Optimising compressive sensing matrix using Chicken Swarm Optimisation algorithm. (1st October 2019) - Aziz, Ahmed
Abstract : According to compressive sensing (CS) technique, the smaller the mutual coherence between CS matrix and transformer matrix (Φ and Ψ ), the better the performance of CS matrix to reduce the reconstruction error. Generating CS matrix from any distribution may achieve low but not minimum mutual coherence. In this study, using Chicken Swarm Optimisation (CSO), we propose a new efficient CS matrix optimisation algorithm (CSMO‐CSO) to optimise CS matrix by minimising the mutual coherence between Φ and Ψ . The proposed CSMO‐CSO succeeds to minimise the coherence between CS matrix and transformer matrix which improves the CS matrix, and thereby minimise the reconstruction error. The simulation results show that the performance of proposed algorithm exceeds the baseline existing algorithm in terms of mutual coherence reduction and normalised mean square error reduction.