Abstract
The paper considers the problem of tracking a moving target using a pair of cooperative bearing-only mobile sensors. Sensor trajectory optimisation plays the central role in this problem, with the objective to minimize the estimation error of the target state. Two approximate closed-form statistical reward functions, referred to as the Expected Rényi information divergence (RID) and the Determinant of the Fisher Information Matrix (FIM), are analysed and discussed in the paper. Being available analytically, the two reward functions are fast to compute and therefore potentially useful for longer horizon sensor trajectory planning. The paper demonstrates, both numerically and from the information geometric viewpoint, that the Determinant of the FIM is a superior reward function. The problem with the Expected RID is that the approximation involved in its derivation significantly reduces the correlation between the target state estimates at two sensors, and consequently results in poorer performance.
Highlights
Multiple passive sensors can be cooperatively used in a target tracking system to achieve target observability
Since the target state in the Fisher Information Matrix (FIM) calculation can only be approximated by the estimate from tracker, the reward calculated by Determinant of FIM (DetFIM) can be senseless if the track error is significantly large
We investigated the problem of sensor trajectory optimisation for tracking a moving target using multiple cooperative passive sensors
Summary
Multiple passive sensors can be cooperatively used in a target tracking system to achieve target observability. A 2D moving target can be observed by two cooperative bearing-only sensors, while they exhibit observability issues individually [2]. In this case, the track accuracy is highly dependent on the locations of the sensors when taking measurements. The sensor trajectory planning problem is known as trajectory scheduling or optimisation in the literature It can be cast as a partially observed Markov decision process (POMDP) [5,6], where the decision process is carried out by minimising the cost or maximising the reward against a measure criterion that is related to the Fisher information [7,8,9]
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