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Abstract: To address the problem of maneuvering target tracking, where the target trajectory has prolonged smooth regions and abrupt maneuvering regions, a modified variable rate particle filter (MVRPF) is proposed. First, a Cartesian-coordinate based variable rate model is presented. Compared with conventional variable rate models, the proposed model does not need any prior knowledge of target mass or external forces. Consequently, it is more convenient in practical tracking applications. Second, a maneuvering detection strategy is adopted to adaptively adjust the parameters in MVRPF, which helps allocate more state points at high maneuver regions and fewer at smooth regions. Third, in the presence of small measurement errors, the unscented particle filter, which is embedded in MVRPF, can move more particles into regions of high likelihood and hence can improve the tracking performance. Simulation results illustrate the effectiveness of the proposed method.

This paper presents a hybrid work that integrates the well-known technologies for maneuver-detection (MD), variable rate particle filtering (VRPF) and unscented particle filter (UPF), as the name of the proposed filter 'MD-VR-UPF' indicates. Although new contribution is limited, the simulation results show that good result has been achieved. Overall, the paper is well written.

改进的变速率粒子滤波及其在机动目标跟踪中的应用

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