单站水下方位频率机动目标运动分析方法  

A single-platform underwater maneuvering target motion analysis method based on bearing and frequency measurements

作　　者：孙大军 张艺翱[1,2,3] 滕婷婷 胡哲健 SUN Dajun;ZHANG Yiao;TENG Tingting;HU Zhejian(National Key Laboratory of Underwater Acoustic Technology,Harbin Engineering University,Harbin,150001;Key Laboratory of Marine Information Acquisition and Security(Harbin Engineering University),Ministry of Industry and Information Technology,Harbin,150001;College of Underwater Acoustic Engineering,Harbin Engineering University,Harbin,150001)

机构地区：[1]哈尔滨工程大学水声技术全国重点实验室,哈尔滨150001 [2]工业和信息化部海洋信息获取与安全工信部重点实验室(哈尔滨工程大学),哈尔滨150001 [3]哈尔滨工程大学水声工程学院,哈尔滨150001

出　　处：《声学学报》2024年第4期683-695,共13页Acta Acustica

摘　　要：水下方位频率机动目标运动分析常需已知中心频率先验信息且存在机动检测错误等,因此提出了一种基于方位与多线谱信息融合的自适应无迹卡尔曼滤波算法。该算法将方位与多线谱频率信息融合处理,重新构建了方位−多线谱目标运动分析模型。将中心频率信息引入状态向量中构建实时迭代估计,同时引入了自适应时变渐消因子,实时调整过程噪声协方差矩阵使其适应机动目标。仿真与海试数据处理结果表明,所提方法的机动目标运动分析性能最优,其目标运动分析结果能以最快的速度逼近克拉美罗下界。This paper is concerned with the underwater bearing and frequency maneuvering target motion analysis problem.To address the existing problems of requiring known prior center frequency information and the existence of maneuvering detection errors,an adaptive unscented Kalman filter algorithm based on multiple frequencies and bearing(MFB-AUKF)algorithm is presented.The MFB-AUKF algorithm constructs a new target motion analysis model by fusing bearing and multiple frequency information.The center frequencies are introduced into the state vector and the real-time estimation of the center frequencies can be obtained by iteration.The algorithm introduces a time-varying fading factor to adjust the process noise covariance matrix,which makes the MFB-AUKF algorithm capable of processing maneuvering targets.Simulation and sea trial analysis results show that the proposed method achieves better tracking performance and the target motion analysis result can approach the Cramer-Rao lower bound with the fastest speed.

关 键 词：目标运动分析 多线谱信息融合 机动目标跟踪 无迹卡尔曼滤波 渐消因子 

分 类 号：TB566[交通运输工程—水声工程]