基于环境扰动的线性匹配场处理方法

匹配场处理面临的挑战主要是拷贝场失配。常规线性匹配场处理方法虽然对失配较宽容,但旁瓣较高,定位正确率依赖于失配的程度,且很容易受到强干扰的影响而失效。自适应匹配场处理可以获得较高的定位精度和旁瓣抑制性能,但环境参数失配、距离/深度空间欠采样、基阵及环境的波动所造成的误差等因素,均会导致其性能严重下降。环境扰动约束可提高自适应匹配场处理器的稳健性,基于这种思想,提出了基于环境扰动的线性匹配场处理方法,它不仅在环境失配的条件下比常规线性方法的定位正确率更高,而且在阵列采样数据存在幅度与相位随机误差的条件下,比自适应匹配场方法的稳健性更强.针对环境失配条件下的强干扰问题,还提出了一种线性匹配场干扰抑制算法,可有效地抑制水面干扰。典型浅海环境下的数值仿真和实测数据分析验证了方法的有效性.

Linear matched field processing based on environmental perturbation

The mismatch of replica field is the main challenge for matched field processing. Conventional linear matched field method is more tolerable to mismatch, but its sidelobes are high and its probability of correct localization depends on the degree of mismatch. Furthermore, linear matched field method will often be invalid to detect weak target if there is a strong interference. Adaptive matched field processing can achieve higher localization accuracy and suppress sidelobes more effectively, but its performance will degrade significantly due to environmental mismatch, undersampling of range-depth, and random data errors. Based on the idea that adding environmentally perturbed constraint can improve the robustness of adaptive matched field processing, this paper presents a new linear matched field algorithm with environmental perturbation. This algorithm can achieve higher probability of correct localization than conventional Bartlett processor with environmental mismatch. Moreover, it may obtain more robust performance than adaptive matched field processing with random amplitude and phase errors included in data. Furthermore, this paper presents a linear matched field algorithm to null interference. The robustness and effectiveness of the suggested algorithm has been illustrated through the numerical simulation and the experimental Mediterranean benchmark shallow-water data.