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Sparsity and Adaptivity for Sensor Location Optimization of Underwater Conformal Vector Array

In this paper, a sparse array signal processing algorithm using iterative optimization strategy is proposed to select an optimal sparse-aperiodic subarray in an existing fixed array. The algorithm is based on an Improved Genetic Algorithm (IGA) that simultaneously adjusts the inter-senor spacing of a conformal array with arbitrary geometrical configuration. A novel crossover method is developed to improve the convergence performance. The proposed method can achieve lower Peak Sidelobe Level (PSL) using fewer sensors and thereby reduce redundancy. The robustness of the algorithm is also tested using multiple independent simulations.