一种基于三维到达角的无偏伪线性卡尔曼滤波

为了解决大场景下基于三维到达角的目标跟踪问题，提出了一种具有无偏性的伪线性卡尔曼滤波。首先，基于三维到达角信息对目标运动模型与量测模型进行建模；之后，对量测模型进行了伪线性化处理，得到了线性形式的目标量测模型。为了解决伪线性卡尔曼滤波存在的有偏性问题，提出了一种结合EKF（extend Kalman filter）的三维伪线性无偏卡尔曼滤波。仿真实验表明，该模型能够对非机动目标与机动目标有效跟踪，对于百公里级别的目标，当角测量误差从0.1°变化到0.5°，算法在仿真时间结束时均能将绝对位置误差降低至10 km以内，且算法的运行速度与EKF为同一个量级，同时兼顾了抗干扰能力、定位跟踪精度、运行效率的要求，能够为大场景下的目标跟踪提供有效方法。

An unbiased pseudo-linear Kalman filter based on three-dimensional angle of arrival

In order to solve the problem of target tracking based on three-dimensional angle of arrival (AOA) in large scenes, a pseudo linear Kalman filter with unbiased properties is proposed. Firstly, the target motion and measurement are modeled based on three-dimensional angle of arrival. After that, the measurement model is pseudo-linearized to obtain a linear form of the target measurement model.In order to solve the bias problem of pseudo-linear Kalman filter, a 3D unbiased pseudo-linear Kalman filter combined with extended Kalman filter(EKF) is proposed. Simulation results show that the model can effectively track both non maneuvering and maneuvering targets. For targets at 100 km level, when the angle measurement error changes from 0.1° to 0.5°, the algorithm can reduce the absolute position error to less than 10 km at the end of the simulation time. The algorithm''s operating speed is at the same order of magnitude as EKF. Meanwhile, to meet the requirements of anti-interference ability, positioning and tracking accuracy, and operating efficiency, and it can provide effective method for target tracking in large scenes.