Cao Huapeng, Yang Tingting, Yiu Ka-Fai Cedric
School of Navigation, Dalian Maritime University, Dalian 116026, China.
Peng Cheng Laboratory, Shenzhen 518066, China.
Sensors (Basel). 2025 Aug 1;25(15):4762. doi: 10.3390/s25154762.
In this paper, a joint Matching Field Processing (MFP) Algorithm based on horizontal uniform circular array (UCA) is proposed for three-dimensional position of underwater wrecked targets. Firstly, a Marine search and rescue position model based on Minimum Variance Distortionless Response (MVDR) and matching field quadratic joint Algorithm was proposed. Secondly, an MVDR beamforming method based on pre-Kalman filtering is designed to refine the real-time DOA estimation of the desired signal and the interference source, and the sound source azimuth is determined for prepositioning. The antenna array weights are dynamically adjusted according to the filtered DOA information. Finally, the Adaptive Matching Field Algorithm (AMFP) used the DOA information to calculate the range and depth of the lost target, and obtained the range and depth estimates. Thus, the 3D position of the lost underwater target is jointly estimated. This method alleviates the angle ambiguity problem and does not require a computationally intensive 2D spectral search. The simulation results show that the proposed method can better realise underwater three-dimensional positioning under certain signal-to-noise ratio conditions. When there is no error in the sensor coordinates, the positioning error is smaller than that of the baseline method as the SNR increases. When the SNR is 0 dB, with the increase in the sensor coordinate error, the target location error increases but is smaller than the error amplitude of the benchmark Algorithm. The experimental results verify the robustness of the proposed framework in the hierarchical ocean environment, which provides a practical basis for the deployment of rapid response underwater positioning systems in maritime search and rescue scenarios.
本文提出了一种基于水平均匀圆阵（UCA）的联合匹配场处理（MFP）算法，用于水下失事目标的三维定位。首先，提出了一种基于最小方差无失真响应（MVDR）和匹配场二次联合算法的海洋搜索救援定位模型。其次，设计了一种基于预卡尔曼滤波的MVDR波束形成方法，用于细化期望信号和干扰源的实时波达方向（DOA）估计，并确定声源方位进行预定位。根据滤波后的DOA信息动态调整天线阵列权重。最后，自适应匹配场算法（AMFP）利用DOA信息计算丢失目标的距离和深度，并获得距离和深度估计。从而联合估计出丢失水下目标的三维位置。该方法缓解了角度模糊问题，且不需要进行计算量大的二维谱搜索。仿真结果表明，该方法在一定信噪比条件下能较好地实现水下三维定位。当传感器坐标无误差时，随着信噪比的增加，定位误差比基线方法小。当信噪比为0 dB时，随着传感器坐标误差的增加，目标定位误差增大，但小于基准算法的误差幅度。实验结果验证了所提框架在分层海洋环境中的鲁棒性，为海上搜索救援场景中快速响应水下定位系统的部署提供了实际依据。
