摘要
基于概率假设密度滤波(PHD)的检测前跟踪(TBD)技术可以有效解决未知弱小多目标检测问题。PHD-TBD算法粒子权重计算受量测噪声影响明显,导致目标数估计存在起伏现象,制约了PHD-TBD算法性能。对PHD-TBD技术进行研究,引进概率假设密度滤波平滑器,提出基于平滑的PHD-TBD算法。该算法对当前帧目标数估计时,综合利用前向递推和后向平滑结果对粒子权重进行更新,在一定程度上克服了随机量测噪声的影响。通过仿真验证,该算法能够有效发现目标,准确估计目标数目和位置,性能有较大提高。
Track-before-detect （TBD） technology based on the probability hypothesis density （PHD） filter can effectively solve the problem of tracking number-varying dim multi-target. The main limitation of the standard PHD- TBD algorithm is the estimation error of target numbers influenced by the measurement noise remarkably. An improved PHD-TBD algorithm based on the smooth is proposed. The algorithm can overcome the influence of noise in a certain extent by updating the weight of particle using forward recursion and backward smooth, and then a steady estimation of target numbers is obtained. In addition, the simulation results demonstrate that the proposed algorithm can effectively and stably estimate the number of targets and their positions comparing with the standard one.
出处
《光学学报》
EI
CAS
CSCD
北大核心
2012年第10期124-131,共8页
Acta Optica Sinica
基金
武器装备预研基金(9140A21041110KG0148)资助课题
关键词
传感器
检测前跟踪
概率假设密度滤波
序列蒙特卡罗方法
粒子平滑
sensors
track-before-detect
probability hypothesis density filter
sequential Monte Carlo
particle smooth
