环境扰动约束下的稀疏贝叶斯定位方法

针对匹配场被动定位技术对海洋环境扰动敏感的问题,提出了一种基于稀疏贝叶斯学习的稳健匹配场处理方法。该方法通过分析环境扰动情况下的声场构成,建立了海洋环境扰动模型,同时结合水下定位问题的稀疏性,将匹配场被动定位问题表述为稀疏信号重构问题。然后,使用稀疏贝叶斯学习方法迭代更新目标位置及模型失配权向量,收敛至最优稀疏解作为目标定位结果。最后使用仿真数据和北厄尔巴岛的海试数据对算法进行了验证,仿真和实验结果表明:该算法在海洋环境模型失配情况下也能够准确定位,且能分辨水平间距为100 m的两个目标。因此,基于稀疏贝叶斯学习的稳健匹配场处理方法能够有效利用海洋环境扰动声场结构和水下定位稀疏特性,以增强匹配场处理的稳健性和定位精度,并且具有应对多源定位问题的能力。 

Sparse Bayesian learning localization method with environmental perturbation constrains

Matched-Field Processing (MFP) passive localization method suffers from its sensitivity to the ocean environmental perturbations.A method named Sparse Bayesian Learning (SBL) for robust MFP (MFP-SBL) is proposed for source localization in an uncertain shallow water waveguide.By exploiting the acoustic field structure using the normal mode model with the existence of environmental mismatch,an ocean environmental perturbation model is established.Then,we reformulate the MFP localization as a linear sparse signal recovery problem to exploit the inherent sparsity of underwater localization problem.The proposed algorithm iteratively updates the target location and the model mismatch weights via performing Bayesian inference and convergences to the optimal sparse solution as the estimated location.Simulations and sea trial data in north Elba island are used to evaluate the performance of the proposed MFPSBL algorithm.It demonstrates that MFP-SBL can estimate the target location accurately even with the existence of environmental mismatch,and can distinguish two targets with a horizontal space of 100 m.Therefore,the proposed scheme can enhance the robustness and positioning accuracy of MFP via exploiting the acoustic field structure under ocean environmental mismatch and the inherent sparsity of underwater localization problem.Also,it can deal with the multi-source positioning problem.