Results 1 
7 of
7
A subNyquist radar prototype: Hardware and algorithms
 IEEE Transactions on Aerospace and Electronic Systems, special issue on Compressed Sensing for Radar, Aug. 2012
"... Traditional radar sensing typically employs matched filtering between the received signal and the shape of the transmitted pulse. Matched filtering (MF) is conventionally carried out digitally, after sampling the received analog signals. Here, principles from classic sampling theory are generally em ..."
Abstract

Cited by 9 (7 self)
 Add to MetaCart
Traditional radar sensing typically employs matched filtering between the received signal and the shape of the transmitted pulse. Matched filtering (MF) is conventionally carried out digitally, after sampling the received analog signals. Here, principles from classic sampling theory are generally employed, requiring that the received signals be sampled at twice their baseband bandwidth. The resulting sampling rates necessary for correlationbased radar systems become quite high, as growing demands for target distinction capability and spatial resolution stretch the bandwidth of the transmitted pulse. The large amounts of sampled data also necessitate vast memory capacity. In addition, realtime data processing typically results in high power consumption. Recently, new approaches for radar sensing and estimation were introduced, based on the finite rate of innovation (FRI) and Xampling frameworks. Exploiting the parametric nature of radar signals, these techniques allow significant reduction in sampling rate, implying potential power savings, while maintaining the system’s estimation capabilities at sufficiently high signaltonoise ratios (SNRs). Here we present for the first time a design and implementation of an Xamplingbased hardware prototype that allows sampling of radar signals at rates much lower than Nyquist. We demonstrate by realtime analog experiments that our system is able to maintain reasonable recovery capabilities, while sampling radar signals that require sampling at a rate of about 30 MHz at a total rate of 1 MHz.
Compressive multiplexers for correlated signals
 in Proc. IEEE Asilomar Conf. on Sig. Sys. and Comp
, 2012
"... We present a general architecture for the acquisition of ensembles of correlated signals. The signals are multiplexed onto a single line by mixing each one against a different code and then adding them together, and the resulting signal is sampled at a high rate. We show that if the M signals, each ..."
Abstract

Cited by 3 (1 self)
 Add to MetaCart
We present a general architecture for the acquisition of ensembles of correlated signals. The signals are multiplexed onto a single line by mixing each one against a different code and then adding them together, and the resulting signal is sampled at a high rate. We show that if the M signals, each bandlimited to W/2 Hz, can be approximated by a superposition of R < M underlying signals, then the ensemble can be recovered by sampling at a rate within a logarithmic factor of RW (as compared to the Nyquist rate of MW). This sampling theorem shows that the correlation structure of the signal ensemble can be exploited in the acquisition process even though it is unknown a priori. The reconstruction of the ensemble is recast as a lowrank matrix recovery problem from linear measurements. The architectures we are considering impose a certain type of structure on the linear operators. Although our results depend on the mixing forms being random, this imposed structure results in a very different type of random projection than those analyzed in the lowrank recovery literature to date. 1
Localization through compressive sensing: A survey
 International Journal of Wireless Communications and Mobile Computing
, 2015
"... Abstract: User mobile device or for wireless node detection localization is a primary concern not only in normal days but especially during emergency situations. There is variety of useful and necessary applications related to localization and it is an important technology playing critical role in w ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
Abstract: User mobile device or for wireless node detection localization is a primary concern not only in normal days but especially during emergency situations. There is variety of useful and necessary applications related to localization and it is an important technology playing critical role in wireless communication. The conceptual point of view is to sense the localization (coordinates of the user) from a specific region of interest (ROI). For reducing the complexity and increasing efficiency, the data samples for location sensing is limited in a term of taking sparsity of the detected signal in known transformed domain by taking fewer data samples. This whole phenomenon is called compressive sensing. This paper introduces this technology especially in locationsensing and discusses the present techniques.
Compressed Subspace Matching on the Continuum
, 2014
"... We consider the general problem of matching a subspace to a signal in RN that has been observed indirectly (compressed) through a random projection. We are interested in the case where the collection of Kdimensional subspaces is continuously parameterized, i.e. naturally indexed by an interval from ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
(Show Context)
We consider the general problem of matching a subspace to a signal in RN that has been observed indirectly (compressed) through a random projection. We are interested in the case where the collection of Kdimensional subspaces is continuously parameterized, i.e. naturally indexed by an interval from the real line, or more generally a region of RD. Our main results show that if the dimension of the random projection is on the order of K times a geometrical constant that describes the complexity of the collection, then the match obtained from the compressed observation is nearly as good as one obtained from a full observation of the signal. We give multiple concrete examples of collections of subspaces for which this geometrical constant can be estimated, and discuss the relevance of the results to the general problems of template matching and source localization. 1
PulseDoppler Signal Processing With Quadrature Compressive Sampling
"... Quadrature compressive sampling (QuadCS) is a recently introduced subNyquist sampling scheme for effective acquisition of inphase and quadrature (I/Q) components of sparse radio frequency signals. In applications to pulseDoppler radars, the QuadCS outputs can be arranged into a twodimensional dat ..."
Abstract
 Add to MetaCart
(Show Context)
Quadrature compressive sampling (QuadCS) is a recently introduced subNyquist sampling scheme for effective acquisition of inphase and quadrature (I/Q) components of sparse radio frequency signals. In applications to pulseDoppler radars, the QuadCS outputs can be arranged into a twodimensional data format, in terms of slow time and virtual fast time, similar to that by Nyquist sampling. This paper develops a compressive sampling pulseDoppler (CoSaPD) processing scheme which performs Doppler estimation/detection and range estimation from the subNyquist data without recovering the Nyquist samples. The Doppler estimation is realized through a spectrum analyzer as in classical processing, whereas the detection is performed using the Doppler bin data. The range estimation is performed using sparse recovery algorithms only for the detected targets to reduce the computational load. A low detection threshold is used to improve the detection probability and the introduced false
HigherOrder Methods for LargeScale Optimization
, 2015
"... There has been an increased interest in optimization for the analysis of largescale data sets which require gigabytes or terabytes of data to be stored. A variety of applications originate from the fields of signal processing, machine learning and statistics. Seven representative applications are ..."
Abstract
 Add to MetaCart
(Show Context)
There has been an increased interest in optimization for the analysis of largescale data sets which require gigabytes or terabytes of data to be stored. A variety of applications originate from the fields of signal processing, machine learning and statistics. Seven representative applications are described below. Magnetic Resonance Imaging (MRI): A medical imaging tool used to scan the anatomy and the physiology of a body [80]. Image inpainting: A technique for reconstructing degraded parts of an image [14]. Image deblurring: Image processing tool for removing the blurriness of a
1Technical Report: Observability
"... Recovery of the initial state of a highdimensional system can require a large number of measurements. In this paper, we explain how this burden can be significantly reduced when randomized measurement operators are employed. Our work builds upon recent results from Compressive Sensing (CS). In par ..."
Abstract
 Add to MetaCart
(Show Context)
Recovery of the initial state of a highdimensional system can require a large number of measurements. In this paper, we explain how this burden can be significantly reduced when randomized measurement operators are employed. Our work builds upon recent results from Compressive Sensing (CS). In particular, we make the connection to CS analysis for random block diagonal matrices. By deriving Concentration of Measure (CoM) inequalities, we show that the observability matrix satisfies the Restricted Isometry Property (RIP) (a sufficient condition for stable recovery of sparse vectors) under certain conditions on the state transition matrix. For example, we show that if the state transition matrix is unitary, and if independent, randomlypopulated measurement matrices are employed, then it is possible to uniquely recover a sparse highdimensional initial state when the total number of measurements scales linearly in the sparsity level (the number of nonzero entries) of the initial state and logarithmically in the state dimension. We further extend our RIP analysis for scaled unitary and symmetric state transition matrices. We support our analysis with a case study of a twodimensional diffusion process.