An Enhanced Smoothed L0-Norm Direction of Arrival Estimation Method Using Covariance Matrix.

An enhanced smoothed l0-norm algorithm for the passive phased array system, which uses the covariance matrix of the received signal, is proposed in this paper. The SL0 (smoothed l0-norm) algorithm is a fast compressive-sensing-based DOA (direction-of-arrival) estimation algorithm that uses a single snapshot from the received signal. In the conventional SL0 algorithm, there are limitations in the resolution and the DOA estimation performance, since a single sample is used. If multiple snapshots are used, the conventional SL0 algorithm can improve performance in terms of the DOA estimation. In this paper, a covariance-fitting-based SL0 algorithm is proposed to further reduce the number of optimization variables when using multiple snapshots of the received signal. A cost function and a new null-space projection term of the sparse recovery for the proposed scheme are presented. In order to verify the performance of the proposed algorithm, we present the simulation results and the experimental results based on the measured data.

Statistics on noise covariance matrix for covariance fitting-based compressive sensing direction-of-arrival estimation algorithm: For use with optimization via regularization.

A covariance fitting algorithm for the estimation of direction-of-arrivals of multiple incident signals is addressed in this paper. The scheme takes advantage of the fact that the incident signals are spatially sparse. A previous study has presented the regularization parameters of the covariance fitting for a very large number of snapshots. In this paper, a strategy on how to determine the regularization constant of the covariance fitting for a general number of snapshots is presented. The strategy essentially exploits the norm of the noise covariance matrix. The proposed algorithm has been validated via numerical simulations.