An Efficient Improved OGWSBI Algorithm for Accurate Off-Grid DOA Estimation of Coherent Signals

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%.