Quantum-Classical Solution Methods for Binary Compressive Sensing Problems

Compressive sensing is a signal processing technique used to acquire and reconstruct sparse signals using significantly fewer measurement samples. Compressive sensing requires finding the most sparse solution to an underdetermined linear system, which is an NP-hard problem and as a consequence in practise is only solved approximately. In our...

A Compressed Sensing Algorithm for Magnetic Dipole Localization

This paper proposes an algorithm to localize a magnetic dipole using a limited number of noisy measurements from magnetic field sensors. The algorithm is based on the theory of compressed sensing, and exploits the sparseness of the magnetic dipole in space. Beforehand, a basis consisting of magnetic dipole fields belonging to individual dipoles...

An overview of joint activities on computational imaging and compressive sensing systems by NATO SET-232

Conventional electro-optical and infrared (EO/IR) systems (i.e., active, passive, multiband and hyperspectral) capture an image by optically focusing the incident light at each of the millions of pixels in a focal plane array. The optics and the focal plane are designed to efficiently capture desired aspects (like spectral content, spatial...

On pre-whitening and accuracy in DOA estimation by sparse signal processing on beamformed data

Design and analysis of compressed sensing radar detectors

We consider the problem of target detection from a set of Compressed Sensing (CS) radar measurements corrupted by additive white Gaussian noise. We propose two novel architectures and compare their performance by means of Receiver Operating Characteristic (ROC) curves. Using asymptotic arguments and the Complex Approximate Message Passing (CAMP)...

Asymptotic analysis of complex LASSO via complex approximate message passing (CAMP)

Recovering a sparse signal from an undersampled set of random linear measurements is the main problem of interest in compressed sensing. In this paper, we consider the case where both the signal and the measurements are complex-valued. We study the popular recovery method of l1- regularized least squares or LASSO.While several studies have shown...

Compressive Sensing Radar: Simulation and Experiments for Target Detection

In this paper the performance of a combined Constant False Alarm Rate (CFAR) Compressive Sensing (CS) radar detector is investigated Using the properties of the Complex Approximate Message Passing (CAMP) algorithm, it is demonstratedthat the behavior of the CFAR processor can be separated from that of the non-linear l1-norm recovery, thus...

Compressive sensing for high resolution profiles with enhanced Doppler performance

In this paper we demonstrate how Compressive Sensing (CS) can be used in pulse-Doppler radars to improve the Doppler performance while preserving range resolution. We investigate here two types of stepped frequency waveforms, the coherent frequency bursts and successive frequency ramps, which can be used for high resolution range profiling....

Compressive CFAR radar detection

In this paper we develop the first Compressive Sensing (CS) adaptive radar detector. We propose three novel architectures and demonstrate how a classical Constant False Alarm Rate (CFAR) detector can be combined with ℓ1-norm minimization. Using asymptotic arguments and the Complex Approximate Message Passing (CAMP) algorithm we characterize the...

CS Radar Imaging via Adaptive CAMP

In this paper we present results on application of Compressive Sensing (CS) to high resolution radar imaging and pro- pose the adaptive Complex Approximate Message Passing (CAMP) algorithm for image reconstruction. CS provides a theoretical framework that guarantees, under certain assumptions, reconstruction of sparse signals from many fewer...

Single Image Super Resolution via Sparse Reconstruction

High resolution sensors are required for recognition purposes. Low resolution sensors, however, are still widely used. Software can be used to increase the resolution of such sensors. One way of increasing the resolution of the images produced is using multi-frame super resolution algorithms. Limitation of these methods are that the...

False Alarm Probability Estimation for Compressive Sensing Radar

In this paper false alarm probability (FAP) estimation of a radar using Compressive Sensing (CS) in the frequency domain is investigated. Compressive Sensing is a recently proposed technique which allows reconstruction of sparse signal from sub-Nyquist rate measurements. The estimation of the FAP is based on an empirical model derived from...

Detection Performance of Compressive Sensing Applied to Radar

In this paper some results are presented on detection performance of radar using Compressive Sensing. Compressive sensing is a recently developed theory which allows reconstruction of sparse signals with a number of measurements much lower than implied by the Nyquist rate. In this work the behavior of detection and false alarm performance of a...

Compressive sensing for high resolution radar imaging

In this paper we present some preliminary results on the application of Compressive Sensing (CS) to high resolution radar imaging. CS is a recently developed theory which allows reconstruction of sparse signals with a number of measurements much lower than what is required by the Shannon sampling theorem. This method has already found its way in...