In traditional target tracking methods, the angle error and range error are often measured by the empirical value, while observation noise is a constant. In this paper, the angle error and range error are analyzed. They are influenced by the signal-to-noise ratio (SNR). Therefore, a model related to SNR has been established, in which the SNR information is applied for target tracking. Combined with an advanced nonlinear filter method, the extended Kalman filter method based on the SNR model (SNR-EKF) and the unscented Kalman filter method based on the SNR model (SNR-UKF) are proposed. There is little difference between the SNR-EKF and SNR-UKF methods in position precision, but the SNR-EKF method has advantages in computation time and the SNR-UKF method has advantages in velocity precision. Simulation results show that target tracking methods based on the SNR model can greatly improve the tracking performance compared with traditional tracking methods. The target tracking accuracy and convergence speed of the proposed methods have significant improvements.

在传统的目标跟踪方法中，角度误差和距离误差通常由经验值来衡量，而观察噪声则是恒定的。本文分析了角度误差和距离误差。它们受信噪比（SNR）的影响。因此，已经建立了与SNR有关的模型，其中将SNR信息应用于目标跟踪。结合先进的非线性滤波方法，提出了基于SNR模型（SNR-EKF）的扩展卡尔曼滤波方法和基于SNR模型（SNR-UKF）的无味卡尔曼滤波方法。SNR-EKF和SNR-UKF方法在位置精度上几乎没有区别，但是SNR-EKF方法在计算时间上具有优势，而SNR-UKF方法在速度精度上具有优势。仿真结果表明，与传统的跟踪方法相比，基于信噪比模型的目标跟踪方法可以大大提高跟踪性能。所提方法的目标跟踪精度和收敛速度都有明显的提高。