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基于箱式粒子滤波的群目标跟踪算法

DOI: 10.16383/j.aas.2015.c140222, PP. 785-798

Keywords: 群目标,跟踪,箱式粒子滤波,广义似然函数,演化网络模型,区间分析,峰值误差

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Abstract:

?在现有群目标跟踪方法中,粒子滤波(Particlefilter,PF)算法常被用来解决点量测的非线性滤波问题.而当量测数据受到测量偏差或未知分布边界误差的影响时,传感器获得的点量测需要转换成区间量测,此时原有PF算法不能直接适用.因此,本文提出基于广义似然(Generalizedlikelihood,GL)函数加权的PF算法.该算法在原有PF算法的基础上,利用广义似然函数的积分解来计算区间量测下的粒子权重.为了降低算法的运算量问题,又提出基于箱式粒子滤波(Boxparticlefilter,Box-PF)的群跟踪算法.首先,在目标状态空间内抽样矩形区域的箱式粒子.然后采用区间分析和约束传播方法,利用区间量测压缩后的粒子与预测粒子的容积比来计算粒子权重.最后,在群目标状态估计结果和群演化网络模型的基础上估计群结构.仿真实验结果表明,与GL-PF算法相比,Box-PF算法具有更高的运算效率,并能降低估计结果中的峰值误差.

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