基于稀疏贝叶斯学习的水下电场测向方法

Underwater Electric Field Direction Finding Method Based on Sparse Bayesian Learning

摘要:

水下电场是一种可用于对水下目标进行探测和识别的重要物理场，通过高灵敏度、低自噪声的水下电场探测系统，可实现对水下目标电场信号的远程测向。针对水下目标测向问题，提出了一种基于稀疏贝叶斯学习的水下电场测向方法。该方法采用多个探测单元同时采集处理水下目标辐射的交变电场信号，再通过稀疏贝叶斯学习方法，实现对目标电场信号波达方向的估计，最终估计出目标与探测系统的相对方位。通过湖上试验，验证了该方法的可行性与鲁棒性。相对于常规波束形成算法，在一定测试场景下，该方法对16Hz频率的正弦波电场信号的测向精度提高了4.8°。

Abstract:

Underwater electric field is an important physical field that can be used for underwater target detection and recognition. Through a high sensitivity and low self-noise underwater electric field detection system, remote direction finding of underwater targets can be achieved. Aiming at the underwater target direction finding problem, an underwater electric field direction finding method based on sparse Bayesian learning is proposed. The method uses multiple detection arrays to simultaneously collect and process the alternating electric field signal of the underwater target. Then, it estimates the signal direction of arrival(DOA) by the sparse Bayesian learning(SBL) algorithm. Finally, it estimates the relative orientation between the target and the detection system. The feasibility and robustness of the method were verified through the lake test. Compared with the conventional beamforming direction finding method, the direction finding accuracy of the sine wave electric field signal at 16Hz frequency was improved by 4.8° under certain test scene.