基于变分贝叶斯的星载雷达非线性滤波

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。

Nonlinear filtering for spaceborne radars based on variational Bayes

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.