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This paper studies the method for sidelobe suppression of difference pattern at subarray level in phased array. Sidelobe suppression of difference pattern by digital weighting at subarray level takes advantage that the hardware cost and complexity can be reduced effectively. The optimization method for subarray weights based on genetic algorithm is presented. In order to avoid being constrained on local optimal solution of genetic operation, we partition the genetic optimization process into two stages. Bayliss weight is adopted as objective function for the first stage and parameters of pattern for the second one. Compared with conventional GA, the improved GA achieves better sidelobe suppression performance. The simulation results verify the validity of the proposed method.

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In phased array at subarray level with monopulse technology, generally, analog weighting at element level are adopted for sidelobe suppression of patterns, while digital weighting at subarray level for adaptive processing. This paper studies the sidelobe suppression method for sum and difference beam by using only subarray level weighting instead of analog weighting, which is most of interest for the reduction of hardware cost and complexity. A new approach is presented, which adopts genetic algorithm (GA) to optimize weights vector at subarray level for sum and difference beam, and the encoding scheme, constitution of fitness function, and realization of genetic optimization are also given. Simulation results demonstrate that the proposed method can suppress sidelobe of patterns effectively, especially for sum beam.