Abstract
The performance of the weighted sparse Bayesian inference (OGWSBI) algorithm for off-grid coherent DOA estimation is not satisfactory due to the inaccurate weighting information. To increase the estimation accuracy and efficiency, an improved OGWSBI algorithm based on a higher-order off-grid model and unitary transformation for off-grid coherent DOA estimation is proposed in this paper. Firstly, to reduce the approximate error of the first-order off-grid model, the steering vector is reformulated by the second-order Taylor expansion. Then, the received data is transformed from complex value to real value and the coherent signals are decorrelated via utilizing unitary transformation, which can increase the computational efficiency and restore the rank of the covariance matrix. Finally, in the real field, the steering vector higher-order approximation model and weighted sparse Bayesian inference are combined together to realize the estimation of DOA. Extensive simulation results indicate that under the condition of coherent signals and low SNR, the estimation accuracy of the proposed algorithm is about 50% higher than that of the OGWSBI algorithm, and the calculation time is reduced by about 60%.
Highlights
Direction-of-arrival (DOA) estimation is a basic problem in array signal processing and one of the crucial tasks in radar, sonar, and other fields [1, 2]
In order to further analyze the performance of the unitary transformation and the higher-order approximation of the steering vector, in the following simulations, we first put together the unitary transformation and the OGWSBI algorithm to obtain a real value (RV)-OGWSBI algorithm
Thereafter, the performance of the off-grid sparse Bayesian inference (OGSBI), OGWSBI, and RV-OGWSBI algorithms are compared by simulations
Summary
Direction-of-arrival (DOA) estimation is a basic problem in array signal processing and one of the crucial tasks in radar, sonar, and other fields [1, 2]. Compared with subspace DOA estimation algorithms, the sparse representation methods exhibit many advantages, e.g., improved robustness to noise, limited number of snapshots, and correlation of signals [11]. These methods employ a fixed sampling grid and can achieve outstanding performance only if all the true DOAs are exactly lying on the sampling grid points. The most representative one is the off-grid sparse Bayesian inference (OGSBI) algorithm proposed by Yang et al [16], which achieves high-precision DOA estimation under coarse grid conditions. Simulation results show that compared with the OGWSBI algorithm, the proposed algorithm has obvious improvements in accuracy and efficiency
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