access icon free Non-myopic sensor scheduling to track multiple reactive targets

This study addresses the sensor scheduling problem of selecting and assigning sensors dynamically for multi-target tracking. The authors goal is to trade off the tracking accuracy and the interception risk in a period of time. The interception risk is incurred by the fact that the emission energy originating from a sensor can be intercepted by the target during the tracking mission. To react to sensor emission, the targets are able to switch between dynamic models. This non-myopic sensor scheduling problem is formulated as a partially observable Markov decision process, where the one-step reward is constructed by combining the tracking error with the interception probability and the information state is tracked by the interacting multiple model extended Kalman filtering. A novel sampling approach using the unscented transformation is proposed for long-term reward approximation. Numerical simulations illustrate the validity of the proposed scheduling scheme.

Inspec keywords: numerical analysis; nonlinear filters; Markov processes; sensors; target tracking; signal sampling; Kalman filters

Other keywords: sampling approach; interacting multiple model extended Kalman flltering; tracking mission; tracking accuracy; information state; numerical simulations; dynamic models; sensor emission; emission energy; tracking error; nonmyopic sensor scheduling; one-step reward; multiple reactive target tracking; unscented transformation; interception probability; long-term reward approximation; partially observable Markov decision process

Subjects: Sensing devices and transducers; Other numerical methods; Other numerical methods; Markov processes; Signal processing theory; Filtering methods in signal processing; Markov processes

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