基于特征提取的匹配场处理

Matched Field Processing Based on Feature Extraction

匹配场被动定位具有优于传统被动定位算法的性能,但其对环境参数具有依赖性的缺点严重制约了算法的应用。为了改善算法的适应性,在充分利用拷贝场协方差矩阵信号分量和噪声分量的基础上,构造了两种优化的特征提取匹配场处理器。最后使用1993年NATO SCALANT Centre在地中海用垂线阵采集的实验数据对算法进行了验证,结果显示在不确实海洋环境下,算法不仅可以成功定位固定声源和运动声源,而且其输出信干比和峰值背景比都得到了改善。

The matched field processing on passive localization is prior to the classical passive source localization algorithm,but the application of the algorithm which has disadvantages of the environmental dependence is severely restricted. In order to improve its adaption to the environment, two optimized feature extraction matched field processors are proposed by using the signal vectors and noise vectors of the replica covariance matrixes sufficiently. In the end,the vertical array data from the mediterranean sea collected by the NATO SCALANT Centre is used to evaluate the algorithms. The results show that the two algorithms can not only localize the fixed source and the moving source successfully,but also improve the output Signal to Interference plus Noise Ratio（SINR）and the Peak to Background Ratio（PBR）in uncertain environments.