GNSS Signal Compression Acquisition Algorithm Based on Sensing Matrix Optimization

Abstract

Due to the sparsity of GNSS signal in the correlation domain, compressed sensing theory is considered to be a promising technology for GNSS signal acquisition. However, the detection probability of the traditional compression acquisition algorithm is low under low signal-to-noise ratio (SNR) conditions. This paper proposes a GNSS compression acquisition algorithm based on sensing matrix optimization. The Frobenius norm of the difference between Gram matrix and an approximate equiangular tight frame (ETF) matrix is taken as the objective function, and the modified conjugate gradient method is adopted to reduce the mutual coherence between the measurement matrix and the sparse basis. Theoretical analysis and simulation results show that the proposed algorithm can significantly improve the detection probability compared with the existing compression acquisition algorithms under the same SNR.