Spatial filter design based on compressed replica vectors

Matched field processing techniques have been studied extensively in recent decades, and a lot of detail algorithms have been put forward for practical use. When the underwater target is obscured by the strong surface interferences, the performance of matched field processing localization will degrade severely. The now existing spatial filter technique can be used to suppress the surface interferences, but the burden of calculation is heavy and the memory usage is large. In this paper, a scheme of optimizing spatial filter design based on the compressed replica vectors is presented, and the broadband data are processed incoherently. In contrary to the existing spatial filter, the optimized spatial filter can effectively reduce the computational complexity and memory usage when the number of array elements N is greater than the number of the effective modes Q, meanwhile, it also retains the original performance of interference suppression. Numerical simulations in a littoral shallow water environment are performed to validate the performance of the spatial filter and the promotion of computation speed. Then, data processing results obtained from an experiment conducted in the littoral shallow water environment are presented. It follows from the results that the weak underwater target can be distinguished from the strong surface interference clearly by use of the incoherent matched field processing with the application of the spatial filter based on compressed replica vectors.