Target Tracking Algorithm Using Square-Root Gaussian Kernel Quadrature Filter

WANG Jiabao; CHEN Shuxin; WU Hao; HE Renke; XU Han

doi:10.7652/xjtuxb202204017

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Target Tracking Algorithm Using Square-Root Gaussian Kernel Quadrature Filter

更新时间：2025-07-09

- Target Tracking Algorithm Using Square-Root Gaussian Kernel Quadrature Filter
- Vol. 56, Issue 4, Pages: 157-164(2022)
- 作者机构：
  
  1. 空军工程大学信息与导航学院,西安,710077
  2. 地理信息工程国家重点实验室,西安,710054
  3. 93184部队,北京,100076
  4. 95655部队,成都,611530
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202204017
  CLC： TP391
- Online First：10 April 2022，
  
  Published：2022
- 稿件说明：
移动端阅览
WANG Jiabao, CHEN Shuxin, WU Hao, et al. Target Tracking Algorithm Using Square-Root Gaussian Kernel Quadrature Filter[J]. 2022, 56(4): 157-164.
DOI：

WANG Jiabao, CHEN Shuxin, WU Hao, et al. Target Tracking Algorithm Using Square-Root Gaussian Kernel Quadrature Filter[J]. 2022, 56(4): 157-164. DOI： 10.7652/xjtuxb202204017.

摘要

为进一步提高目标跟踪精度

提出了一种新的平方根高斯核积分滤波算法(SGKQF)。选取比例因子重构高斯-厄米特积分点

利用高斯核构造的线性方程组计算相应权重

从而建立单变量高斯核积分规则; 采用张量积方法将其扩展到多变量积分规则

以实现对多维积分的数值近似; 将其引入非线性高斯递推滤波框架中

为增强算法的稳定性

在滤波过程中使用协方差的平方根形式

得到SGKQF算法。将所提算法应用于纯方位目标跟踪系统中

仿真结果表明

在选取合适高斯核带宽的条件下

相对于传统GHQF算法

SGKQF对目标位置和速度估计的精度分别提高了8.7%和11.8%

可获得更高的滤波估计精度。

Abstract

To further improve the accuracy of target tracking

a novel algorithm of square-root Gaussian kernel quadrature filter(SGKQF)is proposed. The scale factor is selected to reconstruct the Gauss-Hermite integral point in the algorithm

and the corresponding weight is calculated by linear equations formulated by the Gaussian kernel

thereby establishing the univariate Gaussian kernel quadrature rule; Subsequently

the tensor product method is used to extend the rule into multivariate quadrature rule by for the numerical approximation of multi-dimensional integrals; Finally

the multivariate quadrature rule is introduced into the framework of nonlinear Gaussian recursive filtering

and the SGKQF is obtained by implementing the square-root form of the covariance in the filtering process to enhance the stability. The proposed algorithm is applied to the bearings-only target tracking system to test its performance. The simulation result shows that

compared with traditional GHQF algorithm

SGKQF can achieve higher filter estimation accuracy under the condition of selecting appropriate Gaussian kernel bandwidth

with the improvement of estimation accuracy in position and velocity by 8.7% and 11.8% respectively.

关键词

Keywords

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