Abstract
In this paper, a new Gaussian approximate (GA) filter for stochastic dynamic systems with both one-step randomly delayed measurements and colored measurement noises is presented. For linear systems, a Kalman filter can be obtained to include one-step randomly delayed measurements and colored measurement noises. On the other hand, for nonlinear stochastic dynamic systems, different GA filters can be developed which exploit numerical methods to compute Gaussian weighted integrals involved in the proposed Bayesian solution. Existing GA filter with one-step randomly delayed measurements and existing GA filter with colored measurement noises are special cases of the proposed GA filter. The efficiency and superiority of the proposed method are illustrated in a numerical example concerning a target tracking problem.
摘要
创新点
本文提出了一种新的带一步随机延迟量测和有色量测噪声的高斯近似滤波器. 对于线性系统, 提出的滤波器将退化成带一步随机延迟量测和有色量测噪声的 Kalman 滤波器. 对于非线性系统, 提出的滤波器的递归运行需要解析计算和高斯加权积分, 并且利用不同的数值积分方法去计算这些高斯加权积分可以推导出不同的高斯近似滤波器. 现有的带一步随机延迟量测的高斯近似滤波器和带有色量测噪声的高斯近似滤波器都是本文所提出方法的特例. 目标跟踪的仿真结果验证了本文所提出方法的有效性和与现有方法相比的优越性.
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Zhang, Y., Huang, Y. Gaussian approximate filter for stochastic dynamic systems with randomly delayed measurements and colored measurement noises. Sci. China Inf. Sci. 59, 92207 (2016). https://doi.org/10.1007/s11432-015-5489-1
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DOI: https://doi.org/10.1007/s11432-015-5489-1
Keywords
- state estimation
- Gaussian approximate filter
- one-step randomly delayed measurements
- colored measurement noises
- nonlinear stochastic dynamic systems
- Bayesian estimation