William W. Whitacre

Cooperative Tracking Using Vision Measurements on SeaScan UAVs

A cooperative tracking approach for uninhabited aerial vehicles (UAVs) with camera-based sensors is developed and verified with flight data. The approach utilizes a square root sigma point information filter, which takes important properties for numerical accuracy (square root), tracking accuracy (sigma points), and fusion ability (information). Important augmentations to the filter are also developed for delayed data, by estimating the correlated processes, and moving targets, by using multiple models in a square root interacting multiple model formulation. The final form of the algorithm is general and scales well to any tracking problem with multiple, moving sensors. Flight data using the SeaScan UAV is used to verify the algorithms for stationary and moving targets. Cooperative tracking results are evaluated using multiple test flights, showing excellent results.

Cooperative Tracking of Moving Targets by a Team of Autonomous UAVs

This paper summarizes current work on theoretical and experimental cooperative tracking of moving targets by a team of UAVs. The Insitu Group is leading a diverse group of researchers to develop building block foundations for cooperative tracking. The building block algorithms have been maturing through the partners, and the team led by Insitu is now pulling the technologies together for demonstration and commercialization. The work reported here focuses on cooperative tracking using multiple UAVs, with the ability for one operator to control many UAVs which are tasked to 1) provide autonomous tracking of moving and evading targets, and 2) report to a centralized database (without operator attention): the position, position history, velocity vector of the target being tracked. Flock guidance algorithms have been developed and simulated to enable a flock of UAVs to track an evading vehicle. Algorithms have been demonstrated in simulation that: dynamically allocate tasks and compute near-optimal paths in real-time; minimize the probability that vehicles are destroyed due to collision or damage from threat; and accommodate moving targets, time-on-targets, and sequencing, as well as the effects of weather (especially wind) and terrain. Additionally geolocation estimation algorithms and software have been developed which exchange information among vehicles, process the information robustly and in real time, and have demonstrated that the joint accuracy is improved. Work has also focused on accurate probabilistic analysis of the estimates, especially considering variations across multiple vehicle sets of ScanEagle UAVs

Flight Results from Tracking Ground Targets Using SeaScan UAVs with Gimballing Cameras

Flight test results using a SeaScan UAV with a gimballing camera to track both stationary and moving ground targets are presented. We experimentally studied the effect of UAV altitude above the target, camera field of view, and orbit center offsets within the geolocation tracking performance for both stationary and moving targets. In addition, all of the tests were performed using two different aircraft navigation systems, showing important sensitivities within the system. Sensor biases are shown to directly cause slowly varying errors in the geolocation estimates which can dominate tracking performance. These errors, which typically oscillate with the UAV orbit, are adequately bounded with a geolocation estimator which captures both the target tracking uncertainty, as well as unobservable sensor biases.

Decentralized Geolocation and Bias Estimation for Uninhabited Aerial Vehicles with Articulating Cameras

The cooperative geolocation of a point of interest using multiple uninhabited aerial vehicles with articulating camera sensors is addressed, where there are non-zero-mean errors (biases) in the estimate of the uninhabited aerial vehicle state. The proposed approach is to use the onboard navigation solution in the estimator and, further, to consider biases across all uninhabited aerial vehicles and to jointly estimate both the biases and the unknown point-of-interest location. Furthermore, a decentralized solution is presented that uses marginalization of the biases, thus allowing the uninhabited aerial vehicles to share only information about the point of interest and model only their local biases. This decentralized approach saves significant computation and scales well with the number of uninhabited aerial vehicles. Real flight-test data and hardware-in-the-loop simulations are used to demonstrate the improvement in geolocation with bias estimation, as well as the effectiveness of the new decentralized point of interest and bias estimation algorithm, for both stationary and moving points of interest.

Cooperative Geolocation with UAVs Under Communication Constraints

Cooperative tracking of a ground target using camera measurements on SeaScan UAVs with periodic access to inter-vehicle communication is considered. The optimal solution to the communication limited estimation problem for systems with known measurement equations and statistics is derived and insight from this solution is used to develop two new methods. These methods attempt to approximate the information matrix updates to be communicated. The first method uses a piecewise constant approximation to the information matrix updates and the second method estimates the information matrix updates to be shared directly from estimates of the cooperating UAV states. These new methods are evaluated using flight test data and show tracking accuracy comparable to the full communication accuracy even with a drastic reduction in communication.

Developing a Robust and Flexible Simulation Environment to Support Cooperative Tracking of UAVs

This paper will discuss the physical and technical challenges of developing a robust and flexible system for the simulation and implementation of cooperative tracking of moving targets by a group of unmanned aerial vehicles (UAVs). Insitu, Inc. with assistance from the University of Washington and Cornell, has been developing a simulation testbed for the cooperative tracking project which has unique needs. The requirements include multiple computers serving as two simulated aircrafts and a ground control station, with a data distribution system to relay the information between the different components. This system needs to be modular in order to facilitate quick disassembly and reassembly in a different location, and needs to support testing in a stand-alone mode as well as with a hardware-inthe-loop (HIL) simulator to further simulate the aircraft and finally to support the actual flight tests. Minimal adjustment to the hardware or software configuration between these different modes is desired. The challenges for this system have included creating a physical setup that includes all of the necessary hardware while being compact enough to be easily moved from site to site, implementing a framework flexible enough to incorporate code and scripts written in a variety of languages, finding an agile and robust data distribution method and setting up a simulation system that requires minimal effort to initialize.

Cooperative Estimation Using Mobile Sensor Nodes in the Presence of Communication Loss

Cooperative estimation using multiple mobile sensor nodes communicating over a lossy network is considered. A new method, termed predicted information, is developed from a separable formulation of the extended information filter. Two variations of the predicted information method are presented, which trade between accuracy and computational complexity. The first variation estimates the information matrix updates directly from estimates of the cooperating sensor node states. The second variation uses a piecewise constant approximation to predict the informationmatrixupdates.Thepredictedinformation methodisshowntogivetheexactsolutionforlinearsystems when the measurement dynamics are constant or known by all sensor nodes. The predicted information method is evaluated with a cooperative geolocation problem with two uninhabited aerial vehicles using gimballing camera sensors. Flight-test data and high-fidelity hardware-in-the-loop simulations are used to compare the predicted information method with three benchmark methods from the literature for tracking both stationary and maneuvering targets and for single extended losses and random dropouts.

Autonomous Cooperative Geo-Location and Coordinated Tracking of Moving Targets

This is an overview of current developments in support of autonomous geolocation and tracking of moving targets with a flock of coordinated UAVs. The Insitu Group leads a group of researchers with the aim of geolocating and tracking of ground targets. The UAVs use coordinated maneuvering to accommodate maximum sensor precision. Flock guidance logic was demonstrated in real time on a distributed network with representative target data and image processing. Geolocation estimation algorithms and software were developed which exchange information among vehicles, process the information robustly in real time, and have demonstrated that the joint precision is improved.