Recursive least square algorithm for radar position estimation under uncertainty of measurement noise covariance

Abstract

Because the deployment position of mobile radar can not be obtained or is not accurate enough, when the recursive least squares (RLS) algorithm is used for radar position estimation based on the position of a known detected target and radar measurement data, the colored noise in the measurement data leads to the uncertainty of radar measurement noise covariance matrix, resulting in deviation or even divergence of estimation results. In this paper, a recursive least squares algorithm for exponential weighted covariance estimation based on estimation error feedback to adjust forgetting factor and adaptive window is proposed. Firstly, the nonlinear observation equation is constructed according to the radar detection model. Then, aiming at the influence of colored noise on the measurement noise covariance in the radar system, a measurement noise covariance estimation algorithm is designed, and the algorithm flow for radar position estimation is given. The simulation results show that the proposed method can change the forgetting factor and adaptive window according to the estimation error, and update the measurement noise covariance matrix in real time, so that the recursive convergence speed is faster, and the estimation accuracy is higher than that of the RLS algorithm and the RLS algorithm using residual-based adaptive estimation (RAE) for measurement noise estimation; At the same time, the algorithm eliminates the influence of colored noise in the measurement data, the estimation result of radar position can quickly converge to the real value, and the plane estimation accuracy can reach meter level.