Estimation of direction-of-arrivals based on the array manifold space

In this paper, a high resolution technique for estimating DOAs of spatially close source signals is presented. It is observed that the array manifold over a sector of interest is rank deficient and the dimension of the array manifold space, which is the range space of the array manifold, is less than the number of sensors in the array. The true signal subspace is a subspace in the array manifold space. A novel technique is provided that searches for the signal subspace in this array manifold space. The resulting estimated signal subspace has minimum principal angles with the data signal subspace generated by eigen-decomposing the covariance matrix of the array data vector. It is proved that the proposed estimator is asymptotically consistent and the estimated signal subspace is closer to the true signal subspace than the data signal subspace formed by MUSIC. The proposed novel technique has better performance than the MUSIC algorithm. Its performance is comparable to MLE and MD-MUSIC yet it requires only one-dimensional searches and is computationally much less intense. Simulation results are presented to show the effectiveness of the proposed technique, and comparisons with MUSIC, MLE, and MD-MUSIC algorithms are also included.This research was supported by TRIO and NSERC.