摘要
针对传统的高斯混合概率假设密度(GM-PHD)滤波器在噪声先验特性未知或不准确时跟踪性能会下降,提出了一种基于噪声方差估计的高斯混合概率假设密度(NCE-GM-PHD)滤波算法.该算法可以同时在线估计时变的目标个数、多目标状态以及噪声方差.首先,通过引入遗忘因子和采取有偏估计的方法改进了传统的Sage-Husa自适应滤波器.基于改进的自适应滤波器,推导了带噪声方差估计的GM-PHD滤波算法.仿真结果表明,在非时变或时变量测噪声方差未知的情况下,NCE-GM-PHD算法的跟踪性能优于传统的GM-PHD算法,对噪声变化的适应能力更强.
When the prior noise statistics was unknown or inaccurate, the conventional Gaussian mixture probability hypothesis density （GM-PHD） filter declined in tracking performance. To solve this problem, a noise covariance estimation based GM-PHD （NCE-GM-PHD） filter was proposed for jointly estimating the time-varying number of targets, multi-target states and noise covariance. First, the adaptive filter of Sage and Husa was modified by introducing a forgetting factor and using the biased estimation. Based on the modified adaptive filter, the noise estimation method was adopted to derive a closed-form solution for NCE-GM-PHD filter. Simulation results demonstrate that the NCE-GM-PHD filter has a favorable per- formance and adaptability of noise changes compared to the classical GM-PHD filter with unknown time-in- variant or time-varying measurement noise statistics.
出处
《上海交通大学学报》
EI
CAS
CSCD
北大核心
2016年第9期1355-1361,共7页
Journal of Shanghai Jiaotong University
基金
国家自然科学基金资助项目(61175028)
关键词
高斯混合概率假设密度滤波器
多目标跟踪
噪声方差估计
自适应滤波器
Gaussian mixture probability hypothesis density （GM-PHD） filter
multi-target tracking
noise covariance estimation
adaptive filter
