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A variety of constraints exist on the design of a wireless tele-monitoring system of bio-signals. In order to reduce on-chip energy consumption or extend sensor life, recorded signals are usually compressed before transmission. Compressed sensing (CS) is promising to as a low-power compression framework to improve energy efficiency. Its performance is largely determined by the characteristic of sensing...
From a numerical analysis perspective, assessing the robustness of ℓ1-minimization is a fundamental issue in compressed sensing and sparse regularization. Yet, the recovery guarantees available in the literature usually depend on a priori estimates of the noise, which can be very hard to obtain in practice, especially when the noise term also includes unknown discrepancies between the finite model...
With the increase of the imaging resolution, the resulting enormous amount of sampling raw data aggravates transmission and storage load for multi-channel synthetic aperture radar (SAR) system. Considering the fact that the correlation among the dual-channel SAR images is high, we propose a Bayesian compressive sensing (BCS) based SAR imaging algorithm for ground moving targets indication (GMTI) system,...
Energy efficiency and reliability are a pair of the most important factors in wireless sensor networks (WSNs). Recent research results have demonstrated that the previously suggested scheme of the joint network-coding-compressive-sensing can greatly improve the energy efficiency and the communication reliability of WSNs. In this paper, we propose a practical scheme, Non-Binary Joint Network-Coding...
Distributed channel estimation (DCE) is one of the core research topics in wireless sensor networks (WSNs). Under the hypothesis that channel parameters can be modeled as a sparse system, DCE based on compressed sensing (CS) is an effective approach to channel estimation. Among all the existing CS-DCE schemes, every node must store a sensing matrix whose size will increase with the number of channel...
In this work, we consider the joint sparse support recovery problem where the goal is to recover the common support of multiple joint sparse vectors from their compressive, linear measurements. We propose a Rényi Divergence based Covariance Matching Pursuit (RD-CMP) algorithm which recovers the common support of the joint sparse signals as the hyperparameters of a joint sparsity inducing Gaussian...
The simple structure of compressed sensing (CS) lead to low complexity encryption, which is fascinating for nodes with limited resources in the wireless network. In this paper, we analyze the performance of the encryption method based on compressed sensing, and prove that the encryption can achieve the information theory security. The effect of the sparsity and the signal noise ratio (SNR) on the...
Introducing compressive sensing theory to solve multiple targets localization problem is a promising method by only considering received signal's power. In previous researches, the targets are assumed to be positioned into an regular mesh grid and restoration model according to compressive underdetermined equation is built to recover the positions of unknown targets. However, it is inconvenient to...
The emergency of compressed sensing breaks the bottleneck of traditional Nyquist theory and results in various sub-Nyquist sampling architectures. As a representative, the random pulse-position-modulation analog-to-digital converter (PPM ADC) combines compressed sensing techniques with time-domain signal processing to effectively leverage the power efficiency. For this frame, period random sampling...
In this paper, conventional modal wavefront reconstruction is compared with compressed wavefront sensing to reconstruct freeform surface profiles using the Shack-Hartmann wavefront sensor. The modal wavefront reconstruction represents the phase or the wavefront in the Zernike domain. The compressed wavefront sensing method based on the sparse Zernike representation (SPARZER) represents the phase slopes...
Crowdsensing systems collect large-scale sensor data from mobile devices to provide a wide-area view of phenomena including traffic, noise and air pollution. Because such data often exhibits sparse structure, it is natural to apply compressive sensing (CS) for data sampling and recovery. However in practice, crowd participants are often distributed highly unevenly across the sensing area, and thus...
In this preliminary work, we study the problem of {\it distributed} authentication in wireless networks. Specifically, we consider a system where multiple Bob (sensor) nodes listen to a channel and report their {\it correlated} measurements to a Fusion Center (FC) which makes the ultimate authentication decision. For the feature- based authentication at the FC, channel impulse response has been utilized...
In this paper, we propose a generalized expectation consistent signal recovery algorithm to estimate the signal x from the nonlinear measurements of a linear transform output z = Ax. This estimation problem has been encountered in many applications, such as communications with front-end impairments, compressed sensing, and phase retrieval. The proposed algorithm extends the prior art called generalized...
A solution for the completely blind sensing problem of determining the minimum number of measurements sufficient to recover multi-band signals without any spectral information beside an upper bound on the measure of the whole support set in the frequency domain is presented. The number of measurements sufficient for reconstruction is provided, as well as a tight converse bound. Results show that a...
The realisation of sensing modalities based on the principles of compressed sensing is often hindered by discrepancies between the mathematical model of its sensing operator, which is necessary during signal recovery, and its actual physical implementation, which can amply differ from the assumed model. In this paper we tackle the bilinear inverse problem of recovering a sparse input signal and some...
Compressed sensing (CS) with prior information concerns the problem of reconstructing a sparse signal with the aid of a similar signal which is known beforehand. We consider a new approach to integrate the prior information into CS via maximizing the correlation between the prior knowledge and the desired signal. We then present a geometric analysis for the proposed method under sub-Gaussian measurements...
In this paper, we study the recovery of a signal from a collection of unlabeled and possibly noisy measurements via a measurement matrix with random i.i.d. Gaussian components. We call the measurements unlabeled since their order is missing, namely, it is not known a priori which elements of the resulting measurements correspond to which row of the measurement matrix. We focus on the special case...
Unlike compressive sensing where the measurement outputs are assumed to be real-valued and have infinite precision, in one-bit compressive sensing, measurements are quantized to one bit, their signs. In this work, our contributions are as follows: 1. We show how to recover the support of sparse high-dimensional vectors in the 1-bit compressive sensing framework with an asymptotically near-optimal...
In the problem of compressive phase retrieval, one wants to recover an approximately k-sparse signal x ∊ Cn, given the magnitudes of the entries of ϕx, where ϕ ∊ Cm×n This problem has received a fair amount of attention, with sublinear time algorithms appearing in [CBJC14], [PLR14], [YLPR15]. In this paper we further investigate the direction of sublinear decoding for real signals by giving a recovery...
In [1], a sharp phase transition has been numerically observed when a constrained convex procedure is used to solve the corrupted sensing problem. In this paper, we present a theoretical analysis for this phenomenon. Specifically, we establish the threshold below which this convex procedure fails to recover signal and corruption with high probability. Together with the work in [1], we prove that a...
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