带旁瓣抑制的稳健匹配场定位算法

Robust Matched Field Localize Algorithm with Sidelobe Suppression

在特征值空间中,利用接收数据协方差阵的主特征向量,与扰动环境参数生成的拷贝场向量协方差阵的主特征向量,构造了一种巴特利特型的匹配场定位算法,它对环境参数失配稳健,但是带有很高的旁瓣级。引入一种约束优化机制,以最大化目标区域的空间平均的输出功率与搜索区域空间平均的功率比为目标,得到一个经过优化的观测数据向量。使用数据向量对定位表面的旁瓣进行约束抑制,在保持目标区域输出功率的同时,有效降低搜索区域的背景功率。实测数据分析表明,该算法能有效抑制旁瓣,提高定位性能。

In a eigenvalue space, the Bartlett matched field localize algorithm is developed by using a main eig- envector of a measure data covariance matrix and the main eigenveetor of a replica vector covariance matrix produced by modeling a perturbing environmental parameters. This algorithm is robust to environmental parameters mismatch, but the sidelobe level is very high. The contrast - maximized optimization scheme is presented, which enhances the contrast between the acoustic power output of the source region and the power output of the total region of interest. The optimization observation vector is obtained and an ambiguity surface sidelobe of the Bartlett localize is suppressed with this optimization data vector. A proposed algorithm is applied to a real ocean data and the result shows that sidelobe level is reduced about 4 dB.