摘要
针对粒子滤波跟踪算法中重采样所引起的粒子多样性缺失问题,提出了一种自适应粒子滤波重采样方法。首先将粒子权值进行分类,中等权值粒子保持不变,大、小权值粒子进行权值优化组合,其次对优化组合后的小部分粒子进行系统重采样。最后对粒子的权值及粒子的复制次数分别进行比较运算。试验结果表明,改进后的粒子滤波算法不仅提高了机动目标跟踪的运算效率,而且还有效提高了跟踪的稳定性,目标跟踪更加准确。
To solve the problem of particle lack of diversity in particle filter resampling track algorithm, a particle filter algorithm based on adaptive sampling methods is presented. First, the particle weight classification, medium weight particles remain unchanged, large and small particles weights by optimized combination of weights, followed a small part of the optimization particle by system resampling. Finally, the copy number of particles in the right amount and particle compare operations. The test results show that the improved particle filter algorithm not only improves the operational efficiency of maneuvering target tracking, but also improves the stability of tracking, targeting more accurate.
出处
《光电工程》
CAS
CSCD
北大核心
2014年第4期35-40,共6页
Opto-Electronic Engineering
基金
国家自然科学基金项目(61272043)
应急通信重庆市重点实验室开放课题(CQKLEC
20120504)
关键词
粒子滤波
重采样
权值
优化组合
粒子多样性
particle filter
resampling
weight
optimal combination
diversity among the particles
