摘要
基于粒子滤波的检测前跟踪(TBD)算法是检测微弱目标的有效手段,但现有粒子滤波方法在进行微弱目标检测时,通常是在已知粒子初始参数范围的条件下进行研究的,没有考虑粒子初始参数对算法性能的影响。在对粒子状态预测过程中涉及的参数进行理论分析的基础上,发现粒子初始速度范围和初始状态范围两种参数对算法性能有较大影响。通过大量实验证明粒子初始参数的优化对算法的检测能力、跟踪精度和时间复杂度3方面性能都有所改善,为研究利用基于粒子滤波的TBD微弱目标检测提供新的思路。
The particle filter based Track-Before-Detect（TBD） algorithm is an effective approach to detect dim targets.However,current researches commonly proceed without considering the effect of particle initialization.Through theoretical analysis to the parameters involved in the particle predict process,it is discovered that the initial velocity range and position range of particles have great effect on the particle filter based TBD algorithm.Experiments proved that the optimization of initial particle parameters can improve the performance of the algorithm in its detection capability,tracking accuracy and computation complexity.This supplies a new idea for study on dim target detection.
出处
《电光与控制》
北大核心
2010年第6期15-20,24,共7页
Electronics Optics & Control
基金
全国优秀博士学位论文作者专项基金资助项目(20443)
"泰山学者"建设工程专项经费资助项目
关键词
TBD
粒子初始化
参数优化
检测能力
跟踪精度
时间复杂度
TBD
particle initialization
parameter optimization
detection ability
tracking accuracy
computation complexity
