基于容积粒子滤波的时延差定位估计算法

在基于粒子滤波的时延差定位估计方法中, 重要密度函数的选取将直接影响估计的性能, 为此, 提出了基 于容积粒子滤波的时延差估计(BCPF-TDE) 算法. 该算法利用最新的数据检测信息, 通过容积卡尔曼滤波(CKF) 获 取粒子滤波的重要性密度函数. 仿真实验表明, 在粒子数目相同的情况下, 基于容积粒子滤波的时延差估计(BCPF- TDE) 方法与基于扩展粒子滤波的时延差估计(BEPF-TDE) 方法相比, 定位估计误差只有后者的50% 左右, 而运行时 间相当.

Time delay difference localization estimation algorithm based on cubature particle filter

In the time delay difference localization estimation methods based on the particle filter, the importance density function is the key to the performance of time delay difference estimation. A time delay difference estimation method based on cubature particle filter(BCPF-TDE) is proposed, which uses the latest measurements to generate the importance density function through cubature Kalman filter(CKF). The simulation results show that when the particle number is the same, compared with the time delay difference estimation based on extended particle filter(BEPF-TDE), the localization error of BCPF-TDE is about 50% of BEPF-TDE’s, but the run time of the time delay difference estimation based on cubature particle filter(BCPF-TDE) is close to that based on BEPF-TDE.