基于梯度粒子群的车载雷达图像畸变校正方法  

A distortion correction method of vehicle radar image based on gradient particle swarm

作　　者：李星军 蒋燕翔 邵志伟 LI Xingjun;JIANG Yanxiang;SHAO Zhiwei(Eurasia University,Xi’an 710065,China;Hainan University,Haikou 570028,China)

机构地区：[1]西安欧亚学院,西安710065 [2]海南大学,海口570028

出　　处：《激光杂志》2023年第10期79-83,共5页Laser Journal

基　　金：陕西省科技厅项目(No.2022JM-055)。

摘　　要：受车载雷达斜视成像的性质及地形起伏的影响,车载雷达图像的几何畸变较为复杂,正射校正结果不够理想,因此,提出基于梯度粒子群的车载雷达图像畸变校正方法。采取多尺度非均匀滤波对车载雷达图像展开降噪处理,利用维纳滤波算法初步复原处理车载雷达图像,通过梯度粒子群优化算法选取适应度函数,对车载雷达图像展开增益误差复原处理,构建卡尔曼滤波模型,将复原后的车载雷达图像输入滤波模型中,完成图像畸变矫正处理。实验结果表明,所提方法图像畸变程度分析准确,图像清晰度平均为0.8,图像校正定量评价因子最高达到了26%。Affected by the squint imaging nature and terrain fluctuation of vehicle-borne radar,the geometric distortion of vehicle-borne radar image is complex,and the orthorectification result is not ideal.Therefore,a method of vehicle-borne radar image distortion correction based on gradient particle swarm optimization is proposed.Multi-scale non-uniform filtering is used to denoise the vehicle-borne radar image,Wiener filtering algorithm is used to restore the vehicle-borne radar image,gradient particle swarm optimization algorithm is used to select the fitness function,and the gain error of the vehicle-borne radar image is restored,and a Kalman filtering model is constructed.The restored vehicle-borne radar image is input into the filtering model to complete the image distortion correction.The experimental results show that the proposed method can accurately analyze the degree of image distortion,with the average image definition of 0.8 and the highest quantitative evaluation factor of image correction of 26%.

关 键 词：车载雷达图像 多尺度非均匀滤波 图像畸变校正 梯度粒子群 

分 类 号：TN249[电子电信—物理电子学]