Joint passive sensor scheduling for target tracking

In this paper, we investigate cooperative passive sensor trajectory planning for tracking a target where the tracking error is sensor trajectory dependent. We consider the problem under a scenario of tracking a moving target using two unmanned bearings-only sensors. The basic idea is to maximise the target information acquired from the processing measurements of the two sensors by cooperatively scheduling their future trajectories at which sensor measurements will be taken. In the literature this problem is modeled by a partially observed Markov decision process and optimal action which maximises an expected reward function is sought. Three reward functions, namely, the Expected Reward, the Determinant, and Trace of the associated Fisher Information Matrix (FIM) for the underlying problem are analysed and discussed. These rewards may only be evaluated practically through various approximations. We show that the correlation between two sensor states is weakened significantly for the Expected Reward due to linearisation and thus the closed-form Expected Reward as well as the Trace of FIM are inappropriate for this sensor trajectory scheduling problem. Finally, we present simulation results which are based on the example of a non-cooperative target chasing via two cooperative bearing-only sensors.