Performance Evaluation of Compressive Sensing for multifrequency audio Signals with Various Reconstructing Algorithms

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.

show abstract

Performance Evaluation of Compressive Sensing for multifrequency audio Signals with Various Reconstructing Algorithms

Cited by 4 publications

References 16 publications

Comparative Study to Analyse the Effect of Speaker’s Voice on the Compressive Sensing Based Speech Compression

Comparative Study to Analyse the Effect of Speaker’s Voice on the Compressive Sensing Based Speech Compression

Speech Signal Compression and Reconstruction Using Compressive Sensing Approach

GNSS Signal Compression Acquisition Algorithm Based on Sensing Matrix Optimization

Contact Info

Product

Resources

About