Random finite sets-based joint manoeuvring target detection and tracking filter and its implementation
Ying Wei, Yaowen Fu, Long Jian-qian, Xiang Li · IET Signal Processing · 2012
This study considers the problem of jointly detecting whether a target is present in a scene and estimating its state, if it is there. This joint detection and estimation problem can be solved using a special case of the multi-target Bayes filter (referred to as the joint target detection and tracking (JoTT) filter). However, if the model used by the JoTT filter does not match the actual dynamics, the filter will tend to miss-detection directly or diverge such that the actual errors fall outside the range predicted by the filter's estimate of the error covariance. A similar difficulty arises, if the target behaviour can switch between different modes of operation, since the filter may then be accurate for only one particular mode. This study proposes a novel joint detection and tracking filter, which is the multiple model extension of the JoTT filter to accommodate the possible target manoeuvring behaviour. In addition, a sequential Monte Carlo implementation (for generic models) and a Gaussian mixture implementation (for linear Gaussian models) are proposed. The simulation results are presented to show the effectiveness of the proposed filter over the original JoTT filter.