Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Atomic Decomposition by Basis Pursuit
SIAM Review
Uniform Uncertainty Principle and Signal Recovery via Regularized Orthogonal Matching Pursuit
Foundations of Computational Mathematics
High-resolution radar via compressed sensing
IEEE Transactions on Signal Processing
Restricted isometry constants where lpsparse recovery can fail for 0
IEEE Transactions on Information Theory
Relaxed conditions for sparse signal recovery with general concave priors
IEEE Transactions on Signal Processing
An effective memetic differential evolution algorithm based on chaotic local search
Information Sciences: an International Journal
A memetic particle swarm optimization algorithm for multimodal optimization problems
Information Sciences: an International Journal
Matching pursuits with time-frequency dictionaries
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
IEEE Transactions on Information Theory
A New TwIST: Two-Step Iterative Shrinkage/Thresholding Algorithms for Image Restoration
IEEE Transactions on Image Processing
Target Estimation Using Sparse Modeling for Distributed MIMO Radar
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Two-stage ensemble memetic algorithm: Function optimization and digital IIR filter design
Information Sciences: an International Journal
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Recent results of Compressive Sampling (CS) have demonstrated its feasibility in high-resolution radar targets estimation and imaging [2,10,14,15,17,19,23,29,30,32-34]. However, the signal recovery is reduced to seeking a sparse solution to an underdetermined linear system of equations. It is potentially very difficult because even finding a solution that approximates the true minimum is NP-hard. In this paper, we introduce Memetic Algorithm (MA) to solve this non-convex l"0-norm minimization problem, and design a compressive receiver for high-resolution range-reflectivity estimation of multiple radar targets. A double-population MA is proposed, where the position population is used to evaluate the ranges, and the coefficient population is used to realize a local search of target reflectivities. By combining the global search with a local searching operation to exploit the available knowledge in the recovery, the proposed MA outperforms the general purpose optimization algorithms in terms of the quality of solution. Some experiments are taken to investigate the performance of this compressive receiver at different sampling rates, and the results show the superiority to its counterparts in both noiseless environment and noisy, cluttered environment.