Target tracking with distributed sensing: information-theoretic bounds and closed-loop scheduling for urban terrain - de Rezende Barbosa, Patricia, author; Chong, Edwin K. P., advisor; Scharf, Louis L., committee member; Luo, J. Rockey, committee member; Lee, Chihoon, committee member (Colorado State University. Libraries, 2010)
We address both theoretical and practical aspects of target tracking in a distributed sensing environment. First, we consider the problem of tracking a target that moves according to a Markov chain in a sensor network. We provide necessary and sufficient conditions on the number of (queries per time step to track a target in three different scenarios: (1) the tracker is required to know the exact location of the target at each time step; (2) the tracker may lose track of the target at a given time step, but it is able to “catch-up”, regaining up-to-date information about the target’s track; and (3) tracking information is only known by the tracker after a delay of d time steps. We then address the problem of target tracking in urban terrain. Specifically, we investigate' the integration of detection, signal processing, tracking, and scheduling, by simultaneously exploiting three diversity modes: (1) spatial diversity through the use of coordinated multistatic radars; (2) waveform diversity by adaptively scheduling the transmitted waveform; and (3) motion model diversity by using a bank of parallel filters matched to different motion models. A closed-loop active sensing system is presented, and Monte Carlo simulations demonstrate its effectiveness in urban terrain. Finally, we propose a scheduling scheme that adaptively selects the sequence of transmitters and waveforms that maximizes the overall tracking accuracy, while maintaining the sensing system’s covertness in a hostile environment. We formulate this problem as a POMDP and use two distinct schedulers: (1) a myopic scheduler that updates waveforms at every radar scan; and (2) a non-myopic scheduler that activates a new set of transmitters if the overall tracking accuracy falls below a threshold or if a detection risk is exceeded. By simultaneously exploiting myopic and non-myopic scheduling schemes, with benefit from trading off short-term for long-term performance, while maintaining low computational costs. Monte Carlo simulations are used to evaluate the proposed scheduling scheme in a multitarget tracking setting.
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Keywords: Sensor networks; Tracking radar; Target acquisition