摘要
对于水下主动探测,目标与声呐系统的相对位姿关系对目标识别与跟踪非常重要。现有基于波形字典的水下目标位姿估计方法,难以在低信噪比下精确估计目标位姿。鉴于互相关处理在目标回波检测中的抗噪性,提出了一种基于互相关字典的方法,通过构造互相关字典并结合正交匹配追踪(OMP)算法估计目标位姿。利用亮点模型生成不同水平位姿角的回波信号,进而构造互相关字典并分析不同系统参数对字典相关性能的影响。水池实验表明,基于互相关字典的目标位姿估计方法低信噪比(小于-12 dB)时,估计正确率优于波形字典;通过增加带宽,在少量阵元条件下仍可在低信噪比下实现准确估计。结合使用HFM信号,基于互相关字典的水下目标位姿估计方法可用于运动弱目标的位姿估计。
For underwater active detection,the relative position relationship between a target and a sonar system is very important for identifying and tracking the target.It is difficult for the existing underwater target position estimation method based on the waveform dictionary to accurately estimate the target position under at low signal-to-noise ratios.Because the cross-correlation processing has noise immunity in target echo detection,this paper proposes a target position estimation method based on the cross-correlation dictionary.It constructs a cross-correlation dictionary and combines it with the orthogonal matching pursuit(OMP)algorithm.To construct the cross-correlation dictionary,the echo highlight model is used to generate echo signals with different horizontal position angles.The effects of different system parameters on the correlation performance of the dictionary are analysed.Pool experimental results show that the target position estimation method based on the cross-correlation dictionary outperforms the waveform dictionary at low signal-to-noise ratios(less than-12 dB)and can still carry out the accurate estimation at low signal-to-noise ratios with a small number of array elements by increasing the bandwidth.Jointly used with HFM signals,the underwater target position estimation method based on the cross-correlation dictionary can be used to estimate the position of a slow target.
作者
杨长生
高斐宇
卫艳
娄紫洋
YANG Changsheng;GAO Feiyu;WEI Yan;LOU Ziyang(School of Marine Science and Technology,Northwestern Polytechnical University,Xi'an 710072,China)
出处
《西北工业大学学报》
北大核心
2025年第4期677-684,共8页
Journal of Northwestern Polytechnical University
基金
国家自然科学基金(61771398)资助。
关键词
目标位姿估计
互相关字典
回波亮点模型
稀疏表示
target position recognition
cross-correlation dictionary
echo highlight model
sparse representation
