Abstract
An enhanced smoothed -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 -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.
Highlights
In order to verify the performance of the proposed algorithm, we present the simulation results, which are based on MATLAB and the experimental results, which are based on the measured data
In order to verify the DOA estimation performance of the covariance-fitting smoothed l0 -norm (SL0), the spectra of the proposed scheme are compared with the conventional beamforming algorithm
In order to improve the performance of the conventional smoothed l0 norm method that performs sparse recovery using a single sample, the compressive-sensing-based covariance-fitting smoothed l0 norm method was proposed in this paper
Summary
The direction-of-arrival (DOA) estimation method is a basic required technique to estimate the locations of the targets. In order to estimate the DOAs of the incident signals based on the CS method, a data-fitting algorithm was proposed [8]. In order to enhance the DOA estimation performance of the data-fitting algorithm, the single measurement formulation of the conventional algorithm was expanded to the multiple snapshots measurement formulation. In [16], a reweighted smoothed l0 norm-based DOA estimation method for the monostatic MIMO radar system was proposed. In order to perform the sparse recovery using a single snapshot, the conventional method is vulnerable to the low SNR and adjacent multiple targets. To enhance the performance of the conventional scheme and robustness for the correlated signal, a covariance-fitting-based SL0 algorithm is presented in this paper. In order to verify the performance of the proposed algorithm, we present the simulation results, which are based on MATLAB and the experimental results, which are based on the measured data
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