基于自适应强跟踪CQKF的目标跟踪算法

刘畅; 杨锁昌; 汪连栋; 张宽桥

doi:10.13700/j.bh.1001-5965.2017.0312

基于自适应强跟踪CQKF的目标跟踪算法

doi: 10.13700/j.bh.1001-5965.2017.0312

1.
军械工程学院导弹工程系, 石家庄 050000
2.
电子信息系统复杂电磁环境效应国家重点实验室, 洛阳 471003

详细信息

作者简介:
刘畅男, 博士研究生。主要研究方向:非线性滤波、目标跟踪

杨锁昌男, 博士, 教授, 博士生导师。主要研究方向:导弹制导

通讯作者:
杨锁昌, E-mail: yangsuochang_jx@sina.com

中图分类号: TN953
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- 被引次数: 15
出版历程
- 收稿日期: 2017-05-15
- 录用日期: 2017-06-26
- 网络出版日期: 2018-05-20

Target tracking algorithm based on adaptive strong tracking CQKF

1.
Department of Missile Engineering, Ordnance Engineering College, Shijiazhuang 050000, China
2.
State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, Luoyang 471003, China

More Information

Corresponding author: YANG Suochang, E-mail:yangsuochang_jx@sina.com

摘要

摘要:
针对容积积分卡尔曼滤波（CQKF）受模型不确定性影响较大及需要精确已知噪声统计特性的缺点，提出了一种自适应强跟踪CQKF算法。该算法根据强跟踪滤波原理，引入渐消因子调整状态预测协方差矩阵，强迫残差序列正交，有效抑制了模型不确定性引起的滤波发散。在滤波过程中，利用Sage-Husa时变噪声统计估值器对过程噪声及量测噪声实时估计，提高了算法在未知时变噪声环境下的滤波精度。目标跟踪仿真实验验证了算法的有效性和鲁棒性。
- 目标跟踪 /
- 容积积分卡尔曼滤波(CQKF) /
- 强跟踪滤波 /
- 噪声统计估值器 /
- 自适应滤波
Abstract:
As cubature quadrature Kalman filter (CQKF) is easily influenced by uncertainty of state-space model and need to know exactly noise statistics, a new type of adaptive CQKF algorithm with strong tracking behavior is proposed. Based on the theory of strong tracking filter, the new algorithm introduces fading factor to adapt to covariance matrix and reinforces residual sequence to be orthogonal, which effectively suppresses the filtering divergence caused by the model uncertainty. In the process of filtering, processing noise and measurement noise should be estimated online by the Sage-Husa noise statistics estimator, which will improve the filter precision under the circumstance of unknown time-varying noise. Simulations of target tracking demonstrate the efficiency and robustness of the algorithm.
- target tracking /
- cubature quadrature Kalman filter (CQKF) /
- strong tracking filter /
- noise statistics estimators /
- adaptive filter

HTML全文

图 1 AST-CQKF算法流程图

Figure 1. AST-CQKF algorithm flowchart

下载: 全尺寸图片幻灯片

图 2 跟踪均方根误差(情形1)

Figure 2. RMSE tracking(Case 1)

下载: 全尺寸图片幻灯片

图 3 跟踪均方根误差(情形2)

Figure 3. RMSE tracking(Case 2)

下载: 全尺寸图片幻灯片

表 1 CQKF与AST-CQKF算法蒙特卡罗仿真计算时间

Table 1. Calculation time of Monte Carlo simulation in CQKF and AST-CQKF algorithm

仿真步数	仿真次数	仿真计算时间/s
仿真步数	仿真次数	CQKF	AST-CQKF
100	200	3.350 8	6.933 4
100	100	1.742 3	3.417 1
100	1	0.034 4	0.082 9
50	200	1.736 6	3.380 9
50	100	0.903 4	1.699 1
50	1	0.023 7	0.062 2

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