Abstract
Direction-of-arrival (DOA) estimation has drawn considerable attention in array signal processing, particularly with coherent signals and a limited number of snapshots. Forward–backward linear prediction (FBLP) is able to directly deal with coherent signals. Support vector regression (SVR) is robust with small samples. This paper proposes the combination of the advantages of FBLP and SVR in the estimation of DOAs of coherent incoming signals with low snapshots. The performance of the proposed method is validated with numerical simulations in coherent scenarios, in terms of different angle separations, numbers of snapshots, and signal-to-noise ratios (SNRs). Simulation results show the effectiveness of the proposed method.
Highlights
Direction-of-arrival (DOA) estimation is of great importance in array signal processing [1,2].A considerable number of techniques have been developed for the determination of the DOAs of incoming signals
There are K far-field narrow band incoming signals impinging on the antennas, which are corrupted by an additive Gaussian white noise (AGWN)
The research results in [15] show that the decorrelation ability of Linear prediction (LP) methods is at the expense of the real effective array aperture
Summary
Direction-of-arrival (DOA) estimation is of great importance in array signal processing [1,2]. SS was originally proposed for DOA estimation with coherent signals in [9], which was only a single-direction SS technique. The other approach combines the theory of SVR with classical signal processing methods. There are combinations of SVR with linear signal processing methods such as auto-regressive model (AR) [18] and auto-regressive moving average (ARMA) [23] for frequency estimation and system identification problems. There is no explicit work about SVR-based LP models with coherent signals. We propose to combine SVR with FBLP in the estimation of the DOAs of coherent incoming signals with a small number of snapshots. Re(z) and Im(z) denote the real and imaginary parts of z, respectively
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