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Based on affine projection algorithm (APA) in adaptive filtering and the technique of parallel computing, we propose a novel algorithm called ℓ0-APA with its parallel implementation for sparse system identification and sparse signal recovery. For sparse system identification, parallel ℓ0-APA can serve as an effective approach for practical hardware implementation, since it lowers the requirement on...
Compressive spectral imaging (CSI) systems capture the 3D spatio-spectral information of a scene by measuring 2D focal plane array (FPA) coded projections. A reconstruction algorithm exploiting the sparsity of the signal is then used to recover the underlying hyperspectral scene. CSI systems use a set of binary coded apertures, commonly realized through photomasks, to modulate the spatial characteristics...
A new approach for sparse nonstationary signal reconstruction based on multiple windows is introduced. Signals which are localizable in the time-frequency (TF) domain give rise to sparsity in the same domain. When combined, sparse reconstructions, applied to randomly sampled data and corresponding to different selected windows, provide enhanced TF signature estimation. Among possible orthogonal windows,...
To solve the problem of joint sparsity pattern recovery in a decentralized network, we propose an algorithm named decentralized and collaborative subspace pursuit (DCSP). The basic idea of DCSP is to embed collaboration among nodes and fusion strategy into each iteration of the standard subspace pursuit (SP) algorithm. In DCSP, each node collaborates with several of its neighbors by sharing high-dimensional...
We explore the problem of deterministically constructing frames and matrices with low coherence, which arises in areas such as compressive sensing, spherical codes, and MIMO communications. In particular, we present a generalization of the familiar harmonic frame by selecting a subset of rows of the generalized discrete Fourier transform matrix over finite groups. We apply our methods to the group...
Consider the scenario where a receiver acquires information (data) corrupted by interference and noise. Both the information and interference have a sparse structure. To fully exploit the individual sparse structure of the information and interference, the joint interference mitigation and data recovery is formulated as a sparse maximum likelihood estimation (MLE) problem which maximizes the associated...
An improved algorithm for the reconstruction of electrocardiogram signals in compressive sensing is proposed. The algorithm is based on the minimization of a mixed pseudonorm of first- and second-order differences of the signal. Locations of QRS segments are estimated using a technique based on signal derivatives and the Hilbert transform, and they are used to implement the mixed pseudonorm. Simulation...
Through-the-wall radar imaging aims at determining the locations and velocities of obscured targets. The slow velocities of indoor targets are in particular difficult to detect and estimate. It is shown by theoretical considerations and simulation that indirect propagation paths contain significant information on the target movements, which can be utilized for improved sensing. We propose a compressive...
Parameter estimation from compressively sensed signals has recently received some attention. We here also consider this problem in the context of frequency sparse signals which are encountered in many application. Existing methods perform the estimation using finite dictionaries or incorporate various interpolation techniques to estimate the continuous frequency parameters. In this paper, we show...
In this paper we formulate a Multi-Armed Bandit Compressive Spectrum Sensing (MAB-CSS) problem, in which a Cognitive Receiver (CR) decides dynamically how to best sense N sub-channels states, that switch from being occupied to being available as independent and statistically identical Markov chains. We assume that the CR is endowed with K CSS samplers each sensing an arbitrary mixture of the N signals...
We introduce novel algorithms for the joint recovery of an ensemble of signals that live on a smooth manifold from their under sampled measurements. Unlike current methods that are designed to recover a single signal assuming perfect knowledge of the manifold model, the proposed algorithms exploit similarity between the signals without prior knowledge of the underlying manifold structure. Our first...
Sparse-signal processing (SSP) is interpreted in this paper as a sparse model-based refinement of typical steps in radar processing. Matched filtering remains vital within SSP but joined with radar detection promoting the sparsity. Realistic measurements are also supported in SSP by using MonteCarlo (MC) methods. MC-based SSP promotes the sparsity by detection-driven MC-sampling that also improves...
The performance of existing approaches to the recovery of frequency-sparse signals from compressed measurements is limited by the coherence of required sparsity dictionaries and the discretization of frequency parameter space. In this paper, we adopt a parametric joint recovery-estimation method based on model selection in spectral compressive sensing. Numerical experiments show that our approach...
In this paper, we consider the problem of sensor management for target tracking in a wireless sensor network (WSN). To determine the set of sensors that have the most information, we develop a probabilistic sensor management scheme based on the concepts developed in compressive sensing. In the proposed scheme, each senor node decides whether it should transmit its observation via multiple access channels...
An effective complex multitask Bayesian compressive sensing (CMT-BCS) algorithm is proposed to recover sparse or group sparse complex signals. The existing multitask Bayesian compressive sensing (MT-CS) algorithm is powerful in recovering multiple real-valued sparse solutions. However, a large class of sensing problems deal with complex values. A simple approach, which decomposes a complex value into...
In this work we combing two novelty in the area of Analog Information Converter based on Compressed Sensing. A new architecture, the Spread Spectrum Random Modulation PreIntegration and a new design flow, the rakeness based design of a Compressed Sensing system. We demonstrate that combining these approaches produces a strong reduction of the internal chipping frequency in the sensing coupled with...
We propose a method for signal recovery in compressed sensing when measurements can be highly corrupted. It is based on lp minimization for 0 < p ≤ 1. Since it was shown that ℓp minimization performs better than ℓ1 minimization when there are no large errors, the proposed approach is a natural extension to compressed sensing with corruptions. We provide a theoretical justification of this idea,...
This paper deals with the problem of estimating the Directions of Arrival (DOA) of multiple source signals from a single observation of an array data. In particular, an estimation algorithm based on the emerging theory of Compressed Sensing (CS) is analyzed and its statistical properties are investigated. We show that, unlike the classical Fourier beamformer, a CS-based beamformer (CSB) has some desirable...
There has been growing interest in performing signal processing tasks directly on compressive measurements, e.g. low-dimensional linear measurements of signals taken with Gaussian random vectors. In this paper, we present a highly efficient algorithm to recover the covariance matrix of high-dimensional data from compressive measurements. We show that, as the number of data samples increases, the eigenvectors...
In this paper, we propose effective coprime array configurations in which the minimum interelement spacing is much larger than the typical half-wavelength requirement. Such configurations are important in many applications where the half-wavelength requirement cannot be met due to the physical sensors size or to avoid spatial oversampling in wideband operations. The application of such coprime arrays...
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