A robust l(p)-norm localization of moving targets in distributed multiple-input multiple-output radar with measurement outliers

The Gaussian noise model and estimators based on least squares (LS) are widely used in target localisation with distributed multiple-input multiple-output (MIMO) radar because of their computational efficiency. However, the accuracy of existing LS-based target localisation algorithms deteriorates sharply in the presence of outliers in the measurements. Thus, a robust solution is developed based on the lp ${l}_{p}$-norm minimisation criterion and iteratively reweighted least squares (IRLS) for locating a moving target with impulse noise using the angle of arrival (AOA), time delay (TD), and Doppler shift (DS) measurements. First, the AOA, TD, and DS measurement noise models are developed based on the & alpha;-stable distribution. Then, the localisation problem is transformed into an lp ${l}_{p}$-norm minimisation problem by linearising the AOA, TD, and DS measurement equations. Finally, the lp ${l}_{p}$-norm minimisation problem is solved using an IRLS method to obtain the target position and estimate the velocity. Moreover, the optimum of the norm order (p) and the Cramer-Rao lower bound for the target position and velocity estimation are derived under & alpha;-stable distributed measurement noise. The simulation results demonstrate that the developed algorithm offers higher accurascy and robustness than the existing ones in the presence of measurement outliers.