system were processed with independent algorithms appropriate to each data type and the relative distance between each of the two sensors, and the simulated drogue was measured with an RMS error of less than 10 cm.
Figure 7. Geo-MMS (left) and its application (right) for measuring relative distance.
INTEGRITY
While outside the scope of this article, in addition to supplying a GPS-free relative distance observation, the use of vision sensors was applied to the task of increasing system integrity. This includes, in general, the capability to indicate when the system should not be used for the intended operation. We focused on two aspects: outlier detection (inner reliability), and the effect of undetected outliers (outer reliability).
To properly address the reliability and integrity requirements, a quality testing mechanism was designed to assess the estimated/predicted relative distance observations before passing them in to the R-EKF module.
CONCLUSIONS
An autonomous relative navigation, in its application for the aerial refueling problem, places special attention on system architecture so that it can handle most possible real-world scenarios, including frequent data link dropouts, data link latency and GPS outages. The core of the system is a relative extended Kalman filter, which uses GPS and IMU measurements of the primary and secondary platforms to estimate the relative inertial navigation states. The system is able to provide relative TSPI at the IMU sample rate with an accuracy of ±1.0 m position, 0.1 m/s velocity and ±0.5º attitude.
An added benefit of the system architecture is the ability to add observation models that do not rely on GPS. Thus, redundancy can be introduced using sensors such as vision systems.
SHAHRAM MOAFIPOOR is a senior navigation scientist at Geodetics, focusing on new sensor technologies, sensor-fusion architectures, application software, embedded firmware and sensor interoperability in GPS and GPS-denied environments. He holds a Ph.D. in geodetic science from The Ohio State University.
JEFFREY A. FAYMAN serves as Geodetics’ CTO. He holds a Ph.D. in computer science from the Technion Israel Institute of Technology and has published more than 40 papers in robotics, computer vision, computer graphics and navigation systems.
LYDIA BOCK serves as Geodetics’ president and CEO. She has more than 35 years of industry experience spanning a variety of high-tech industries including electronics, semiconductors and telecommunications. She has a Ph.D. from the Massachusetts Institute of Technology.
DAVID HONCIK, Geodetics’ director of engineering, has more than 30 years of experience in software/hardware integration and structured software design for real-time embedded systems, Windows programs, graphics, telecommunications, aerospace, flight simulation and airborne instrumentation.
The integrated lidar mapping payload referenced is Geodetics’ Geo-MMS system.
Leica Geosystems released its new generation of reference servers and monitoring receiver, optimized with multi-frequency 555-channel capabilities to connect with current and all anticipated GNSS signals.
The Leica Geosystems GM30 receiver.
The new Leica GR30 and GR50 reference servers and GM30 monitoring receiver are primed for the constantly changing requirements of GNSS technology, according to the company. The first equipped with 555 channels, the new reference stations and monitoring receivers support all global GNSS constellations, such as GPS, GLONASS, Galileo and BeiDou, as well as regional systems such as QZSS and SBAS.
The receivers seamlessly work with a multitude of signals so that monitoring professionals, geodetic research and engineering specialists can obtain high-quality data and continuous, uninterrupted accuracy.
These new reference servers and monitoring receiver are part of Leica Geosystems’ GNSS streamlined solution — with standard open interfaces, they can be seamlessly integrated with other existing systems. According to Leica, maximum benefit with minimum effort is achieved through automated firmware updates, plug-and-play connectivity, simultaneous and multiple communications interfaces, power supply and logging capabilities.
“When considering a GNSS reference station solution, superior quality and long product life is very important,” said Frank Pache, senior product manager of Leica Geosystems. “Our new reference servers fulfil this demand. In addition, users who have already invested in the previous generation Leica GR10 and GR25 Unlimited solutions can now benefit from the free upgrade to the new Leica GR30 and GR50 reference servers. They can enjoy the peace of mind that comes with being equipped with today’s and tomorrow’s GNSS signals.”
Thanks to the comprehensive and user-friendly web interface, both GNSS network beginners and highly experienced professionals have complete and easy control. Leica Active Assist’s support team make sure your GNSS projects run smoothly by providing live and secure onboard assistance whenever needed.
Scientists, researchers and engineers are also provided with detailed information about movements of man-made and natural structures as well as real-time position solutions. Three different modes, specifically designed to work with reference station, structural and network real-time kinematic (RTK) service monitoring continuously provide specialists with real-time precision data.
The Leica GM30 monitoring receiver is also part of the GeoMoS solution, delivering timely and actionable information to respond quickly and minimize dangerous and costly damages.
The reference servers and receiver are also part of the Leica Spider family, a suite of software providing RTK services for tailor-made solutions.
The U.S. Geological Survey (USGS) released six new sets of publicly available maps that show the diverse and complex range of seafloor habitats along 80 miles of the central California coast from the Monterey Peninsula north to Pigeon Point, according to a news release form the organization.
The new USGS publications, products of the California Seafloor Mapping Program, combine new and legacy data to reveal offshore bathymetry, habitats, geology and seafloor environments in high resolution. Environments range from the rugged granitic bedrock along the coasts of the Monterey Peninsula, to the bedrock reefs that form the surfing point breaks on the Santa Cruz County coast, to the smooth sand and mud in a large delta bar at the mouth of the Salinas River, and to the steep walls and sinuous channels of one of the largest underwater canyon systems in the world.
“The new high-resolution datasets and maps are stimulating research – scientists are excited,” said Sam Johnson, the USGS project lead. “Our stakeholders like to say that you can’t manage it, monitor it or model it if you don’t know what the ‘it’ is. Our seafloor mapping provides that important ‘it’ to the entire coastal community.”
Seamless onshore-offshore geologic maps incorporating subsurface data document the location and geometry of the San Gregorio fault and show how different strands of the fault extend through Carmel Canyon — across the continental shelf west of Santa Cruz and Davenport — and combine to uplift Año Nuevo State Park and Año Nuevo Island. A separate fault system to the east in Monterey Bay is part of an actively deforming wedge of the Earth’s crust caught between the converging San Andreas and San Gregorio faults, the organization said. The six new sets of California maps are Offshore of Pigeon Point, Offshore of Scott Creek, Offshore of Santa Cruz, Offshore of Aptos, Offshore of Monterey Canyon and Vicinity and Offshore of Monterey.
Each publication includes 10 map sheets, a pamphlet and a digital data catalog with web services. The web services are a new addition to the publications and all previous products in the map series, and can be viewed on smartphones. The USGS said the maps and data provide:
A foundation for assessing marine protected areas and habitats.
An understanding how marine species such as bull kelp, rockfish, crabs and sea otters use the seafloor.
Baselines for monitoring coastal change and sea-level-rise impacts.
Critical input data for modeling and mitigation of coastal flooding.
A framework for understanding coastal erosion and developing regional sediment management plans.
Contributions to earthquake and tsunami hazard assessments.
More accurate data for safer navigation.
Essential information for planning, siting or removing offshore infrastructure.
“These new seafloor maps – used in partnership with the USGS – will give us an additional tool to protect Californians, as well as fish and wildlife,” said John Laird, California’s secretary for natural resources and OPC chair. “The new maps will be used to analyze offshore faults and earthquake hazards. They will also help us identify sources of sand to replenish beaches – and will help establish a scientific baseline to track changes in habitat near shore over time. This investment will pay off for Californians in ways that we cannot even imagine now.”
Hexagon AB plans to acquire the GeoRadar division of the Italian-based company Ingegneria dei Sistemi S.p.A, a privately owned company with core expertise in radar-based solutions for multiple industries.
Located near Pisa, Italy, with approximately 60 employees, the IDS GeoRadar division provides the mining and geospatial industries with innovative radar solutions for structural health monitoring and underground utility mapping.
GeoRadar’s structural health monitoring solutions enable engineers to remotely monitor — in real time — movements and vibrations of the earth such as mine walls, landslides, and glaciers and a wide variety of infrastructures such as bridges, buildings and dams. Its underground utility detection solutions provide engineers with dimensional information such as size and location of buried pipes and/or the health condition of roads and rail tracks through the detection of underground cracks and cavities.
“GeoRadar’s solutions nicely complement our reality capture solutions, enriching Hexagon’s portfolio across a wide variety of segments like surveying, construction and mining,” said Hexagon President and CEO Ola Rollén. “Additionally, combining GeoRadar’s technologies with our mobile reality capture portfolio broadens our solution offering for large-scale asset management across segments like utilities, road and rail.”
The transaction remains subject to customary closing conditions. Closing is expected during the second quarter of 2016. IDS GeoRadar turnover for 2015 amounted to approximately 18 MEUR.
South Korea issued a warning Thursday after detecting satellite signal disruptions that appeared to be coming from North Korea, according to the Korea Herald. The capital city of Seoul appeared to be the target.
Officials said North Korea discharged a large amount of radio waves to jam GPS signals in the region.
“We’ve detected signs that North Korea has been sending radio waves to the capital area since a month ago to disrupt GPS signals,” a senior government official said, speaking on condition of anonymity. “North Korea had been sending test waves since last month, but today, they discharged the largest amount.”
The warning was issued at 7:30 p.m. in Seoul, the adjacent city of Incheon and the surrounding Gyeonggi and Gangwon provinces.
The disruptions could cause mobile phones to malfunction and affect planes and ships that rely on GPS for navigation. No damage has so far been reported in the military or among civilians, officials said.
Since 2010, GPS disruptions have occurred three times in South Korea, and all have been blamed on the North.
Numerous factors will impact the economics and logistics of how farmers and growers will use drones in 2016 and beyond, according to a new report offered by the Commercial UAV Expo.
In “Above the Field with UAVs in Precision Agriculture,” author Jeremiah Karpowicz examines factors such as:
Potential impact of new FAA regulations
Capabilities created or augmented with new sensor technology
The best approach to get in the air.
Download this free report, UAVs in Precision Agriculture and discover how UAVs are set to revolutionize this multi-billion market.
Farmers and growers are starting to use UAVs to increase both productivity and profitability with real-time data, to improve decision making in areas such as for crop scouting, nutrient management, field mapping and water drainage.
KVH Industries has begun shipping the first order of tactical navigation systems to BAE Systems for a prototype program designed to produce a new fleet of U.S. Army Armored Multi-Purpose Vehicles (AMPVs).
KVH’s TACNAV systems are designed to provide the vehicles with such critical elements as continuous heading and pointing data output and extremely accurate navigation regardless of GPS availability.
Deliveries of the tactical navigation systems are part of a recent purchase order that covers the life of the program, which is expected to run through 2020. The initial order of 34 TACNAV II systems is supporting prototype vehicles, and there is potential for an option for additional systems to support the low-rate initial production (LRIP) of the vehicles.
According to BAE Systems, the $1.2 billion AMPV program is designed to replace the U.S. Army’s Vietnam-era M113s and provide a significant upgrade that increases the service’s survivability, force protection, and mobility while providing for future growth potential.
M113 Armored personnel carrier n Vietnam, 1966. (Photo: U.S. Army)
“KVH is pleased to have been selected by BAE Systems for this important U.S. Army armored vehicle program,” says Dan Conway, executive vice president of KVH’s guidance and stabilization group. “KVH’s tactical navigation solution serves as a crucial resource for navigation and battle management, keeping soldiers safe and out of harm’s way wherever they travel.”
KVH TACNAV is a proven solution that has been serving soldiers for years in numerous armored vehicle programs, with more than 19,000 units fielded worldwide.
KVH’s TACNAV military vehicle navigation systems provide unjammable precision navigation, heading, and pointing data for vehicle drivers, crews, and commanders. KVH’s proprietary fiber optic gyro (FOG) technology is a differentiating factor in enabling the TACNAV systems to provide extremely accurate heading and pointing data, which is crucial for situational awareness.
The Armored Multi-Purpose Vehicle (AMPV) is the U.S. Army’s program to replace the Vietnam-era M113 Family of Vehicles. (Photo: BAE Systems)
The systems feature a compact design and flexible architecture ideal for today’s digital military. In addition, TACNAV is designed to integrate easily with Battle Management Systems (BMS), providing a vital component for effective battlefield management.
TACNAV systems are in use by the U.S. Army and Marine Corps, as well as many allied customers including Canada, Sweden, Great Britain, France, Germany, Spain, Egypt, Botswana, Australia, New Zealand, Saudi Arabia, Taiwan, Romania, Poland, Turkey, Malaysia, Switzerland, South Korea, Singapore, Brazil and Italy.
Australian Chief of the Defence Force Mark Binskin said that combat drones could take the place of some Joint Strike Fighters (JSFs).
A defense white paper states that Australia will buy 72 Joint Strike Fighters to replace current fighter planes “Classic” Hornets, six of which are now flying bombing raids over Iraq and Syria. But it leaves open the possibility of not buying a final squadron of roughly 25 JSFs to make up the 100-strong air combat fleet Australia needs.
Instead, the white paper states that to replace the newer, current squadron of Super Hornet aircraft from about 2030, alternatives will be “considered.”
Binskin said the department was keeping an open mind given the rapid improvements in armed drones or unmanned combat aerial vehicles, also known as UCAVs.
