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.