Symbol Rate Estimation Based on Sparse Bayesian Learning

Existing methods for symbol rate estimation of phase coded signals require amounts of sensing data, and are of high computational complexity. This paper analyzes the structure characteristics of BPSK signals, which are employed as the prior information for signal compressing and dimensionality reduction. The sensing matrix can be split into sine and cosine component, combined with the Fourier transform parity. According to the fact that the real and imaginary components of a complex value share the same support set, the symbol rate estimation can be obtained, using unilateral spectral of the delay-product vector reconstructed by multi-task Bayesian compressive sensing. Theoretical analysis and simulation results show that compared with other parameter estimation algorithms, the proposed method can reduce the measurements and significantly improve the real-time ability, while keeping the high reconstruction accuracy.