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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。
In order to improve the detection effect of infrared weak targets, an improved particle swarm optimization algorithm is proposed. Firstly, the particle swarm optimization algorithm is optimized based on the attractor of Gaussian distribution, and the particle swarm optimization is optimized by logistic chaos to avoid falling into the local optimum in the late evolutionary stage. Secondly, the diversity of the deterministic Euclidean distance of particles is used to ensure the later chaos The quantum behavior of particle swarm optimization algorithm is carried out reliably; Finally, the small infrared target is detected under the minimum mean square error criterion, and the prediction weight is modified to ensure the validity of the detection. Experimental results show that the proposed algorithm has a clear detection effect on small infrared targets and a maximum signal-to-noise ratio, and the detection probability and false alarm probability of the algorithm are better.