关键词: 机动目标, 交互式多模型, 广义概率数据关联算法, D-S证据组合规则

Abstract: To effectively solve the problems of distributed multi-sensor multi-maneuvering target tracking under the dense clutter scenario, a new distributed interacting multiple model multi-sensor generalized probabilistic data association algorithm based on improved D-S combination rule of evidence（DIMM-MSGPDA-IDS） is proposed. Firstly, a single sensor IMM-GPDA algorithm is used to track multiple maneuvering targets on each local node, and the filtering results of each model, such as the state estimate, covariance estimate, model probability, combination of residuals and the corresponding covariance matrices, are sent to the fusion center. Secondly, after obtaining the result of track correlation, the state estimate and the covariance matrix about the same target of different sensors are fused according to the likelihood function of each model in the fusion center, and the model probabilities of the same target from different sensors are fused by using the new improved D-S combination rule of evidence which fuses the 3-dimensional（3-D） evidence together. Then the probability is used to update the target state estimate and the result is returned back to the local node. Therefore, more accurate state prediction of target is obtained. Finally, through simulations, the new algorithm is compared and analyzed with DIMM-MSJPDA-DS algorithm. Theoretical analysis and simulation results show that the new algorithm does well in tracking the strong maneuvering target. What’s more, through the whole algorithm, only a small amount of computation is involved. So it can be concluded that the new algorithm is an effective distributed interactive multi-model multi-sensor multi-maneuvering target tracking algorithm.

Key words: maneuvering target, interactive multiple model, generalized probabilistic data association algorithm, D-S combination rule of evidence