A New PAST-Based Adaptive ESPIRT Algorithm with Variable Forgetting Factor and Regularization

Abstract

The estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm is a widely used subspace-based method for direction-of-arrival (DOA) estimation in array signal processing and spectral analysis. It requires the estimation of the signal subspaces of rotational invariance sub-arrays of a sensor array, from which the DOAs can be estimated by solving an eigenvalue problem. This paper proposes a projection approximation subspace tracking (PAST)-based adaptive ESPRIT algorithm with variable forgetting factor (VFF) and variable regularization (VR). The VFF and VR PAST algorithm is based on a recently proposed Locally Optimal FF (LOFF) scheme with improved convergence speed and steady state error performance. Moreover, variable regularization is incorporated to reduce the estimation variance during ill-conditioning or low input signal level. The proposed LOFF-VR adaptive ESPRIT method is also utilized for tracking the eigenvalues and hence the DOAs. Experimental simulations show that the proposed LOFF-VR-ESPRIT algorithm outperforms the conventional approaches in stationary and nonstationary environments, especially in the presence of signal fading.