RoGO Communications, the creator of the DropBlock satellite communications platform for cellular-denied environments, is partnering with Augmented Sense Technologies (AugSense) to integrate artificial intelligence capabilities into RoGO’s communications infrastructure.
RoGO was founded to develop lifesaving technology for wildland firefighters and first responders. It’s product DropBlock is a ruggedized, portable satellite communications platform that provides real-time GPS tracking, weather telemetry, IoT sensor data, and tactical messaging in cellular-denied and remote environments.
The partnership will develop edge AI-powered sensor fusion, Team Awareness Kit (TAK) ecosystem development, and predictive analytics to firefighters, disaster recovery, military and other first responders and remote operators, including All Hazards emergencies such as hurricanes, earthquakes and floods. Last month, RoGO and AugSense presented the combined capabilities at the annual convention for Special Operation Forces (SOF Week) in Tampa.
Wildland firefighters, search-and-rescue teams, and military personnel routinely operate in remote terrain where conventional communications infrastructure does not exist. RoGO’s DropBlock technology has proven its ability to deliver real-time GPS tracking, weather data, IoT sensor telemetry, and tactical messaging over satellite links in these environments—deployed today by wildland fire agencies. As missions grow more complex and sensor-rich, operators increasingly need more than raw data. AI can deliver intelligence at the edge, delivered in real time, without dependence on connectivity.
Through this partnership, RoGO will enhance its platform with AugSense’s edge AI engine, a modular, platform-agnostic system that processes and fuses multi-modal sensor data directly on devices, without requiring a cloud connection. The AI-enriched intelligence products will transform raw sensor feeds into actionable decisions, such as predictions for the spread of a wildfire or other threats to safety.
Edge AI Capabilities
Edge AI Processing: AugSense’s engine runs AI workloads directly on edge devices using neuromorphic and spiking neural network architectures, achieving greater energy efficiency than conventional approaches. This means intelligence processing in power-constrained environments — no cloud, no data center, no latency.
Multi-Modal Sensor Fusion: AugSense’s fusion engine synthesizes data from diverse sensors (RF, weather, geospatial, physiological, and chemical/biological) into a single actionable intelligence picture at the edge.
TAK Integration & Development: Purpose-built plugins for the Android Team Awareness Kit (ATAK) and broader TAK ecosystem that overlay AI-fused intelligence onto the common operating picture, enhancing coordination across distributed teams connected through RoGO’s DropBlock network.
Predictive Analytics: Machine learning models that transform raw sensor telemetry into forward-looking predictions such as anticipating weather shifts, equipment failures, threat patterns, and fire behavior.
Immediate Applications
The combined solution targets several high-impact use cases.
In wildland firefighting, the integration enables AI-predicted wind shifts and fire behavior models to reach incident commanders via RoGO’s satellite network—critical for crew safety decisions.
For search-and-rescue operations, fused sensor data and intelligent mapping overlays allow distributed teams to coordinate effectively through the DropBlock network without relying on cellular infrastructure.
In defense and special operations, the partnership delivers fused multi-sensor intelligence and TAK-integrated common operating pictures over satellite backhaul in contested and communications-degraded environments.
A new RoGO mobile phone app coming in the third quarter enables point-to-point communications among DropBlocks and firefighter crews and displays the location of firefighting assets along with fire weather data.
By Alex Damato, Acting Executive Director, GPS Innovation Alliance
Alex Damato
It can be easy to take GPS for granted as the average driver and smartphone user continues to enjoy convenience, entertainment and navigation from this technology, enhancing nearly every part of our daily lives. While we may not enjoy its benefits every day, one important use case keeps us and our environment safer: GPS has become a vital part of modern emergency response.
Many Americans across the nation are preparing for the impending hurricane season or the threat of other natural disasters, such as wildfires and earthquakes. GPS will play a critical role in recovery and response efforts. When natural disasters occur, accurate and actionable location information helps save lives and restore critical infrastructure as quickly as possible.
GPS has fundamentally improved access to information that can help the public prepare for these natural disasters, rather than waiting for them to strike. This information is more critical than ever. For example, California’s Oak fire spread to almost 20,000 acres and is part of a larger trend in California that has destroyed 14,700 buildings and killed 36 people over the past two years. Farther north, 530 wildfires in Alaska burned areas larger than the state of Connecticut in the state’s worst fire season in recent history.
In addition to helping the public face natural disasters, GPS helps firefighters plan their operations more efficiently and enables them to receive real-time information on the location of the wildfires they are fighting. With real-time mapping, planning and operations, fire chiefs can respond immediately to areas where wildfires are dangerously advancing.
In turn, GPS protects our first responders by preventing firefighters from getting caught in unpredictable fires they would have otherwise not known were heading their way.
Firefighters use IGNIS drones to help prevent wildfires from starting or safely contain them with backburns. IGNIS relies on GPS for tracking, safety and control, which in turn helps firefighters avoid the dangers associated with being near prescribed burns. Without GPS, resources to help firefighters would not be deployed as efficiently — wildfires could spread even more quickly as a result, causing even more damage to our homes and infrastructure.
Beyond wildfires, GPS technology is critical to emergency response and weather safety. GPS data allow emergency responders to better locate callers and reduce the incidence of misrouting to outside jurisdictions. Using GPS data, a caller can be located in close proximity to his or her actual location. By reducing rates of misrouting and accurately pinpointing emergency locations, GPS helps reduce response time by taking away the need to reroute calls and search for callers’ locations.
In a recent experiment, NASA-commissioned researchers used GPS signals to better predict a hurricane’s maximum wind speed, which could help federal agencies and forecasters better predict the danger of hurricanes and provide more actionable information to determine whether to issue evacuation orders.
The GPS Innovation Alliance (GPSIA) is proud to support the role of GPS as a critical enabling technology for public safety, disaster response and relief efforts. With GPS, precise real-time location information is at the fingertips of both consumers and first responders from pre-disaster planning efforts to post-disaster recovery. While GPS has already fundamentally improved modern emergency response systems, GPSIA will continue to advocate for the continued growth of these lifesaving GPS-enabled technologies and applications through rigorously developed technical rules, interference protections, and a predictable spectrum environment.
Many of us have grown accustomed to the ease of GPS-enabled technologies, from smartphone to fitness trackers. At GPSIA, we’re also particularly proud of the role GPS plays in the many other life-saving ways the technology is being used and are committed to continuing this critical work.
Photo: Michael Chapman/iStock Editorial / Getty Images Plus/Getty Images
Aerial perspective and mobile operation management system combine to inform deployment, give increased situational awareness, and save critical time
A new strategic partnership between DJI and Rosenbauer International AG will enable emergency scenarios to be dealt with quicker, more safely and more efficiently, according to the companies.
DJI makes civilian drones and aerial imaging technology, and Rosenbauer manufactures fire service vehicles and firefighting equipment.
The two companies will work together to bring the benefits of digital emergency response management to anyone involved in being called to tackle an emergency situation.
Whether used by a private company at an airport or industrial facility, or a local fire department called out to an emergency even in severe weather, an aerial perspective combined with Rosenbauer’s operation management system enables the situation to be quickly assessed and informed decisions made regarding the safest and most efficient deployment of personnel.
Rosenbauer’s operation management system is the information management system for firefighting operations that supports emergency crews on site with relevant information such as fire safety maps, hazardous material data or vehicle rescue sheets.
Data from DJI’s drone fleet management software, FlightHub, will be integrated into Rosenbauer’s operation management system, giving additional visual and thermal data to the decision maker of the operation.
The information can then be relayed to operational units at the scene on a tablet or displayed on monitors back at the command center giving a full overview of the situation. Informed decisions can be made in real time regarding the efficient and safe deployment of resources such as personnel, vehicles and other equipment.
Data safety is paramount and all information that is delivered to the Rosenbauer operation management system in real-time, and from DJI’s FlightHub, is stored on a server in the highly secure computer center of a well-known European telecommunication firm. The data traffic is secured and encrypted. During deployment, the data is also synced with all mobile end devices so that every operational team has the same information, and it is kept in a closed loop.
“Speed and a truly complete overall picture are key criteria for success when emergency service teams have to make purposeful decisions under time pressure. We have already supported their efforts to meet these criteria with our IT solutions, which range from efficient vehicle management to navigation, right through to alarm applications,” said Dieter Siegel, CEO of Rosenbauer International. “This cooperation with DJI enables us to consolidate our role as a digital pioneer while we work together to develop an integrated technology for comprehensive, data-based firefighting and disaster management.”
“DJI is proud to bring its drone technology to support Rosenbauer’s excellence in fire apparatus manufacturing and its vision of empowering firefighters with the best possible tools for emergency response and disaster relief. At DJI, we aim to provide reliable, scalable drone offerings that empower firefighters, search and rescue and public safety teams to benefit from this technology,” said Roger Luo, President, DJI. ” It plays an increasing role in saving lives, time and resources on a daily basis. This integration is an important step for this long-term partnership, and our commitment demonstrates an increasing maturity in the adoption of drones for firefighting professionals.”
An Esri Storymap provides a quick snapshot of the raging fires across the United States and provides context to the severity of the California fires.
The interactive map can be explored by panning and zooming. Click on a fire and information about that particular fire is displayed including the start date, containment and links to the latest news and social information.
Esri Story Maps let users combine authoritative maps with text, images and multimedia content. It harnesses the power of maps and geography to tell a story in an easy and understandable format, the company said.
The Story Map uses the ArcGIS Javascript API and is linked to interactive timelines and magnitude displays. The cartography uses AGOL Firefly symbology — radial gradients — and a dark basemap.
The fires and perimeters are a service of the GeoMAC community that uses the Geospatial Multi-Agency Coordination, an internet-based mapping application that is designed for fire managers to access online maps of current fire locations and perimeters in the United States.
Members of GeoMAC include:
U.S. Geological Survey
National Interagency Fire Center
National Weather Service
Bureau of Land Management
Remote Sensing Application Center
National Geophysical Data Center.
The data is updated manually based on information from a host of sources including those on the ground. Typically, the data is fresh to about 24 hours, but there is variability because it is a carefully curated process.
Diving deeper for information
Esri has updated the app based on feedback from many different groups including firefighting professionals, those directly affected by fires, and those concerned about loved ones affected by fires. Some of the updates include the addition of the National Weather Service (NWS) animated smoke risk forecast, visualized to more directly represent smoke (see below).
The NWS animated smoke risk forecast is now integrated into Esri’s Story Map app. (Screenshot: Esri)
Another is the addition at finer scales of satellite-detected hot spots to indicate fire direction — sensors. Many Earth-observing satellites contain sensors capable of detecting the infrared energy released by fires. Not only can the hotspots be located, but areas of burned land can also be identified based both on their thermal characteristics and visible appearance. In Esri’s ArcGIS Living Atlas of the World, the MODIS Thermal Activity layer provides daily updated global hotspot locations.
In the U.S., the USA Wildfire Activity layer in the Living Atlas provides a more quality controlled version of the data. It shows only wildfires submitted to the USGS by fire agencies, as opposed to all of the other events that can cause an automated satellite-based hotspot detection. However, since this layer relies on human analysis, sometimes it doesn’t update as frequently as the MODIS hotspots. The layer also contains the perimeter of the fire area. Both current (active) and older (inactive) fires are included.
While the weather-focused satellites from NOAA and NASA provide high temporal resolution fire data, really detailed analysis of the fire impact is often left to moderate resolution multispectral imaging satellites such as Landsat 8 and Sentinel-2, or commercial high-resolution satellites. That is the benefit of the multispectral capabilities of the Sentinel-2 satellite, now available in the Living Atlas. Sentinel-2’s infrared sensitivity (Channel 12; 2.19 micron band) provides the ability to identify areas of active fires, much like NOAA-20 or Aqua/Terra, but at 20m resolution.
In addition to visualizing active fire areas, multispectral imagery is also effective at assessing burn scars. Besides the ecosystem impact, denuded vegetation along sloped areas can lead to landslides, especially when combined with heavy rains.
The NMAS will be the next-generation version of the IAFC’s Mutual Aid Net tool built in 2008. The NMAS will use Esri ArcGIS and Intermedix’s WebEOC, a crisis information management software, to manage and track emergency services resources during mutual-aid responses.
During large-scale emergencies and disasters, it is critical for response personnel to have easy access to a mutual-aid system for managing their resources. WebEOC will allow IAFC to manage information sharing, event reporting and task management in a central, web-based environment that allows IAFC to connect to partner agencies and organizations during response efforts.
The use of spatial data to identify and respond quickly and effectively is also paramount. Esri’s ArcGIS platform brings mutual aid management data into a location context, integrating that information into spatial analysis technology that emergency responders around the world use every day.
The IAFC has long been the leader in supporting state and local fire and emergency management communities in disaster management. The current Mutual Aid Net is used to identify, request and deploy resources for mutual aid support.
The NMAS will use the latest technology to help decision makers accomplish these tasks faster, easier and more accurately.
The use of Intermedix’s WebEOC and Esri’s ArcGIS platforms provides information sharing, decision support and situational awareness capabilities to jurisdictions, regions and countries around the globe.
The foundation of NMAS will be on the WebEOC platform which through the ArcGIS Extension for WebEOC will provide access and integration to Esri online tools and dashboards.
The result of this integration is the near real-time data availability of WebEOC information within ArcGIS Online applications, without the need for any development, middleware or technical expertise.
“The IAFC is extremely pleased to partner with Intermedix and Esri to build the next generation of the National Mutual Aid System,” said Tommy Hicks, IAFC’s Chief Programs & Technology Officer and Assistant Executive Director. “Ensuring that emergency managers and responders have real-time information and resources at their fingertips is an essential to protecting their communities from harm.”
“Identifying the status and availability of resources for mutual aid support has always been challenging,” said Russ Johnson, Esri global director, emergency response. “In today’s environment with increasingly complex multi-jurisdictional incidents, this need is greater than ever. Through the leadership of IAFC and the partnership between Esri and Intermedix, the ability to know the availability of required mutual aid resources and immediately request them will be realized. This will be a major step forward in supporting public safety agencies throughout the country.”
“Intermedix looks forward to our partnership with IAFC and an expansion of our partnership with Esri,” said Bob Watson, Intermedix president of preparedness solutions. “Our mission is to serve those who save lives, and the National Mutual Aid Net project is perfectly aligned with that mission. The only effective way to respond to emergencies is through collaborations and partnerships between public and private organizations. The National Mutual Aid Net takes that principle and puts it into practice. We are honored to be a part of this undertaking.”
Following successful test flights, Insitu’s ScanEagle helps combat Oregon wildfire.
UAV company Insitu and Esri have successfully completed test flights on a new way to support firefighting efforts using software for firefighters and first responders.
The flights were held at the Warm Springs Federal Aviation Administration (FAA) Unmanned Aerial System (UAS) Test Range in Oregon. The test site is a Pan Pacific FAA UAS Test Site for commercial UAS testing. The national FAA test site program facilitates the UAS industry in meeting strict customer needs and qualifications.
Insitu is a wholly-owned subsidiary of The Boeing Company.
A week after successfully completing customer acceptance test flights, Insitu, which has more than one million operational UAS flight hours, deployed its INEXA Solutions professional aerial remote sensing teams to aid firefighters in suppressing the Eagle Creek fire in Oregon.
Onlookers watch the fire burn in the Columbia Gorge on Sept. 4. The fire is now contained. (Photo: U.S. Forest Service)
Collaborating with customers to identify business challenges, INEXA Solutions professionals use a continually expanding suite of capabilities such as INEXA Control (ground-based command and control), INEXA Cloud, INEXA manned and unmanned air vehicles including ScanEagle, and INEXA sensors and analytics to provide custom solutions and answers to mitigate business challenges from seabed to space.
Coordinating with the Oregon Department of Forestry and other governing entities, Insitu’s ScanEagle system provided optimal, near real-time data for firefighters and first responders, resulting in heightened emergency response efforts, increased situational awareness and safety, and supported planning and resource allocation.
Equipped with electro-optical (EO) for daylight and infrared (IR) video for nighttime flights, along with mid-wave sensors, the ScanEagle surveyed fire lines at night over the Eagle Creek wildfire, which had spread to nearly 49,000 acres throughout the Columbia River Gorge region.
The ScanEagle can supplement manned firefighting fleets by operating during dense smoke and at night, when manned aircraft typically cannot fly. Infrared camera technology can penetrate smoke and gather and disseminate georeferenced still images of points of interest. These images allow geographic information system (GIS) specialists to perform analysis using Esri’s ArcGIS software.
“Throughout the difficult Eagle Creek wildfire, our thoughts have been with our friends and neighbors impacted by this unfortunate event,” said Mark Bauman, vice president and co-general manager, Insitu Commercial. “We stand prepared to assist local authorities with ongoing operations in any way we can, and we extend our gratitude to all of those working hard to contain the fire.”
ScanEagle poised for launch at Eagle Creek, Oregon, fire.
As the sole aviation overwatch within the temporary flight restriction, the ScanEagle provided persistent nighttime oversight and monitored the progression of the fire. Insitu coordinated manned and unmanned aviation assets and through data collection, analysis and integration capabilities, produced near real-time georeferenced spatial data (maps tied to specific known locations).
In this way, incident commanders, firefighters, and first responders had data that delivered updated incident perimeter maps, identified spot fires, located fire lines and hotspots, and provided near real-time video feed and still images of critical infrastructure, historical structures and more.
“Prior to pursuing any new effort, we consider the reasons we exist as a company — we call it our ‘why,’ explains Jon Damush, Insitu’s chief growth officer. “Insitu’s ‘why’ is to pioneer and innovate in all that we do to positively impact people’s lives and change the course of history,” he continues. “This statement guides our actions and investments, and is precisely why we are doing the things we are doing to help those in need with our unique technologies and professional approach to aviation.”
A team from Draper and the Massachusetts Institute of Technology (MIT) has developed advanced vision-aided navigation techniques for UAVs that do not rely on external infrastructure, such as GPS, detailed maps of the environment or motion capture systems.
When a firefighter, first responder or soldier operates a small, lightweight flight vehicle inside a building, in urban canyons, underground or under the forest canopy, the GPS-denied environment presents unique navigation challenges.
In many cases, loss of GPS signals can cause these vehicles to become inoperable and, in the worst case, unstable, potentially putting operators, bystanders and property in danger.
Attempts have been made to close this information gap and give UAVs alternative ways to navigate their environments without GPS. But those attempts have resulted in further information gaps, especially on UAVs whose speeds can outpace the capabilities of their onboard technologies.
For instance, scanning lidar routinely fails to achieve its location-matching with accuracy when the UAV is flying through environments that lack buildings, trees and other orienting structures.
Finding a Solution
DARPA awarded contracts to Draper and two other industry teams to create UAVs that autonomously sense and maneuver through unknown environments without external communications or GPS under the Fast Lightweight Autonomy (FLA) program. (Photo: Draper)
Working together under a contract with the Defense Advanced Research Projects Agency (DARPA), Draper and MIT created a UAV that can autonomously sense and maneuver through unknown environments without external communications or GPS under the Fast Lightweight Autonomy (FLA) program.
The team developed and implemented unique sensor and algorithm configurations, and has conducted time-trials and performance evaluations in indoor and outdoor venues.
“The biggest challenge with unmanned aerial vehicles is balancing power, flight time and capability due to the weight of the technology required to power the UAVs,” said Robert Truax, senior member of technical staff at Draper. “What makes the Draper and MIT team’s approach so valuable is finding the sweet spot of a small size, weight and power for an air vehicle with limited onboard computing power to perform a complex mission completely autonomously.”
Draper and MIT’s sensor- and camera-loaded UAV was tested in a number of environments ranging between cluttered warehouses and mixed open and tree filled outdoor environments with speeds up to 10 m/s in cluttered areas and 20 m/s in open areas.
The UAV’s missions were composed of many challenging elements, including tree dodging followed by building entry and exit and long traverses to find a building entry point, all while maintaining precise position estimates.
“A faster, more agile and autonomous UAV means that you’re able to quickly navigate a labyrinth of rooms, stairways and corridors or other obstacle-filled environments without a remote pilot,” said Ted Steiner, senior member of Draper’s technical staff. “Our sensing and algorithm configurations and unique monocular camera with IMU-centric navigation gives the vehicle agile maneuvering and improved reliability and safety — the capabilities most in demand by first responders, commercial users, military personnel and anyone designing and building UAVs.”
Draper’s contribution to the DARPA FLA program — documented in a recent research paper for the 2017 IEE Aerospace Conference — was a novel approach to state estimation (the vehicle’s position, orientation and velocity) called SAMWISE — Smoothing And Mapping With Inertial State Estimation.
SAMWISE is a fused vision and inertial navigation system that combines the advantages of both sensing approaches and accumulates error more slowly over time than either technique on its own, producing a full position, attitude and velocity state estimate throughout the vehicle trajectory.
The result is a navigation solution that enables a UAV to retain all six degrees of freedom and allows it to fly autonomously without the use of GPS or any communication with vehicle speeds of up to 45 miles per hour.
The team’s focus on the FLA program has been on UAVs, but advances made through the program could potentially be applied to ground, marine and underwater systems, which could be especially useful in GPS-degraded or denied environments.
A San Juan interagency hotshot crew member refers to a map on his iPad as he coordinates execution of their burnout operation. (Photo credit: Esther Godson)
Every year thousands of acres of forests are engulfed in fires. Recognizing the benefits of geospatial technology, the United States Forest Service (USFS) Geospatial Management Office (GMO) is using Avenza System Inc.’s award-winning PDF Maps mobile app to deliver interactive digital maps to firefighters and emergency response teams situated in forests across the United States and its territories.
The USFS fights wildfires and other natural disasters in more than 155 national forests and 20 national grasslands, totaling an estimated 193 million acres or 30 percent of all federally managed lands. The USFS GMO is responsible for the implementation of the Forest Service geospatial program which includes using technologies such as GIS, remote sensing, cartography, geodesy and GPS.
The centered blue GPS position on an operations map pinpoints the user’s location. (Photo credit: Carl Beyerhelm)
With increased use of digital solutions, the USFS benefits from Avenza’s PDF Maps app geospatial technology in enabling its thousands of firefighters and support personnel. The PDF Maps app aids emergency response teams who use digital devices for work in the field.
The app provides constant access to geographic information and points of interest, with additional interactive features such as measuring, place marking and location tagging. The app operates without the risk of lost reception due to cell tower proximity and does not rely on an Internet connection to use map data. It uses GPS to obtain and display an accurate position on the ground regardless of network connectivity.
A hardcopy map is compared to its digital counterpart cached on a smartphone. (photo credit: Kari Greer)
“Accessing maps on mobile devices ensure responders have accurate and current geographic information while they’re out in the field,” said Carl Zulick, Geospatial Information Officer, USFS. “Avenza’s PDF Maps app makes it possible for teams to use any map digitally without requiring a data connection while involved in an emergency situation. Since the maps are location-aware and interactive, we can capture real-time data, photos, and locations. This data can be shared to assess the situation and make necessary strategic changes and improve situational awareness.”
The PDF Maps app is available now on the iTunes App Store and Google Play Store free of charge for personal recreational use. A Windows version is currently in public beta release. Commercial, government and academic use licensing is available for a nominal annual fee. Pricing of each map is set by the publisher and free maps remain free to users through the PDF Maps app in-app store. Commercial use licensing starts at US$49 per year and drops on a per-device basis as deployment numbers increase.
Mobile maps help air-tanker pilots avoid the mapped yellow areas, where application of aerial fire retardant is restricted. (Photo credit: Max Wahlberg)
A San Juan interagency hotshot crew member refers to a map on his iPad as he coordinates execution of their burnout operation. (Photo credit: Esther Godson)
Every year thousands of acres of forests are engulfed in fires. Recognizing the benefits of geospatial technology, the United States Forest Service (USFS) Geospatial Management Office (GMO) is using Avenza System Inc.’s award-winning PDF Maps mobile app to deliver interactive digital maps to firefighters and emergency response teams situated in forests across the United States and its territories.
The USFS fights wildfires and other natural disasters in more than 155 national forests and 20 national grasslands, totaling an estimated 193 million acres or 30 percent of all federally managed lands. The USFS GMO is responsible for the implementation of the Forest Service geospatial program which includes using technologies such as GIS, remote sensing, cartography, geodesy and GPS.
The centered blue GPS position on an operations map pinpoints the user’s location. (Photo credit: Carl Beyerhelm)
With increased use of digital solutions, the USFS benefits from Avenza’s PDF Maps app geospatial technology in enabling its thousands of firefighters and support personnel. The PDF Maps app aids emergency response teams who use digital devices for work in the field.
The app provides constant access to geographic information and points of interest, with additional interactive features such as measuring, place marking and location tagging. The app operates without the risk of lost reception due to cell tower proximity and does not rely on an Internet connection to use map data. It uses GPS to obtain and display an accurate position on the ground regardless of network connectivity.
A hardcopy map is compared to its digital counterpart cached on a smartphone. (photo credit: Kari Greer)
“Accessing maps on mobile devices ensure responders have accurate and current geographic information while they’re out in the field,” said Carl Zulick, Geospatial Information Officer, USFS. “Avenza’s PDF Maps app makes it possible for teams to use any map digitally without requiring a data connection while involved in an emergency situation. Since the maps are location-aware and interactive, we can capture real-time data, photos, and locations. This data can be shared to assess the situation and make necessary strategic changes and improve situational awareness.”
The PDF Maps app is available now on the iTunes App Store and Google Play Store free of charge for personal recreational use. A Windows version is currently in public beta release. Commercial, government and academic use licensing is available for a nominal annual fee. Pricing of each map is set by the publisher and free maps remain free to users through the PDF Maps app in-app store. Commercial use licensing starts at US$49 per year and drops on a per-device basis as deployment numbers increase.
Mobile maps help air-tanker pilots avoid the mapped yellow areas, where application of aerial fire retardant is restricted. (Photo credit: Max Wahlberg)
Esri has published an interactive Wildfire Public Information Map and a 2015 California Wildfire Activity Map.
Wildfire Public Information Map
The Wildfire Public Information Map provides continuously updated information about wildfires and their perimeters from the U.S. Geological Survey and other agencies. It provides live weather warnings and wind information from the National Oceanic and Atmospheric Administration, as well as live weather radar from AccuWeather. A local perspective on events is available by turning on geotagged social media from Twitter, YouTube and Flickr on the Layers tab.
2015 California Wildfire Activity
This story map provides a detailed look at 15 active fires throughout California. As you scroll through the map, you can view fire perimeters and hot spots for each active fire, and get up-to-date statistics about each blaze, including total acreage, percent containment and damage caused.
In a striking contrast, the positive and negative sides of unmanned aerial vehicles were highlighted in a single day, July 17. First, in a government-approved demonstration, drones were used to deliver prescription medicine to patients at a temporary health clinic in rural Virginia, reports the Wall Street Journal. The event, reported previously by GPS World, aimed to show how UAVs can alleviate the problem of health-care access while creating economic opportunity for communities.
A manned aircraft carried the packages most of the way, and the flight plan originally called for the drone to make six round trips to carry a total of 10 pounds. But after two successful deliveries, officials decided to send the rest of the payload in one flight.
In stark contrast to that beneficial use of drones, efforts by firefighters to battle a fierce wildfire in California on July 17 were hampered by hobbyists flying consumer drones to capture video of the flames. Planes attempting to deliver water drops found their flights delayed or blocked by the presence of the drones, with private drones flying over the wildfire grounding firefighting aircraft for almost half an hour.
State lawmakers in California are drafting a bill that would impose heavy fines and potential jail time on anyone whose personal drone interferes with firefighting efforts.
When a team of firefighters, first responders, or soldiers operates inside a building, in urban canyons, underground, in foliage, or under the forest canopy, the GPS-denied environment presents unique navigation challenges. An enhanced personal inertial navigation system (ePINS), based on a strapdown navigation solution using a mid-grade IMU and wavelet-based motion-classification algorithms, can track positions with errors of less than 2 percent of distance traveled in both indoor and outdoor environments.
By Yunqian Ma, Wayne Soehren, Wes Hawkinson, and Justin Syrstad
Numerous pedestrian navigation applications are currently available or proposed for development. Some of them include localization for coordinating firefighters, first responders, or soldiers. In these applications, the safety and efficiency of the entire team relies directly on the location and orientation of each team member. Operations in high signal interference areas such as cities, rugged terrain, forest, or indoor spaces deliver intermittent or no GPS signal. An alternative to GPS-based location is required.
In this article, we introduce an enhanced personal inertial navigation system (ePINS) solution specifically designed for environments where GPS is unavailable. ePINS combines an array of state-of-the-art sensors and fusion algorithms into a personal navigation system that provides accurate location information for pedestrian applications.
The ePINS concept.
The ePINS solution has the following benefits:
Accurate positioning in GPS-denied environments;
Small, lightweight unit can be easily carried by first responders, rescue workers, or soldiers;
Ruggedized packaging to withstand difficult first responder and military environments.
Features of the ePINS unit include:
State-of-the-art micro-electromechanical systems (MEMS) gyros and accelerometers, barometric altitude sensor, and advanced navigation software;
Advanced motion classification algorithms that accurately identify and measure user activity;
Immunity to magnetic disturbances.
Related Work
In the field of personal navigation, it is common to find systems that rely on sensors that need infrastructure (for example, Wi-Fi positioning) or sensors that actively emit electro-magnetic radiation (such as Doppler radar). These requirements are major drawbacks for communities such as dismounted soldiers in hostile environments.
Other approaches exploit the so-called Zero-velocity update (ZUPT) mechanism, which resets the inertial measurement unit (IMU) velocity errors during the stationary phase of motion. However, implementation of such schemes relies on sensors embedded in footwear, which is not readily accepted in many user communities.
To address these drawbacks, Honeywell has been developing advanced aiding techniques for personal navigation that do not rely on infrastructure and compute a self-contained, relative-navigation solution based only on passive sensors. One technique that Honeywell has developed uses displacement estimation from human-motion models. This technology has been implemented in the ePINS prototype and shows promising performance.
The human-motion model uses IMU measurements as inputs and was developed to infer distance traveled. It generates a displacement estimate that is used as a measurement in the navigation filtering process. The first version of this model was matured under the DARPA individual Precision Inertial Navigation System (iPINS) program. The iPINS system used an IMU, GPS, barometer, and motion classification to estimate a person’s position in both indoor and outdoor environments. In this system, IMU signal characteristics (e.g., peaks and valleys in the accelerations induced by walking) were exploited to differentiate between walking and running. Honeywell recently expanded the human-motion model to identify more specific motion types using a new wavelet motion classification method.
System Description
Figure 1 displays the hardware architecture of the ePINS, a small battery-powered, highly integrated electronic system. The ePINS processing platform is an ARM11-based, i.MX31 system-on-module, paired with support electronics. In addition to the processing platform, the ePINS assembly includes a MEMS IMU, a barometric pressure sensor, a digital magnetometer, and a GPS receiver.
Figure 1. ePINS hardware architecture.
The MEMS IMU provides inertial measurements for strapdown navigation. The IMU’s small package size, light weight, low power consumption, and impressive performance make it attractive for use in the ePINS system. The device is less than 5 cubic inches and weighs less than 0.35 pounds. It consumes about 3 watts of power with a typical current draw of 600mA at 5V.
The ePINS software system is shown in Figure 2. The navigation software runs within Honeywell’s Embedded Computing Toolbox and Operating System (ECTOS IIc), which provides a layered, customizable, and reusable software architecture for implementing navigation, guidance, and control software. A Honeywell-developed simulation tool for offline analysis and development of ECTOS-based software was also used in ePINS development and testing.
The ePINS demonstration device can achieve path performance of less 2 percent distance traveled for walking motion after 1 hour of operation, independent of the magnetic environment. Current performance, packaging characteristics, and interfaces are summarized in Table 1.
Table 1. ePINS performance objectives and physical specifications.
Algorithm Description
Figure 3 depicts the overall sensor integration and data processing scheme used in the ePINS device.
Figure 3. Sensor integration using the ECTOS extended Kalman filter.
Extended Kalman Filter (EKF). The EKF estimates the navigation and sensor errors and computes the resets applied to the strapdown navigation solution to increase its accuracy. Error models for the navigation sensors (IMU, barometric altimeter, magnetometer, GPS, and motion classification) are contained in the EKF. For the ePINS device, the virtual measurements from the step-length model and the strapdown navigation solution are fused by the EKF to assist in bounding the time dependent error growth of the strapdown navigator, which in turn helps maintain calibration of the inertial sensors. A key output of the EKF is the navigation confidence, which is an estimate of the accuracy of the navigation solution.
An important aspect of the EKF and step-length modeling is the residual test that the EKF supports. This test provides a reasonableness comparison between the step-length model estimate and the distance predicted by the strapdown navigation system. This capability significantly increases the robustness of the navigation solution, especially when the user is engaged in motions not recognized during motion classification.
Human-Motion Model. The human-motion model includes two components: wavelet motion classification and step-length model estimation. The wavelet motion classification identifies the type of motion the user is performing, and the step-length model acts as a virtual sensor that quantifies the motion as a distance-traveled estimate.
Wavelet Motion Classification. Human motions are very diverse and highly irregular. Determining what motion is being performed is a challenging problem of classification. Honeywell’s solution is based on wavelet transformation of IMU data. Predefined, or known, characteristics of a variety of motions (such as walking, running, crawling, etc.) are cataloged and stored to a device’s memory. Estimates of those same characteristics for a user are then computed in real time and compared to the catalog of stored information to find the best match.
Generating the catalog of stored information is an offline task that begins by “segmenting” recorded IMU time domain data into individual steps. An example of the output of the segmentation process is shown in Figure 4.
Figure 4. Segmentation of the IMU data using the y-axis accelerometer signal.
Figure 5 displays the segmentation results for two different walking styles (in red and blue) across approximately 15 example steps. As is evident from the graph, walking has characteristics that are common across users, for example, the sharp peaks in the z-axis acceleration caused by foot-ground impacts. Once the data has been segmented, a wavelet transformation on each data channel is performed. Wavelet transformation for many users over many different motion types takes place offline. Subsequently, a wavelet descriptor is built for each motion type based on the transformations into the wavelet domain. With this method, a wide variety of information (that is, descriptors) suitable for input to a classifier is captured about each motion. These descriptors are then cataloged and stored in memory on the ePINS device.
Figure 5. Sample steps for two subjects (red) and (blue).
Finally, for the online phase, the wavelet descriptor of the incoming IMU data is calculated by performing a wavelet transformation on each data channel. This descriptor is then compared to the pre-computed and stored descriptors to classify the motion. FIGURE 7 shows an example of the motion classifier output, where a running motion was used as an input. The classifier successfully determined the motion type (blue field), frequency and phase of the input motion, depicted by the tallest rectangle in the figure.
Figure 7. Classification results from a query of running at a certain frequency and phase (depicted by the dark sphere).
Step-Length Modeling. Once the current motion is identified, a step-length model specific to that motion is used to aid the navigation algorithms. The model for each motion type is obtained by first collecting data that measures step length and step frequency. From this data, the step-length models can be computed by performing a regression analysis of the step-length vs. step-frequency data. Since the step-length models act as a virtual sensor, the models must be as accurate as possible to achieve better system performance. To attain model accuracy, an accurate data collection method is needed.
For ePINS development, step-length models for multiple users have been identified from step-length and timing information using a precise GPS truth reference system. Step-length regression calculations then determine the step length as a function of step frequency (that is, inverse of the step time period). An example of GPS truth data and the corresponding regression model are shown in FIGURE 6 for walking motions.
Figure 6. Step length versus frequency for the walking of subject.
Although basic step-length models are created offline, online calibration of the step-length model can be performed by the EKF if GPS is available during operation. Online calibration tends to increase the overall position accuracy, as variations in the step-length models are likely due to slight variations in biometric differences across humans, terrain features, and even mission plans and duration.
Heading Determination. Heading initialization is one of the key concerns during system start up. In its current operational use, the ePINS device may perform a dynamic or a static initialization of heading. The static method requires the user to survey the system’s initial heading to an accuracy value that is usually specified by mission performance objectives; the absolute position accuracy is dependent upon the accuracy of the initial heading.
The dynamic method is a general method for heading initialization; it is performed without input from the user, but is possible only when GPS is available. This method of heading initialization does not use any a priori information about heading and requires an EKF implementation with a large-azimuth error model. This method requires an additional period of time in which the heading error uncertainty converges.
User Interface. During a mission, the user can interact with the navigation system and monitor its output on a display. The current ePINS prototype offers two-way communication via a serial connection. The serial communication is made wireless by the addition of a Bluetooth interface. Users can use this link to monitor the status of the navigation solution and to send commands to the device.
Honeywell has developed an application for the Android platform for this purpose. One of the key features of the interface design is that the navigation system outputs data in a standard NEMA format. Thus, publically available Android applications, not just proprietary applications, can also receive and display the navigation solution output by the ePINS device.
Honeywell’s personal navigation application displays the user’s traveled trajectory in real-time. The application can be adapted to include building floor plans as well as other navigation information.
Results
The ePINS prototype has been evaluated both in simulations and indoor/outdoor experiments. The navigation results presented here were obtained in February 2012 at a Honeywell facility (FIGURE 8). First, the user completed the heading calibration, and then online step parameter estimation in the presence of GPS was performed. Once calibration and training was completed, the GPS was disabled to simulate a GPS-denied environment outdoors. The user than transitioned to indoors (with GPS still disabled), and walked a course inside that included walking up and down stairs (FIGURE 9) and ended in a conference room (FIGURE 10).
Figure 8. Course for the Honeywell facility demonstration.
Figure 9. The user walking up stairs.
Figure 10. The user at the end of the demo.
Over these conditions, the ePINS system performed robustly and within performance specifications. Live demonstrations and testing showing similar levels of performance were performed at the 2012 Joint Navigation Conference (JNC) and at military test sites in California and Indiana.
Summary
The technical approach of the ePINS solution to the problem of personnel navigation in GPS-denied environments is based on a strapdown navigation solution maintained using a mid-grade IMU and advanced motion-classification algorithms. We integrated an array of sensors and software into a system that provides accurate position information and is suitable for use by first responders, soldiers, and other personnel where GPS is unavailable. ePINS works well for a variety of pedestrian motion types, including walking, running, crawling, walking upstairs, walking downstairs, sidestepping, and walking backwards. The motion classification and modeling method is extensible to other motion types.
We tested the ePINS system in indoor and outdoor environments. FIGURE 11 depicts the future ePINS concept, and TABLE 2 presents its future physical characteristics.
Figure 11. Future ePINS concept and mounting position.
Table 2. Packaging characteristics of the future ePINS.
Acknowledgments
This article is based on a presentation made at ION GNSS 2012.
Manufacturers
The ePINS processing platform uses Honeywell Agile Navigation and Guidance Integrated Electronics support electronics. It includes a Honeywell HG1930 MEMS IMU, a Bosch Sensortec BMP085 barometric pressure sensor, a Honeywell HMC6343 digital magnetometer, and a NovAtel OEMStar GPS receiver.
Yunqian Ma is a principal scientist at Honeywell Aerospace. He received his Ph.D. degree in electrical engineering from the University of Minnesota, Twin Cities. He is currently the program manager of the GPS-denied navigation program and the next-generation personal navigation program.
Wayne Soehren is a senior technical manager at Honeywell Aerospace. He was the program manager for the development of Honeywell’s first MEMS-based GPS/INS, which developed the core capability now used in Honeywell’s IGS-2XX family of MEMS-based GPS/INS products. He holds an MSEE from the University of Minnesota.
Wes Hawkinson is an engineering fellow at Honeywell Aerospace. He holds a BSEE/CE from the University of Wisconsin–Madison. Justin Syrstad is a guidance and navigation scientist. He received a master’s degree in aerospace engineering from the University of Minnesota.
