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The method of compressive sensing is applied to moving source data created by simulation to estimate the mode wavenumbers, mode depth functions and mode amplitudes without any environmental acoustic information, such as sound speed profile or bottom properties. The method needs in principle only data covering a short range span and is thus applicable to a range-dependent environment to estimate the...
Subspace based techniques for direction of arrival (DOA) estimation need large amount of snapshots to detect source directions accurately. This poses a problem in the form of computational burden on practical applications. The introduction of compressive sensing (CS) to solve this issue has become a norm in the last decade. In this paper, a novel CS beamformer root-MUSIC algorithm is presented with...
For complex large scale networks, like social networks, it is typically impossible to observe complete information about their topology structure or link weight directly. A recent proposal, the network resonance method, can estimate the eigenvalues and eigenvectors of the Laplacian matrix for representing network structure, by using the resonance phenomena of oscillation dynamics on networks. However,...
This paper introduces the basic theory and method of compressed sensing, and its application in DOA estimation. The theory uses a new sampling method through sparse sampling and reconstruction of the signal to break through the limitation of the Nyquist sampling theorem, effectively solving the inherent shortcomings of classic spatial spectrum estimation algorithms.
The under-determined direction of arrival (DOA) estimation problem for a mixture of circular and non-circular signals is studied in the context of sparse arrays and a novel compressive sensing based DOA estimation algorithm is proposed. Compared to a direct application of existing compressive sensing based DOA estimation algorithm, the new one can make a more effective use of the degree of freedoms...
In this paper the dynamic compressed sensing (DCS) estimation of time varying underwater acoustic (UWA) channel is investigated. By modeling the time varying UWA channels as sparse set consisting with constant and time-varying supports, the estimation of time varying UWA channel is transformed into a problem of dynamic compressed sensing (DCS) sparse recovery. Employing the combination of Kalman filter...
The recent advances of compressive sensing (CS) have witnessed a great potential of traffic condition estimation in road networks. In this paper, we propose a traffic estimation approach that applies compressive sensing technique to achieve a city-scale traffic estimation with only a small number of vehicle probes. In particular, we construct a new type of random matrix for CS which can significantly...
Underwater acoustic (UWA) channel estimation based on fast Bayesian matching pursuit (FBMP) is investigated in this paper. In UWA multipath channel, the positions of UWA channel taps usually obey the Bernoulli distribution, while the coefficients of UWA channel taps obey the complex-valued Gaussian distribution. Such knowledge of distribution is considered as a prior for UWA channel estimation based...
In this paper, we present a new approach of time of flight (TOF) estimation using wide band signals, based on compressive sensing (CS) reconstruction in warped domain. In real conditions, constraints related to signal's transmitting/receiving (such as the transducers configuration or the experimental setup) can affect the informational content of the signal. Thus, the received signals need content...
Various channels can be denoted by sparse channels and many algorithms have been proposed to exploit their sparsity. In this paper, we propose a mixed algorithm based on Greedy and LS algorithms for sparse channel estimation. Analyses of the proposed and commonly used algorithms in terms of performance and complexity are performed considering the channel's sparsity, the length of training sequence...
In recent years, accurate location and characterization of damage has motivated the engineering community to develop several damage identification techniques. Many of the nondestructive evaluations and structural health monitoring techniques are based on the analysis of huge amount of data collected from acousto-ultrasonic sensors. Such analysis is typically a very time-consuming process. Therefore,...
In ultrasonic NDE applications, the received signal carries valuable physical information along the wave propagation path such as locations, orientation and sizes of discontinuities. Various signal processing algorithms have been utilized to interrogate ultrasonic echoes, highly overlapped and sometimes noise-contaminated. As of assessing and monitoring the in-situ outsized structures, it becomes...
In this investigation, compressed sensing (CS) is utilized to examine the sparsity of ultrasonic NDE signal, which consequently improves the efficacy of data sampling with significant lower rate than the Nyquist rate. The time-of-arrivals (TOAs) and amplitudes of dominant echoes are estimated through annihilating filter in the CS via matrix operation. The matrix size is proportional to the number...
In this paper, a new compressive sensing (CS)-based direction of arrival (DOA) estimation technique using the Dantzig selector is proposed. The proposed scheme can identify more source signals than the number of sensors used, without requiring an a priori knowledge of the number of source signals to be estimated and without any constraint or assumption about the nature of the signal sources using...
In Big Data Processing we typically face very large data sets that are highly structured. To save the computation and storage cost, it is desirable to extract the essence of the data from a reduced number of observations. One example of such a structural constraint is sparsity. If the data possesses a sparse representation in a suitable domain, it can be recovered from a small number of linear projections...
Distributed compressive sensing is a framework considering jointly sparsity within signal ensembles along with multiple measurement vectors (MMVs). The current theoretical bound of performance for MMVs, however, is derived to be the same with that for single MV (SMV) because the characteristics of signal ensembles are ignored. In this work, we introduce a new factor called "Euclidean distances...
In Compressed Sensing, a real-valued sparse vector has to be recovered from an underdetermined system of linear equations. In many applications, however, the elements of the sparse vector are drawn from a finite set. Adapted algorithms incorporating this additional knowledge are required for the discrete-valued setup. In this paper, turbo-based algorithms for both cases are elucidated and analyzed...
This paper deals with the sub-Nyquist sampling of analog multiband signals. The Modulated Wideband Converter (MWC) is a promising compressive sensing architecture, foreseen to be able to break the usual compromise between bandwidth, noise figure and energy consumption of Analog-to-Digital Converters. The pseudorandom code sequences yielding the sensing matrix are yet the bottleneck of it. Our contributions...
Time delay estimation in multipath environments is important for many applications in the fields of radar, sonar, communication and so on. In this paper, we propose a method based on compressed sensing for time delay estimation. In the proposed method, the signal in time domain is first converted into frequency domain, which tends to convert the problem to a parameter estimation of sinusoidal signals...
This paper presents a novel method for joint frequency and angle estimation of incoming signals. Two-layers compressed sensing method is used to obtain direction wave number and frequency parameters respectively. The algorithm provides high resolution and accuracy in both frequency and direction of arrival (DOA) estimation over the frequency range under the condition of small signal to noise ratio...
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