Compressed Sensing： Optimized Overcomplete Dictionary for Underwater Acoustic Channel Estimation

Compressed Sensing （CS） offers a method to solve the channel estimation problems for an underwater acoustic system, based on the existence of a sparse representation of the treated signal and an overcomplete dictionary with a set of non-orthogonal bases. In this paper, we proposed a new approach to optimize dictionaries by decreasing the average measure of the mutual coherence of the effective dictionary. A fixed link between the average mutual coherence and the CS perforrmnce is indicated by designing three factors： operating bandwidth, the number of pilot subcarriers, and coherence bandwidth. Both the Orthogonal Matching Pursuit （OMP） and the Basis Pursuit De-Noising （BPDN） are compared to the Dantzig Selector （DS） for different Signal Noise Ratio （SNR） and shown to benefit from the newly designed dictionary. Nurnerical sinmlations and experimental data of an OFDM receiver are used to evaluate the proposed method in comparison with the conventional LeastSquare （LS） estirmtor. The results show that the dictionary with a better condition considerably improves the perforrmnce of the channel estimation.

Sensing Matrix Optimization for Multi-Target Localization Using Compressed Sensing in Wireless Sensor Network

In the multi-target localization based on Compressed Sensing(CS),the sensing matrix's characteristic is significant to the localization accuracy.To improve the CS-based localization approach's performance,we propose a sensing matrix optimization method in this paper,which considers the optimization under the guidance of the t%-averaged mutual coherence.First,we study sensing matrix optimization and model it as a constrained combinatorial optimization problem.Second,the t%-averaged mutual coherence is adopted as the optimality index to evaluate the quality of different sensing matrixes,where the threshold t is derived through the K-means clustering.With the settled optimality index,a hybrid metaheuristic algorithm named Genetic Algorithm-Tabu Local Search(GA-TLS)is proposed to address the combinatorial optimization problem to obtain the final optimized sensing matrix.Extensive simulation results reveal that the CS localization approaches using different recovery algorithms benefit from the proposed sensing matrix optimization method,with much less localization error compared to the traditional sensing matrix optimization methods.

RECONSTRUCTION OF SPARSE POLYNOMIALS VIA QUASI-ORTHOGONAL MATCHING PURSUIT METHOD

In this paper,we propose a Quasi-Orthogonal Matching Pursuit(QOMP)algorithm for constructing a sparse approximation of functions in terms of expansion by orthonormal polynomials.For the two kinds of sampled data,data with noises and without noises,we apply the mutual coherence of measurement matrix to establish the convergence of the QOMP algorithm which can reconstruct s-sparse Legendre polynomials,Chebyshev polynomials and trigonometric polynomials in s step iterations.The results are also extended to general bounded orthogonal system including tensor product of these three univariate orthogonal polynomials.Finally,numerical experiments will be presented to verify the effectiveness of the QOMP method.

Statistic of a Gaussian beam from an arbitrary rough target in the single passage atmospheric turbulence

The extended Huygens-Fresnel principle and Goodman model was utilized for target surface to derive the mutual coherence function(MCF) of a Gaussian beam reflected from an arbitrary rough target in atmospheric turbulence. According to the MCF, expressions of the mean irradiance and average speckle size at the receiver were obtained. The analysis indicated that the mean intensity is closely related to the ratio of root mean square(rms) height to the lateral correlation length. In addition, the speckle size at the receiver is associated with turbulence strength, propagation distance and roughness of the target. The results can be reduced to the result of a Gaussian beam illuminating rough target and scattering from a target in free space.