中文摘要：

针对杂波环境下的机动目标跟踪,该文提出一种基于自适应关联门的跟踪算法。该算法以传统交互多模型概率数据关联算法为基础,在关联门内无有效量测点迹时,假设目标在前一滤波时刻或是更早时刻以最大机动水平改变原运动模式,利用该假设条件下所获得的目标预测量测及当前真实预测量测,对用于确定关联门的新息协方差进行修正,使得关联门逐步适当扩大,以尽可能地包含目标真实量测点迹。仿真结果表明,自适应关联门跟踪算法能在不影响跟踪精度和算法运算量的情况下,有效降低机动目标的跟踪丢失概率。

英文摘要：

A tracking algorithm with adaptive association gate is proposed to the maneuvering target tracking in clutters.The algorithm is based on conventional Interacting Multiple Model Probabilistic Data Association （IMM-PDA） algorithm,and the target is assumed to change its moving mode with the maximal maneuvering level at current or heretofore moments when there is no valid measurement in the association gate.The innovation covariance used to determine the association gate is modified according to the predicted measurement in the maximal maneuvering hypothesis and the actual one.The association gate is enlarged step by step appropriately to gate the target measurement as far as possible.Simulation results show that,the proposed algorithm can improve the tracking loss rates of maneuvering target effectively without decreasing tracking precision or increasing computational complexity.