Zhu Guolei, Wang Yingmin, Wang Qi
School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an 710072, China.
Sensors (Basel). 2020 Mar 2;20(5):1374. doi: 10.3390/s20051374.
In order to improve the robustness and positioning accuracy of the matched field processing (MFP) in underwater acoustic systems, we propose a conditional probability constraint matched field processing (MFP-CPC) algorithm in this paper, which protects the main-lobe and suppresses the side-lobe to the AMFP by the constraint parameters, such as the posterior probability density of source locations obtained by Bayesian criterion under the assumption of white Gaussian noise. Under such constraint, the proposed MFP-CPC algorithm not only has the same merit of a high resolution as AMFP but also improves the robustness. To evaluate the algorithm, the simulated and experimental data in an uncertain shallow ocean environment is used. From the results, MFP-CPC is robust to the moored source, as well as the moving source. In addition, the localization and tracking performances of using the proposed algorithm are consistent with the trajectory of the moving source.
为了提高水声系统中匹配场处理（MFP）的稳健性和定位精度，本文提出了一种条件概率约束匹配场处理（MFP-CPC）算法，该算法通过约束参数（如在白高斯噪声假设下由贝叶斯准则获得的源位置的后验概率密度）来保护主瓣并抑制旁瓣至AMFP。在这种约束下，所提出的MFP-CPC算法不仅具有与AMFP相同的高分辨率优点，而且提高了稳健性。为了评估该算法，使用了不确定浅海环境中的模拟和实验数据。结果表明，MFP-CPC对锚定源以及移动源都具有稳健性。此外，使用该算法的定位和跟踪性能与移动源的轨迹一致。
