针对杂波背景下多机动目标跟踪问题,提出一种基于时变转移概率交互式多模型(IMM)的模糊数据关联跟踪算法。首先,针对传统IMM算法模型转移概率假设为常数导致模型间过度竞争的问题,基于贝叶斯理论,推导出一种时变模型转移概率IMM算法,增强了优势模型的利用率;其次,针对传统JPDA算法由于聚矩阵拆分而导致的计算组合爆炸问题,利用模糊聚类的方法,直接计算相关波门内候选量测与目标间的关联概率,用概率加权对目标进行状态和协方差的更新。仿真实验表明:算法对不同机动目标的跟踪适应性得到增强,相比传统的JPDA算法,在保证跟踪精度的基础上其时间性能比较优越,是一种较为实用的工程应用算法。

The fuzzy probabilistic data association algorithm based on the interacting multiple model ( IMM) algorithm with the time-varying transition probability is proposed in this paper to solve the problem of multiple maneuvering targets tracking in clutter. First, the improved IMM algorithm uses the time-varying transition probability based on Bayes theorem to decrease the excessive competition when the transition probability is constant value in traditional algorithm. Second, to solve the problem of the combination explosion when separating the polymer matrix, the proposed algorithm calculates the association probability on the basis of fuzzy clustering, which is used as weight to update target’s state and covariance. Simulation results show that the tracking adaptation to different targets has been enhanced and the real-time performance of the tracking is improved under the premise of tracking accuracy compared with traditional JPDA algorithm. This is a practical engineering application algorithm.

TN957. 52