多雷达组网虚拟观测卡尔曼滤波算法研究

系统仿真学报 ›› 2015, Vol. 27 ›› Issue (4): 851-858.

• 信息、控制、决策与仿真 • 上一篇下一篇

多雷达组网虚拟观测卡尔曼滤波算法研究

赵温波¹, 丁海龙¹, 曲成华², 莫军²

1.陆军军官学院, 合肥 230031;
2.中国电子科技集团公司第38研究所, 合肥 230088

收稿日期:2014-04-03 修回日期:2014-06-11 发布日期:2020-08-20
作者简介:赵温波(1972-):男,吉林吉林人,博士,副教授,硕导,研究方向为数据融合、目标跟踪、统计信号处理;丁海龙(1987-):男,江西新建人,硕士生,研究方向为目标跟踪、雷达数据处理。
基金资助:
国家自然科学基金资助项目(61273001);安徽省自然科学基金资助项目(11040606M130)

Study on Virtual-observation Kalman Filter Algorithm of Multi-radar Networking

Zhao Wenbo¹, Ding Hailong¹, Qu Chenghua², Mo Jun²

1. Amy Officer Academy of PLA, Hefei 230031, China;
2. No. 38 Research Department of CETC, Hefei 230088, China

Received:2014-04-03 Revised:2014-06-11 Published:2020-08-20

摘要/Abstract

摘要： 多雷达组网对空域运动目标跟踪时,组网雷达极坐标测量值与目标状态值呈非线性关系,不满足卡尔曼滤波线性化使用要求。提出将组网融合中心惯性坐标系虚拟为滤波观测坐标系,使得滤波状态和虚拟测量简化为线性关系,通过虚拟观测噪声建模、滤波初始化建模,解决了多雷达组网使用卡尔曼滤波对空域运动目标的最优化滤波估计问题。软件仿真测试和检飞数据验证表明:虚拟观测卡尔曼滤波算法(Virtual-Observation Kalman Filter Algorithms, VOKFA),滤波精度高、算法稳定性好,非常适合于多雷达组网滤波跟踪工程应用。

关键词: 雷达组网, 虚拟观测, 坐标变换, 误差统计特性, 初始化策略

Abstract: Under the circumstances of tracking targets in multi-radar networking (MRN), measure-value in polar coordinates of the networked radar (NR) has the nonlinear relation with state-value of targets tracking coordinates, which does not satisfy linear requirement of Kalman filter algorithm (KFA) application. Virtualizing the tracking coordinates of MRN as the measure coordinates of KFA was introduced. As a result, original nonlinear relation was simplified as a linear form. By means of modeling virtual observation noise and constructing the initialization strategy, KFA could solve state estimation problem in MRN. Simulation analysis and real data verification demonstrate that virtual-observation KFA (VOKFA) has quick computation velocity, high precision and good stability, which is very suitable for engineering application of data fusion in MRN.

Key words: radar network, virtual observation, coordinate transformation, error statistical characteristic, initialization strategy

中图分类号:

TN95

赵温波, 丁海龙, 曲成华, 莫军. 多雷达组网虚拟观测卡尔曼滤波算法研究[J]. 系统仿真学报, 2015, 27(4): 851-858.

Zhao Wenbo, Ding Hailong, Qu Chenghua, Mo Jun. Study on Virtual-observation Kalman Filter Algorithm of Multi-radar Networking[J]. Journal of System Simulation, 2015, 27(4): 851-858.

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多雷达组网虚拟观测卡尔曼滤波算法研究

Study on Virtual-observation Kalman Filter Algorithm of Multi-radar Networking

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