Adaptive sensing for target tracking in covert operations

Abstract

Multisensor applications rely on effectively managing sensor resources. In particular, next-generation multifunctional agile radars demand innovative resource management techniques to achieve a common sensing goal while satisfying resource constraints. We consider an active sensing platform where multiple waveform-agile radars scan a hostile surveillance area for targets. A central controller adaptively selects which transmitters should be active and which waveforms should be transmitted. The controller's goal is to choose the sequence of (transmitter, waveform) pairs that yields the most accurate tracking estimate. We formulate this problem as a partially observable Markov decision process (POMDP), and propose a novel "two-level" scheduling scheme that uses two distinct schedulers: (1) at the lower level, a myopic waveform scheduler; and (2) at the upper level, a non-myopic transmitter scheduler. Scheduling decisions at these two levels are carried out differently. While waveforms are updated at every radar scan, a new set of transmitters only becomes active if the overall tracking accuracy falls below a given threshold, or if the "detection risk" is exceeded, given by a limit on the number of consecutive scans during which a set of transmitters is active. By simultaneously exploiting myopic and non-myopic scheduling schemes, we benefit from trading off short-term for long-term performance, while maintaining low computational costs. Moreover, in certain situations, the myopic scheduling of waveforms at each radar scan improves on non-myopic actions taken in the past. Monte Carlo simulations are used to evaluate the performance of the proposed adaptive sensing scheme in a multitarget tracking setting.

Interval Algebra – An effective means of scheduling surveillance radar networks

“…The alternative choice is to employ longer-term planning, which considers more historic information and generates long-term predictions. Common solutions for the latter choice include partially observed Markov decision processes (POMDPs) [7,8] and multiple-armed bandits (MABs), a simplification of the Markov decision process (MDP) [9,10]. These are both types of Bayesian networks and, while they are promising, they often lead to numerically difficult solutions.…”