Sun Dajun, Zhang Yiao, Teng Tingting, Gao Linsen
National Key Laboratory of Underwater Acoustic Technology, Harbin Engineering University, Harbin 150001, China.
J Acoust Soc Am. 2023 Nov 1;154(5):2843-2857. doi: 10.1121/10.0022323.
Noncooperative maneuvering target motion analysis is one of the challenging tasks in the field of underwater target localization and tracking for passive sonar. Underwater noncooperative targets often perform various maneuvers, and the targets are commonly modeled as a combination of constant-velocity models and coordinate-turn models with unknown turning rates. Traditional algorithms for Doppler-bearing target motion analysis are incapable of processing noncooperative maneuvering targets because the algorithms rely on a priori information of the turning rate and the center frequency. To address these shortcomings, this paper proposes the joint estimated adaptive unscented Kalman filter (JE-AUKF) algorithm. The JE-AUKF places the center frequency and turning rate into the state vector and constructs a time-varying state model that self-adapts to a maneuvering target. The JE-AUKF also introduces a time-varying fading factor into the process noise covariance matrix to improve the tracking performance. Simulations and sea trials are conducted to compare the performance of the JE-AUKF with the iterative unscented Kalman filter, the interacting multiple model-unscented Kalman filter, the interacting multiple model-iterative unscented Kalman filter, and the interacting multiple model-joint estimated unscented Kalman filter. The result shows that the JE-AUKF achieves better tracking performance for noncooperative maneuvering targets.
非合作机动目标运动分析是被动声纳水下目标定位与跟踪领域中具有挑战性的任务之一。水下非合作目标常常进行各种机动，并且这些目标通常被建模为具有未知转弯率的常速模型和坐标转弯模型的组合。传统的多普勒 - 方位目标运动分析算法无法处理非合作机动目标，因为这些算法依赖于转弯率和中心频率的先验信息。为了解决这些缺点，本文提出了联合估计自适应无迹卡尔曼滤波器（JE - AUKF）算法。JE - AUKF将中心频率和转弯率放入状态向量中，并构建一个能自适应机动目标的时变状态模型。JE - AUKF还在过程噪声协方差矩阵中引入了时变衰减因子，以提高跟踪性能。进行了仿真和海上试验，将JE - AUKF的性能与迭代无迹卡尔曼滤波器、交互多模型 - 无迹卡尔曼滤波器、交互多模型 - 迭代无迹卡尔曼滤波器以及交互多模型 - 联合估计无迹卡尔曼滤波器进行比较。结果表明，JE - AUKF在非合作机动目标的跟踪性能方面表现更优。
