铁道科学与工程学报2016，Vol.13Issue(7)：1393-1400,8.
改进的粒子滤波算法及其在车牌跟踪中的应用
I mproved particle filter algorith m and its application in target tracking
摘要
Abstract
To solve the problems of low accuracy,inefficiency and sample impoverishment of particle filter meth-od,the improved method combining quantum particle swarm optimization and adaptive genetic algorithm was pro-posed.After re-sampling,the particle distribution was improved by the position renewal equation of the quantum particle swarm optimization.Then the samples were sorted according to their fitness,and the particles with fitness values less than average fitness were filtered.Then optimal samples were selected to replace the abandoned ones and crossover,mutate with adaptive genetic algorithm,so as to ensure the sample validity and diversity.The modified algorithm was simulated in nonlinear target tracking model and time-constant value model and proved to be high in algorithm accuracy and numerical value stability.This method was also applied to the car tracking ex-periment and proved to be very efficient and accurate especially under the condition that the target moved fast and the intensity and background changed dramatically.关键词
粒子滤波/车牌跟踪/量子粒子群算法/自适应遗传算法Key words
particle filter/license plate tracking/quantum particle swarm optimization/adaptive genetic algo-rithm分类
信息技术与安全科学引用本文复制引用
肖宇麒,潘迪夫,韩锟..改进的粒子滤波算法及其在车牌跟踪中的应用[J].铁道科学与工程学报,2016,13(7):1393-1400,8.基金项目
国家自然科学基金资助项目 （）
