CDS Invited Lecture/ISR Alumni Seminar: Andrew Newman, "UAV Motion Planning and Resource Management"
Wednesday, October 21, 2009
2460 A.V. Williams Building
301 405 6634
Control and Dynamical Systems Invited Lecture Series
ISR Alumni Seminar
UAV Motion Planning and Resource Management of Heterogeneous Platform and Sensor Ensembles
Andrew J. Newman
Principal Professional Staff and Section Supervisor of the Data Fusion Analysis Section of the Weapon and Targeting Systems Group
The Johns Hopkins University Applied Physics Laboratory (JHU/APL)
A reception and student coffee hour will follow the lecture
Ground targets performing unpredictable maneuvers and operating in cluttered environments such as urban areas pose severe challenges to current surveillance systems. It is often necessary to detect, track, and classify multiple targets distributed over broad areas in the face of difficulties such as varying backgrounds, low target observables, occlusions, dense and intersecting traffic, stop-and-go cycles, on-off road transitions, and intentional target countermeasures. The rapidly increasing number, diversity, agility, and capability of surveillance platforms and sensors present an opportunity to deal with the problem. New techniques and algorithms are required to enable effective dynamic collaborative resource management of heterogeneous platform and sensor ensembles that scale to typical problem sizes of interest.
This talk will focus on the problem of non-myopic multiple platform trajectory control in a multiple target search and track setting. It will present a centralized receding discrete time horizon controller (RHC) with variable-step look-ahead for motion planning of a heterogeneous ensemble of airborne sensor platforms. The controller operates in a closed feedback loop with a Bayesian Multiple Hypothesis Tracker (MHT) that fuses the disparate sensor data to produce target declarations and state estimates. The RHC action space for each air vehicle is represented via maneuver automaton with simple motion primitives. The reward function is based on expected Fisher information gain and priority scaling of target tracks and ground regions. A customized Particle Swarm Optimizer (PSO) is developed to handle the resulting non-Markovian, time-varying, multi-modal, and discontinuous reward function. The algorithms were evaluated by simulating ground surveillance scenarios using representative platforms and sensors with varying fields of view, typical target densities and motion profiles, and environmental characteristics such as varying backgrounds and occlusions. Simulation results show improved aggregate target detection, track accuracy, and track maintenance for closed loop operation as compared with typical open-loop surveillance plans.
This talk will also introduce recent applications of these techniques and algorithms to the space surveillance domain. As our reliance on space systems and space technology grows, the consequences of collisions and other spacecraft interactions are becoming increasingly serious. Even relatively small objects can severely damage or destroy a satellite. It is essential to maintain an accurate catalog of most, if not all, objects in orbit. New capabilities, including automated data fusion and resource management of heterogeneous sensor ensembles, are required to accurately detect, track, and classify small objects in earth orbit at a variety of altitude regimes.
Andrew Newman is a member of the APL Principal Professional Staff and Section Supervisor of the Data Fusion Analysis Section of the Weapon and Targeting Systems Group. He received a B.S. in Systems Engineering from the University of Pennsylvania (1987), a M.S. in Electrical Engineering from the University of Virginia (1992), and his Ph.D. in Electrical and Computer Engineering from the University of Maryland (1999). Since joining APL in 2003, he has worked on projects in sensor and data fusion, ground target tracking, space situation awareness, and Intelligence, Surveillance, and Reconnaissance (ISR) resource management. He has led or supported a number of tasks for major programs in those areas including Dynamic Time Critical Warfighting Capability, Upstream Data Fusion for Space Situation Awareness, Global Net-Centric Surveillance and Targeting, and Joint Battle Management Command and Control. He has led several Internal Research and Development projects including Precision Engagement of Moving Ground Targets, Tactically Responsive ISR Management, ISR Performance Prediction, and Sensor Fusion and Resource Management for Space Situation Awareness. Previously, he worked for ALPHATECH, Inc. of Arlington, VA, where he developed optimization and control algorithms for ISR resource management in support of several DARPA and Air Force Research Lab research programs.