自适应IMM-UKF机动目标跟踪算法

doi:10.12305/j.issn.1001-506X.2025.08.26

摘要/Abstract

摘要：

针对跟踪复杂机动目标过程中由于目标运动状态发生变化导致的跟踪误差较大的问题，提出一种自适应交互多模型无迹卡尔曼滤波（interacting multiple model unscented Kalman filter，IMM-UKF）算法，使用模型概率后验信息和模型似然函数自适应修正马尔可夫转移概率矩阵（transition probability matrix，TPM）。设计模型概率校正方法和模型转移加速方法，两种方法分别作用于模型稳定阶段和模型转移阶段，提高模型概率准确度和模型转移响应速度，减小状态估计误差。最后，通过两种场景下的实验验证所提算法在目标具有复杂运动状态下的性能，并与传统方法进行对比分析，在目标做机动运动时，位置精度和速度精度分别提高了15%和26%，验证了算法的有效性和可行性。

关键词: 目标跟踪, 交互多模型, 自适应, 无迹卡尔曼滤波

Abstract:

Aiming at the problem of large tracking error caused by the change of target motion state in the process of tracking complex maneuvering targets, an adaptive interacting multiple model unscented kalman filter （IMM-UKF） algorithm is proposed, which uses model probability posterior information and model likelihood function to adaptively modify Markov transition probability matrix （TPM）. The model probability correction method and model transition acceleration method are designed. The two methods act on the model stability stage and model transition stage respectively, which improve the model probability accuracy and model transition response speed, and reduce the state estimation error. Finally, the performance of the proposed algorithm in the target with complex motion is verified by experiments in two scenarios, and compared with the traditional methods, the position accuracy and velocity accuracy are improved by 15% and 26% respectively when the target is maneuvering, which verifies the effectiveness and feasibility of the algorithm.

Key words: target tracking, interacting multiple model （IMM）, adaptive, unscented Kalman filter （UKF）

中图分类号:

TN 953

周晓, 牟新刚, 柯文, 苏盈, 王丽. 自适应IMM-UKF机动目标跟踪算法[J]. 系统工程与电子技术, 2025, 47(8): 2686-2695.

Xiao ZHOU, Xingang MOU, Wen KE, Ying SU, Li WANG. Adaptive IMM-UKF maneuvering target tracking algorithm[J]. Systems Engineering and Electronics, 2025, 47(8): 2686-2695.

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运动模型	时间/s	运动参数
CA	1~200	a = 10 m/s²
CV	201~400	v = 300 m/s
CT	401~600	ω = 0.02 rad/s

算法	位置/m	速度/（m/s）
IMM-KF	82.15	25.22
IMM-UKF	78.55	25.17
AIMM-UKF	76.89	16.55
ATPM-UKF	65.75	12.17

算法	位置/m	速度/（m/s）
IMM-KF	10.81	14.21
IMM-UKF	9.43	11.74
AIMM-UKF	9.40	11.47
ATPM-UKF	8.13	10.26

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