高斯过程泊松多伯努利混合滤波算法及其变分优化
Gaussian process Poisson multi-Bernoulli mixture filtering and its variational optimization
李翠芸 1许琦 1姬红兵 1谢金池1
作者信息
- 1. 西安电子科技大学电子工程学院,陕西西安 710071
- 折叠
摘要
针对现有算法对多扩展目标跟踪精度低的问题,本文提出了一种高斯过程泊松多伯努利混合(GP-PMBM)滤波算法及其变分优化.首先,基于高斯过程原理建立了增广状态空间模型,接着,将其与泊松多伯努利混合滤波器相结合,提出GP-PMBM算法.然后,针对因使用非线性滤波技术而导致GP-PMBM滤波精度下降的问题,使用变分贝叶斯优化更新结果,实现了对目标状态的优化更新,提升了滤波器的估计精度.仿真结果表明,与已有的滤波算法相比,所提算法具有更高的跟踪精度,并且,在只有部分量测的场景中跟踪性能更稳定.
Abstract
In response to the problem of low tracking accuracy for multi-object tracking in existing algorithms,a Gaus-sian process Poisson multi-Bernoulli mixture(GP-PMBM)filtering algorithm and its variational optimization are proposed.Firstly,an augmented state space model is established based on the principles of Gaussian processes.Subsequently,to ad-dress the issue of decreased filtering accuracy in GP-PMBM caused by the use of nonlinear filtering techniques,variable Bayesian optimization is utilized to update the results,achieving optimized updates of the target states and enhancing the estimation accuracy of the filter.Simulation results demonstrate that the proposed algorithm has higher tracking accura-cy compared to existing filtering algorithms and exhibits more stable tracking performance in scenarios with only partial measurements.
关键词
目标跟踪/泊松多伯努利混合滤波/高斯过程/变分贝叶斯优化Key words
target tracking/Poisson multi-Bernoulli mixture filtering/Gaussian process/variable Bayesian optimization引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据