航空学报 > 2020, Vol. 41 Issue (S2): 724395-724395   doi: 10.7527/S1000-6893.2020.24395

基于变分贝叶斯的星载雷达非线性滤波

闫文旭1,2, 兰华1,2, 王增福1,2, 金术玲3, 潘泉1,2   

  1. 1. 西北工业大学 自动化学院, 西安 710129;
    2. 信息融合技术教育部重点实验室, 西安 710129;
    3. 中国电子科技集团公司第38研究所, 合肥 230088
  • 收稿日期:2020-06-11 修回日期:2020-06-25 发布日期:2020-07-17
  • 通讯作者: 兰华 E-mail:lanhua@nwpu.edu.cn
  • 基金资助:
    国家自然科学基金(61873211)

Nonlinear filtering for spaceborne radars based on variational Bayes

YAN Wenxu1,2, LAN Hua1,2, WANG Zengfu1,2, JIN Shuling3, PAN Quan1,2   

  1. 1. School of Automation, Northwestern Polytechnical University, Xi'an 710129, China;
    2. Key Laboratory of Information Fusion Technology, Ministry of Education, Xi'an 710129, China;
    3. China Electronics Technology Group Corporation 38th Research Institute, Hefei 230088 China
  • Received:2020-06-11 Revised:2020-06-25 Published:2020-07-17
  • Supported by:
    National Natural Science Foundation of China (61873211)

摘要: 星载雷达由于其探测范围广、距离远、全天候等优点,在预警防御系统中占有十分重要的地位。然而,由于观测平台的高速运动以及摄动干扰、传感器观测非线性等问题,使得星载雷达目标高精度跟踪带来严峻挑战。针对星载雷达非线性状态估计问题,采用一种基于变分贝叶斯的非线性滤波方法,该方法通过将非线性状态估计问题转化为优化问题,通过迭代优化获得了闭环解析解。此外,针对坐标变换中俯仰角量测缺失问题,提出了一种基于先验目标高度的俯仰角估计方法。通过数值仿真,验证了所提方法较传统非线性滤波方法,如扩展卡尔曼滤波、不敏卡尔曼滤波、转换量测卡尔曼滤波,具有更好的估计精度。

关键词: 星载雷达, 目标跟踪, 变分贝叶斯, 非线性滤波, 俯仰角估计

Abstract: Spaceborne radars play an important role in early warning defense systems because of their unique advantages such as wide detection range, long distance and all-weather surveillance capability. Due to the high-speed movement of the platform and the strong nonlinear observation function, high-accuracy target tracking for spaceborne radars is difficult. In this paper, we propose a variational Bayes-based nonlinear filtering method, which transforms the nonlinear state estimation problem into an optimization problem. The analytical solution is obtained via a closed-loop iteration manner. Moreover, a pitch angle estimation method is presented using the a priori information of target height. Simulation results show that, compared with the extended Kalman filter, unscented Kalman filter, and the converted measurement Kalman filter, the proposed variational Bayes-based nonlinear filtering method achieves the best estimation accuracy.

Key words: spaceborne radars, target tracking, variational Bayes, nonlinear filtering, pitch estimation

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