A High Precision DOA Estimation Algorithm for Cyclostationary Signals

In order to improve the low estimation accuracy and low resolution of the existing Direction of Arrival (DOA) estimation algorithms for cyclostationary signals, a high precision DOA estimation algorithm for cyclostationary signals using a coprime array combined with common spectral peak search is proposed in this paper. The main idea of the algorithm is to use the sparse characteristics of the coprime array subarrays and solve the DOA estimates from the subarrays respectively. Among them, the blurred values of the subarrays have coprime characteristic, and the common value of the two subarrays DOA solution sets is the true angle of target signal. Firstly, the algorithm constructed the array receiving model of each subarray, and obtained the subarray cyclic autocorrelation matrix. Then, according to the characteristics of each subarray receiving model, we use the subspace method to solve the DOA value of the subarray. Finally, the real DOA value of the target signal can be obtained by comparing the solutions of the two subarrays with the idea of common spectral peaks. The proposed algorithm is based on a coprime array with sparse characteristic, and the common spectral peak is used to eliminate the problem of blurred values caused by sparsity, thus improving the estimation accuracy and resolution of the algorithm. The simulation results show that the proposed algorithm can realize effective estimation of cyclostationary signals. Compared with most cyclostationary signals DOA estimation algorithms, the estimation accuracy of the proposed algorithm is further improved.