Direction-of-arrival estimation based on generalized B-splines and compressed sensing

To address the issues of degraded direction-of-arrival estimation performance and high computational complexity based on compressed sensing under low signal-to-noise ratio conditions, a multi-measurement vector iterative focusing direction-of-arrival estimation method based on generalized B-spline control point representation is proposed. An array signal based on generalized B-spline control points is established. Then, a regularized multi-measurement vector iterative focusing algorithm is developed. The spatial direction-of-arrival spectrum is represented by the energy spectral density of the generalized B-spline control points. The focusing value is adjusted according to the contribution of each atom in the complete dictionary to the array signal control points, thereby enabling the algorithm to focus more on atoms with greater contributions. This improvement ensures that the estimated direction-of-arrival value approaches the optimal solution as closely as possible. Research shows that when the number of control points fitted by the generalized B-spline is 2 to 3 times the number of signal periods contained in the signal length, it effectively suppresses background noise, improves the direction-of-arrival estimation performance under low signal-to-noise ratio conditions, and significantly reduces computational time. Furthermore, adjusting the focusing value based on the contribution ensures that the regularized multi-measurement vector iterative focusing algorithm converges more easily to the optimal solution, raises direction-of-arrival estimation precision and computational efficiency. At a signal-to-noise ratio of −16 dB, the performance of direction-of-arrival estimation is 41 times higher than that of the basic regularized multi-measurement vector iterative focusing algorithm, while the algorithm’s computational complexity is reduced by an average of 51.2%.