Low Complexity Error-Censoring RLS Algorithm for DOA Estimation

Adaptive nulling antenna technology is widely used to estimate the direction of arrival (DOA) with the aim of mitigating the high computational complexity issues imposed by the classical multiple signal classification (MUSIC) algorithms. However, most existing adaptive algorithms, such as the least mean square (LMS) and gradient descent total least-squares (GDTLSs) algorithms, exhibit slow convergence speed and large steady-state error. This article proposes an error-censoring recursive least squares (EC-RLSs) algorithm to improve convergence while reducing computational complexity. By evaluating how the sum of weighted error squares deviates from its minimal value, it is possible to prevent unnecessary weight updating and thereby alleviate the high computational cost associated with conventional algorithms. Simulation results demonstrate the superiority of the proposed EC-RLS algorithm over comparable works, even in situations where the number of incident sources is equal to the number of elements in the array or when the direction of incident signals changes abruptly.