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

Area of Science:

• Electrical Engineering
• Signal Processing
• Radar Systems

Background:

• Sidelobe suppression is a critical challenge in radar pulse compression.
• Traditional window functions (e.g., Hamming, Hanning) have fixed parameters, limiting their application flexibility.

Purpose of the Study:

• To develop a flexible window function design algorithm for improved sidelobe suppression.
• To enhance the performance of radar pulse compression processing, particularly for weak target detection.

Main Methods:

• Derived a polynomial representation of cosine-sum window functions using Taylor series expansion.
• Proposed a chaotic particle swarm optimization (CPSO) algorithm (W-CPSO) to optimize polynomial coefficients.
• Applied W-CPSO to optimize established window functions like Hamming, Hann, and Blackman.

Main Results:

• Optimized window functions demonstrated significant improvements in sidelobe suppression.
• Mainlobe width was maintained at comparable levels to traditional windows.
• Simulations confirmed superior performance in detecting weak targets in multi-target scenarios.

Conclusions:

• The proposed W-CPSO algorithm offers a flexible and effective approach to window function design for radar.
• Optimized windows significantly enhance radar system capabilities, especially in complex detection environments.