摘要
针对混合线性/非线性模型,提出一种新的递推估计滤波算法,称为准高斯Rao-Blackwellized粒子滤波器(Q-GRBPF)。算法采用Rao-Blackwellized思想,将线性状态与非线性状态进行分离,对非线性状态运用准高斯粒子滤波(Q-GPF)算法进行估计,并将其后验分布近似为单个高斯分布,再利用非线性状态的估计值对线性状态进行卡尔曼滤波(KF)估计。将Q-GRBPF应用于目标跟踪的仿真结果表明,与Rao-Blackwellized粒子滤波器(RBPF)相比,Q-GRBPF在保证估计精度的前提下有效降低了计算复杂度,计算时间约为RBPF的58%;与Q-GPF相比,x坐标与y坐标的估计精度分别提升了45%和30%,而计算时间也节省了约30%。
A new recursive estimation algorithm, called the quasi-Gaussian Rao-Blackwellized particle filter （Q- GRBPF）, is proposed for filtering mixed linear/nonlinear state space models. The algorithm utilizes the idea of Rao-Blackwellized to separate the linear and nonlinear states. For the nonlinear states, the posterior distribu- tions of the estimates, which are achieved by the quasi-Gaussian particle filter （Q-GPF）, are approximated as Gaussian distributions. Also, the linear states are estimated by the Kalman filter（KF） with the estimated non- linear states. The simulation results of the proposed method applying to target tracking show that the proposed method only consumes 58% of the computing time required by the RBPF. Furthermore, compared with Q-GPF, the tracking accuracies of the proposed method for estimating x and y coordinate locations of the tracked target are respectively increased by 45% and 30% while 30% computing time is saved.
出处
《航空学报》
EI
CAS
CSCD
北大核心
2008年第2期450-455,共6页
Acta Aeronautica et Astronautica Sinica
基金
国家自然科学基金(60572023)
