将改进的粒子滤波算法即基于均匀重采样的粒子滤波（AUPF）与交互式多模型算法（IMM）相结合，提出交互式多模型均匀重采样粒子滤波算法（IMM-AUPF），并将其应用于被动多传感器的机动目标跟踪中。均匀重采样粒子滤波在标准粒子滤波的基础上通过改进重采样过程，在解决粒子退化问题的同时，增加了粒子的多样性，提高了滤波性能。在多模型中应用均匀重采样粒子滤波，提高被动多传感器系统的机动目标跟踪精度。将该方法与交互式多模型粒子滤波算法（IMM-PF）进行仿真对比，结果表明该方法具有更好的跟踪性能。
The proposed method in this paper is the binding of improved particle filter which called uniform resampling particle filter and interacting multiple model algorithm. The proposed method is called interacting multiple model uniform resampling particle filter, and is applied to target tracking for passive multi-sensor. The resampling process is improved in uniform resampling particle filter compared with standard particle filter. While ensuring solving the degeneration problem of particle, the variety of particle and the filtering properties are improved. Uniform resampling particle filter is applied in interacting multiple model to improving the tracking accuracy of maneuvering target for passive multi-sensor. By comparing the proposed method and interacting multiple model particle filter, better tracking performance of proposed method is presented.
2017,39(3): 145-149,157 收稿日期：2016-03-30
DOI：10.3404/j.issn.1672-7619.2017.03.030
分类号：TP301.6
作者简介：刘威(1989-),男,助理工程师,研究方向为主被动声呐目标定位
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