Distributed Adaptive Grid Interacting Multiple Model Joint Probabilistic Data Association Algorithm

Xu Jianghu, L. Zhong
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Abstract

Adaptive grid interacting multiple model (AGIMM) algorithm is combined with joint probabilistic data association (JPDA), distributed adaptive grid interacting multiple model joint probabilistic data association (DAGIMM- MSJPDA) algorithm is proposed using for multisensor multitarget tracking. In the algorithm, the model probabilities, model-conditioned state estimation and its covariance are fused in fusion centre of this algorithm based on fuzzy weighted method after track association is finished, then the overall state estimation and its covariance can be obtained. The fused model probabilities is feed backed to all sonsors, so uniform and more accurate grid adaptation can be carried out. Validity and correctness of the algorithm is justified by computer simulation.
分布式自适应网格交互多模型联合概率数据关联算法
将自适应网格交互多模型(AGIMM)算法与联合概率数据关联(JPDA)算法相结合,提出了用于多传感器多目标跟踪的分布式自适应网格交互多模型联合概率数据关联(DAGIMM- MSJPDA)算法。该算法在航迹关联完成后,采用模糊加权法将模型概率、模型条件状态估计及其协方差融合到算法的融合中心,得到整体状态估计及其协方差。将融合后的模型概率反馈给所有传感器,从而实现更均匀、更精确的网格自适应。计算机仿真验证了该算法的有效性和正确性。
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