Binary phase-coded radar waveform optimization based on improved hybrid quantum genetic algorithm

Abstract

To overcome the problem of single search focus and limited application scope of existing algorithm, a binary phase-coded radar waveform optimization (RWO) based on improved hybrid quantum genetic algorithm (IHQGA) was proposed.IHQGA adopted a novel self-adaptive rotation angle strategy, which dynamically adjusted the rotation angle based on evolutionary process and cosine similarity.The convergence speed, global search capability, and solution quality were improved.Simulation results demonstrate that compared with genetic algorithms, basic quantum genetic algorithms, and hybrid quantum genetic algorithms, IHQGA performs better in terms of solution quality and resource consumption for six benchmark functions that include single-peak, multi-peak, and non-convex optimization problems.Additionally, for binary phase-coded RWO, which verifies the feasibility and effectiveness of IHQGA in WO.

Keywords

• phase coding
• radar waveform
• QGA
• self-adaptive rotating angle strategy
• PSLL