Low Complexity Single-Snapshot DoA Estimation via Bayesian Compressive Sensing

The problem of single-snapshot direction of arrival (DoA) estimation with antenna arrays has been considered. A sectorized approach based on Bayesian Compressive Sensing (BCS) has been proposed. In this method, the angular space is discretized, defining many non-overlapping small grids which cover the desired large angular space. First, a BCS...

Analysis of Orthogonal Matching Pursuit for Compressed Sensing in Practical Settings

Orthogonal matching pursuit (OMP) is a widely used greedy algorithm for sparse signal recovery in compressed sensing (CS). Prior work on OMP, however, has only provided reconstruction guarantees under the assumption that the columns of the CS matrix have equal norms, which is unrealistic in many practical CS applications due to hardware...

A compressive sensing with photonic crystals enabled spectrometer for trace gas observation

Recently a spectrometer concept has been invented which uses compressive sensing in combination with photonic crystal filters. Here we present an adaption of this concept in push-broom configuration for earth observation. This implementation allows for a compact design, while maintaining a high spatial resolution and high signal-to-noise...

Structure-Aware Sparse Bayesian Learning-Based Channel Estimation for Intelligent Reflecting Surface-Aided MIMO

This paper presents novel cascaded channel estimation techniques for an intelligent reflecting surface-aided multiple-input multiple-output system. Motivated by the channel angular sparsity at higher frequency bands, the channel estimation problem is formulated as a sparse vector recovery problem with an inherent Kronecker structure. We solve...

Fusion of Data from Multiple Automotive Radars for High-Resolution DoA Estimation

High angular resolution is in high demand in automotive radar. To achieve a high azimuth resolution a large aperture antenna array is required. Although MIMO technique can be used to form larger virtual apertures, a large number of transmitter-receiver channels are needed, which is still technologically challenging and costly. To circumvent this...

Total Variation Compressive Sensing for Extended Targets in MIMO Radar

The problem of extended target cross-section estimation has been considered. A two-step method based on the Total Variation Compressive Sensing theory has been proposed to solve it. First, a coarse estimation of the target cross-section is performed with classical beamforming methods, and then Compressive Sensing algorithms have been applied...

State Estimation of Linear Systems With Sparse Inputs and Markov-Modulated Missing Outputs

In this paper, we consider the problem of estimating the states of a linear dynamical system whose inputs are jointly sparse and outputs at a few unknown time instants are missing. We model the missing data mechanism using a Markov chain with two states representing the missing and non-missing data. This mechanism with memory governed by the...

Sampling Graph Signals with Sparse Dictionary Representation

Graph sampling strategies require the signal to be relatively sparse in an alternative domain, e.g. bandlimitedness for reconstructing the signal. When such a condition is violated or its approximation demands a large bandwidth, the reconstruction often comes with unsatisfactory results even with large samples. In this paper, we propose an...

K-Space Trajectory Design for Reduced MRI Scan Time

The development of compressed sensing (CS) techniques for magnetic resonance imaging (MRI) is enabling a speedup of MRI scanning. To increase the incoherence in the sampling, a random selection of points on the k-space is deployed and a continuous trajectory is obtained by solving a traveling salesman problem (TSP) through these points. A...

Active Semi-Supervised Learning for Diffusions on Graphs

Diffusion-based semi-supervised learning on graphs consists of diffusing labeled information of a few nodes to infer the labels on the remaining ones. The performance of these methods heavily relies on the initial labeled set, which is either generated randomly or using heuristics. The first sometimes leads to unsatisfactory results because...

Fault Detection in Photovoltaic Arrays via Sparse Representation Classifier

In recent years, there has been an increasing interest in the integration of photovoltaic (PV) systems in the power grids. Although PV systems provide the grid with clean and renewable energy, their unsafe and inefficient operation can affect the grid reliability. Early stage fault detection plays a crucial role in reducing the operation and...

An Energy-Efficient Multi-Sensor Compressed Sensing System Employing Time-Mode Signal Processing Techniques

This paper presents the design of an ultra-low energy, rakeness-based compressed sensing (CS) system that utilizes time-mode (TM) signal processing (TMSP). To realize TM CS operation, the presented implementation makes use of monostable multivibrator based analog-to-time converters, fixed-width pulse generators, basic digital gates and an...

Information distances for radar resolution analysis

A stochastic approach to resolution based on information distances computed from the geometry of data models which is characterized by the Fisher information is explored. Stochastic resolution includes probability of resolution and signal-to-noise ratio (SNR). The probability of resolution is assessed from a hypothesis test by exploiting...

Signal-to-Noise-Ratio Analysis of Compressive Data Acquisition

Data acquisition in compressive sensing (CS) is commonly believed to be less complicated, and even less costly, while performing agreeably. There is a major lack of measureable foundations supporting this optimism as the performance and complexity of a CS sensor have hardly been quantified. We aim to fill the gap by computing the performance...

Single Channel RF Signal Recovery for Nyquist Folding Receiver

This paper presents a method for exploiting wideband spectral information of real-valued radio frequency (RF) signals using the Nyquist Folding Receiver architecture. A new system model based on a symmetric modulation matrix is introduced so that the frequency band of the real input signals can be estimated without in-phase and quadrature...

Bound on the estimation grid size for sparse reconstruction in direction of arrival estimation

A bound for sparse reconstruction involving both the signal-to-noise ratio (SNR) and the estimation grid size is presented. The bound is illustrated for the case of a uniform linear array (ULA). By reducing the number of possible sparse vectors present in the feasible set of a constrained ℓ1-norm minimization problem, ambiguities in the...

Stochastic resolution analysis of co-prime arrays in radar

Resolution from co-prime arrays and from a full ULA of the size equal to the virtual size of co-prime arrays is investigated. We take into account not only the resulting beam width but also the fact that fewer measurements are acquired by co-prime arrays. This fact is relevant in compressive acquisition typical for compressive sensing. Our...