有色噪声下基于总体最小二乘和卡尔曼滤波的无源定位方法

Bearings-only Passive Target Localization Based on TLS and Kalman Filter with Colored Noise

研究了有色量测噪声条件下存在系统误差时的单站纯方位角无源定位问题,给出了一种新的迭代总体最小二乘-Kalman滤波算法。该算法利用总体最小二乘法(TLS)进行无源定位,并通过有色量测噪声下的Kalman滤波对系统误差进行估计,将该滤波与TLS进行迭代,可有效抑制色噪声的干扰并大大减小系统误差对定位精度的影响,计算机仿真验证了该算法的有效性,且其性能优于文中其他几种算法。

The single station bearings-only passive target localization is studied when system bias is existent and the observation noise is colored noise, a new iterative TLS-KF algorithm is given. The algorithm utilize the total least squares （TLS） to passive target localization, and the system bias is estimated by using the Kalman filter with colored noise, the iteration of the filter and TLS can restrain the disturbance of colored noise in observation and minish the effect of system bias obviously. Simulations have proved the efficacy of the algorithm ,which is superior then the other algorithms in this paper. The study of this paper is helpful in practical target localization.