Publications

Refine Results

(Filters Applied) Clear All

Efficient reconstruction of block-sparse signals

Published in:
IEEE Statistical Signal Processing Workshop, 28-30 June 2011.

Summary

In many sparse reconstruction problems, M observations are used to estimate K components in an N dimensional basis, where N > M ¿ K. The exact basis vectors, however, are not known a priori and must be chosen from an M x N matrix. Such underdetermined problems can be solved using an l2 optimization with an l1 penalty on the sparsity of the solution. There are practical applications in which multiple measurements can be grouped together, so that K x P data must be estimated from M x P observations, where the l1 sparsity penalty is taken with respect to the vector formed using the l2 norms of the rows of the data matrix. In this paper we develop a computationally efficient block partitioned homotopy method for reconstructing K x P data from M x P observations using a grouped sparsity constraint, and compare its performance to other block reconstruction algorithms.
READ LESS

Summary

In many sparse reconstruction problems, M observations are used to estimate K components in an N dimensional basis, where N > M ¿ K. The exact basis vectors, however, are not known a priori and must be chosen from an M x N matrix. Such underdetermined problems can be solved...

READ MORE

A multi-sensor compressed sensing receiver: performance bounds and simulated results

Published in:
43rd Asilomar Conf. on Signals, Systems, and Computers, 1-4 November 2009, pp. 1571-1575.

Summary

Multi-sensor receivers are commonly tasked with detecting, demodulating and geolocating target emitters over very wide frequency bands. Compressed sensing can be applied to persistently monitor a wide bandwidth, given that the received signal can be represented using a small number of coefficients in some basis. In this paper we present a multi-sensor compressive sensing receiver that is capable of reconstructing frequency-sparse signals using block reconstruction techniques in a sensor-frequency basis. We derive performance bounds for time-difference and angle of arrival (AoA) estimation of such a receiver, and present simulated results in which we compare AoA reconstruction performance to the bounds derived.
READ LESS

Summary

Multi-sensor receivers are commonly tasked with detecting, demodulating and geolocating target emitters over very wide frequency bands. Compressed sensing can be applied to persistently monitor a wide bandwidth, given that the received signal can be represented using a small number of coefficients in some basis. In this paper we present...

READ MORE

Compressed sensing arrays for frequency-sparse signal detection and geolocation

Published in:
Proc. of the 2009 DoD High Performance Computing Modernization Program Users Group Conf., HPCMP-UGC, 15 June 2009, pp. 297-301.

Summary

Compressed sensing (CS) can be used to monitor very wide bands when the received signals are sparse in some basis. We have developed a compressed sensing receiver architecture with the ability to detect, demodulate, and geolocate signals that are sparse in frequency. In this paper, we evaluate detection, reconstruction, and angle of arrival (AoA) estimation via Monte Carlo simulation and find that, using a linear 4- sensor array and undersampling by a factor of 8, we achieve near-perfect detection when the received signals occupy up to 5% of the bandwidth being monitored and have an SNR of 20 dB or higher. The signals in our band of interest include frequency-hopping signals detected due to consistent AoA. We compare CS array performance using sensor-frequency and space-frequency bases, and determine that using the sensor-frequency basis is more practical for monitoring wide bands. Though it requires that the received signals be sparse in frequency, the sensor-frequency basis still provides spatial information and is not affected by correlation between uncompressed basis vectors.
READ LESS

Summary

Compressed sensing (CS) can be used to monitor very wide bands when the received signals are sparse in some basis. We have developed a compressed sensing receiver architecture with the ability to detect, demodulate, and geolocate signals that are sparse in frequency. In this paper, we evaluate detection, reconstruction, and...

READ MORE

Showing Results

1-3 of 3