Compressed Sensing Fusion Algorithm Based on Immune Algorithm

In current compressed sensing fusion reconstruction algorithms, one scheme is to execute compressed sensing reconstruction algorithms in parallel and independently, and obtain the estimated data by several different algorithms, this is the first stage of the fusion algorithm. The second stage is to fuse these estimated data to get the final estimated signal. In the second stage, different methods can be used to realize data fusion. In this paper, we proposed Immune Fusion of Reconstruction Algorithm (IFRA), which fuses data to obtain the final estimated signal by immune algorithm. Through simulation, we find that immune algorithm used in fusion has better performance than genetic algorithm and least square method used by predecessors, and the optimal solution is closer to the original signal. We use the measured underwater ship noise data to verify of the reconstruction algorithm. The effect of the fusion algorithm is better than a single reconstruction algorithm, and IFRA is the best in the fusion algorithms.