Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Detection, Estimation, and Modulation Theory: Radar-Sonar Signal Processing and Gaussian Signals in Noise
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Atomic Decomposition by Basis Pursuit
SIAM Review
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
On the performance of artificial bee colony (ABC) algorithm
Applied Soft Computing
Analysis of the publications on the applications of particle swarm optimisation
Journal of Artificial Evolution and Applications - Regular issue
High-resolution radar via compressed sensing
IEEE Transactions on Signal Processing
Coherence-based performance guarantees for estimating a sparse vector under random noise
IEEE Transactions on Signal Processing
Cross-ambiguity function domain multipath channel parameter estimation
Digital Signal Processing
A sparse signal reconstruction perspective for source localization with sensor arrays
IEEE Transactions on Signal Processing - Part II
Identification of Matrices Having a Sparse Representation
IEEE Transactions on Signal Processing
Sparse Bayesian learning for basis selection
IEEE Transactions on Signal Processing
Optimal training design for MIMO OFDM systems in mobile wireless channels
IEEE Transactions on Signal Processing
Matching pursuits with time-frequency dictionaries
IEEE Transactions on Signal Processing
Optimal training for block transmissions over doubly selective wireless fading channels
IEEE Transactions on Signal Processing
Cluster Characteristics in a MIMO Indoor Propagation Environment
IEEE Transactions on Wireless Communications
Uncertainty principles and ideal atomic decomposition
IEEE Transactions on Information Theory
Lower bounds on the maximum cross correlation of signals (Corresp.)
IEEE Transactions on Information Theory
Complex sequences with low periodic correlations (Corresp.)
IEEE Transactions on Information Theory
Greed is good: algorithmic results for sparse approximation
IEEE Transactions on Information Theory
Stable recovery of sparse overcomplete representations in the presence of noise
IEEE Transactions on Information Theory
Just relax: convex programming methods for identifying sparse signals in noise
IEEE Transactions on Information Theory
A Statistical Model for Indoor Multipath Propagation
IEEE Journal on Selected Areas in Communications
Orthogonal Matching Pursuit for Sparse Signal Recovery With Noise
IEEE Transactions on Information Theory
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In this paper, a novel algorithm is proposed to achieve robust high resolution detection in sparse multipath channels. Currently used sparse reconstruction techniques are not immediately applicable in multipath channel modeling. Performance of standard compressed sensing formulations based on discretization of the multipath channel parameter space degrade significantly when the actual channel parameters deviate from the assumed discrete set of values. To alleviate this off-grid problem, we make use of the particle swarm optimization (PSO) to perturb each grid point that reside in each multipath component cluster. Orthogonal matching pursuit (OMP) is used to reconstruct sparse multipath components in a greedy fashion. Extensive simulation results quantify the performance gain and robustness obtained by the proposed algorithm against the off-grid problem faced in sparse multipath channels.