Sidelobe suppression for cosine-sum window functions via chaotic particle swarm optimization

Sidelobe suppression is a common and critical issue in radar pulse compression processing. However, the traditional window functions such as Hamming and Hanning windows are all limited to fixed weight parameters, and can only be applied to a specific application. Thus, we propose a flexible window function design algorithm based on chaotic particle swarm optimization (W-CPSO). First, a polynomial representation of the cosine-sum window functions is derived using Taylor series expansion. Then, the CPSO-based optimization algorithm is proposed to improve the sidelobe suppression performance of window functions by optimizing the polynomial coefficients. The proposed algorithm has been applied to optimize several widely used windows, including the Hamming, Hann, Blackman windows, etc. Analysis results demonstrate that the optimized window functions significantly improve sidelobe suppression while maintaining comparable mainlobe width. Furthermore, the simulation results of multi-target detection in radar systems also validate the effectiveness of the proposed algorithm, showing that the optimized windows exhibit superior performance in detecting weak targets compared to the traditional windows.