激光与光电子学进展, 2017, 54 (11): 111101, 网络出版: 2017-11-17
基于改进粒子算法的红外弱小目标检测研究 下载: 509次
Infrared Dim Target Detection Based on Improved Particle Swarm Optimization Algorithm
成像系统 量子行为粒子群优化算法 混沌 红外 弱小目标 imaging systems quantum-behaved particle swarm optimization algori chaos infrared dim target
摘要
为了提高红外弱小目标的检测效果,提出了一种改进粒子群算法。首先基于高斯分布吸引因子对量子行为粒子群算法进行优化,通过logistic混沌对粒子群映射寻优,避免了进化后期陷入局部最优;接着利用粒子群平均欧氏距离确定的多样性来保证后期混沌量子行为粒子群优化算法的可靠进行;最后在最小均方差准则下对红外弱小目标进行检测,修正预测权值,保证检测的有效性。实验仿真结果表明,本文算法对红外弱小目标的检测效果清晰,信噪比最大,算法的检测概率和虚警概率较好。
Abstract
In order to improve the detection effect of the infrared dim target, an improved particle swarm optimization algorithm is proposed. Firstly, a quantum-behaved particle swarm algorithm is optimized based on the Gaussian distribution attraction factor, and the particle swarm mapping is optimized by the logistic chaos which can avoid the later evolution into the local optimum. Secondly, the reliability of the chaotic quantum-behaved particle swarm optimization algorithm is ensured according to the diversity determined by the average Euclidean distance of the particle swarm in the later stage. Finally, the infrared dim target is detected under the minimum mean variance criterion, and the prediction value is corrected which can ensure the validity of the detection. The experimental results show that the proposed algorithm is effective in detecting infrared dim targets with the largest signal noise ratio value, and the detection probability and false alarm probability are better than other algorithms.
姚成乾, 陈伟. 基于改进粒子算法的红外弱小目标检测研究[J]. 激光与光电子学进展, 2017, 54(11): 111101. Yao Chengqian, Chen Wei. Infrared Dim Target Detection Based on Improved Particle Swarm Optimization Algorithm[J]. Laser & Optoelectronics Progress, 2017, 54(11): 111101.