不确定海洋环境中的模态子空间重构稳健定位方法

实际的海洋是一个不确定的声传播环境,常规的匹配场方法在进行目标定位时会遇到环境失配的问题,导致定位性能下降.在不确定的海洋环境中,声场传播中的一部分简正波模态受到声场不确定性的影响较小.基于此,本文提出了一种模态子空间重构的稳健定位方法.该方法使用稳定的模态来重构拷贝场向量,相比于常规匹配场定位方法中使用全阶模态来构造拷贝场向量,其定位结果更加稳健.利用计算机仿真数据和海试数据进行了定位性能分析,并给出了常规匹配场定位方法和稳健最大似然定位方法作为对比.研究结果表明:1)不确定海洋环境中,常规匹配场定位方法即使在较高的信噪比条件下其定位性能也较差.2)模态子空间重构定位方法的性能优于常规匹配场定位方法和稳健最大似然方法.

Robust source localization based on mode subspace reconstruction in uncertain shallow ocean environment

Existing localization methods have mismatch problem when applied to the real uncertain ocean, and this will lead to performance degradation. In normal mode models, some modal eigenfunctions remain to be more correlated than others in the presence of environmental uncertainties. Based on this, we have proposed a mode subspace reconstruction robust localization method, which uses stable modes to reconstruct the replica vector to grantee the localization performance. The data from simulation and experiment are used to verify the effectiveness of the proposed method. Performances of the matched field processor (MFP) and the robust ML (maximum localization) estimator are also given here for comparison. Results show that: (1) the generally used MFP method has a low localization performance even at high SNR values; (2) the proposed method outperforms the robust ML estimator and the generally used MFP.