ISAR Imaging Algorithm Based on PSO Joint Motion Compensation
In traditional inverse synthetic aperture radar(ISAR)imaging,target motion compensation usually requires envelope a-lignment to achieve one-dimensional range profile alignment,followed by phase correction.This compensation method is called cascaded motion compensation.Cascade motion compensation under low signal-to-noise ratio can easily increase error accumula-tion,ultimately leading to image blurring and defocusing,seriously affecting imaging quality.To address this issue,this paper pro-poses an ISAR imaging algorithm based on particle swarm optimization(PSO)combined with motion compensation.This algorithm decomposes the motion model of the target,sets parameter vectors through high-order expansion,and uses image sharpness as the cost function to find the optimal value of vector parameters for joint motion compensation of the target,resulting in the best ISAR imaging effect.In the process of solving the optimal parameters,this article adopts the particle swarm optimization(PSO)algo-rithm,which achieves faster convergence during operation.This algorithm improves the solving speed of the model and obtains ISAR imaging with better focusing effect.This article verifies the effectiveness of the algorithm through simulation data and field measurement data.
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