Corpus ID: 15480420

The Compressive Matched Filter

@inproceedings{Davenport2006TheCM,
  title={The Compressive Matched Filter},
  author={M. Davenport and M. Wakin and Richard Baraniuk},
  year={2006}
}
The recently introduced theory of compressed sensing enables the reconstruction of sparse or compressible signals from a small set of nonadaptive, linear measurements. If properly chosen, the number of measurements can be much smaller than the number of Nyquist rate samples. Interestingly, it has been shown that random projections are a satisfactory measurement scheme. This has inspired the design of physical systems that directly implement similar measurement schemes. However, despite the… Expand
Compressive Sampling for Signal Detection
  • J. Haupt, R. Nowak
  • Mathematics, Computer Science
  • 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07
  • 2007
TLDR
A generalized restricted isometry property (GRIP) is introduced, which guarantees that angles are preserved, in addition to the usual norm preservation, by CS, and is leveraged to derive error bounds for a CS matched filtering scheme, and to show that the scheme is robust to signal mismatch. Expand
Compressive measurements detection without reconstruction
  • Junhu Ma, L. Gan, H. Liao
  • Computer Science
  • 2018 International Conference on Electronics Technology (ICET)
  • 2018
TLDR
A novel algorithm to study the problem of signal detection from the compressive measurements by designing a new random measurement matrix from a joint random Gaussian matrix and dictionary basis matrix and achieves deciding on the presence/absence of the signal. Expand
Compressive Sensing and Fast Simulations: Applications to Radar Detection
TLDR
This thesis investigates sampling methods that can deal with the problems of processing complexity as well as analysis (or performance evaluation) extremely efficiently by reducing the required amount of samples by efficiently sampling the underlying probability density function. Expand
Image Reconstruction, Classification, and Tracking for Compressed Sensing Imaging and Video
TLDR
A tracking algorithm is described and evaluated which can hold target vehicles at very high levels of compression where reconstruction of video frames fails and the perceptual quality and peak signal to noise ratio (PSNR) of reconstructed frames is improved. Expand
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 canExpand
Direct tracking from compressive imagers: A proof of concept
  • Henry Braun, P. Turaga, A. Spanias
  • Mathematics, Computer Science
  • 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • 2014
TLDR
The proof-of-concept tracker described here uses a particle filter with a likelihood update based on a “smashed filter” which estimates correlation directly, avoiding the reconstruction step and has been successfully tested on sequences of moving cars in the PETS2000 dataset. Expand
Compressive sensing of direct sequence spread spectrum signals
TLDR
Compressive Sensing methods for Direct Sequence Spread Spectrum signals are introduced and are evaluated with DSSS signals generated using Maximum-length Sequences and Binary Phase-Shift-Keying modulation at varying signal-to-noise and compression ratios. Expand
A Frequency-Domain Adaptive Matched Filter for Active Sonar Detection
TLDR
The experimental results indicate that the FDAMF does improve the performance of the MF, and can adapt to actual interference in a way, and the TRC-IS preprocessing method works well in an actual noisy ocean environment. Expand
Compressive Mahalanobis classifiers
  • P. Barbano, R. Coifman
  • Computer Science
  • 2008 IEEE Workshop on Machine Learning for Signal Processing
  • 2008
TLDR
A new framework for detection/estimation designed to avoid the loss of salient information in the process of reducing the dimensionality of digitized data is proposed, which combines a first step performed at the data acquisition level with an energy based algorithm aimed at defining a global metric on the data. Expand
Optical flow for compressive sensing video reconstruction
TLDR
A reconstruction method is presented which fully utilizes optical flow information to increase the quality of reconstruction and the performance of the algorithm on existing datasets is evaluated. Expand
...
1
2
...

References

SHOWING 1-10 OF 24 REFERENCES
Sparse Signal Detection from Incoherent Projections
TLDR
This paper demonstrates how CS principles can solve signal detection problems given incoherent measurements without ever reconstructing the signals involved, and proposes an incoherent detection and estimation algorithm (IDEA) based on matching pursuit. Expand
Extensions of compressed sensing
TLDR
The results show that, when appropriately deployed in a favorable setting, the CS framework is able to save significantly over traditional sampling, and there are many useful extensions of the basic idea. Expand
Active learning versus compressive sampling
TLDR
It is shown that for certain classes of piecewise constant signals and high SNR regimes both CS and AS are near optimal, the first evidence that shows that compressive sampling, which is non-adaptive, cannot be significantly outperformed by any other method (including adaptive sampling procedures), even in the presence of noise. Expand
Random Filters for Compressive Sampling and Reconstruction
TLDR
A new technique for efficiently acquiring and reconstructing signals based on convolution with a fixed FIR filter having random taps, which is sufficiently generic to summarize many types of compressible signals and generalizes to streaming and continuous-time signals. Expand
Analog-to-Information Conversion via Random Demodulation
TLDR
This paper proposes a system that uses modulation, filtering, and sampling to produce a low-rate set of digital measurements, inspired by the theory of compressive sensing (CS), which states that a discrete signal having a sparse representation in some dictionary can be recovered from a small number of linear projections of that signal. Expand
Compressed sensing and best k-term approximation
The typical paradigm for obtaining a compressed version of a discrete signal represented by a vector x ∈ R is to choose an appropriate basis, compute the coefficients of x in this basis, and thenExpand
Signal Reconstruction From Noisy Random Projections
  • J. Haupt, R. Nowak
  • Mathematics, Computer Science
  • IEEE Transactions on Information Theory
  • 2006
TLDR
A practical iterative algorithm for signal reconstruction is proposed, and potential applications to coding, analog-digital (A/D) conversion, and remote wireless sensing are discussed. Expand
Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
  • E. Candès, T. Tao
  • Mathematics, Computer Science
  • IEEE Transactions on Information Theory
  • 2006
TLDR
If the objects of interest are sparse in a fixed basis or compressible, then it is possible to reconstruct f to within very high accuracy from a small number of random measurements by solving a simple linear program. Expand
An Architecture for Compressive Imaging
TLDR
This paper proposes algorithms and hardware to support a new theory of compressive imaging based on a new digital image/video camera that directly acquires random projections of the signal without first collecting the pixels/voxels. Expand
SIGNAL RECOVERY FROM PARTIAL INFORMATION VIA ORTHOGONAL MATCHING PURSUIT
This article demonstrates theoretically and empirically that a greedy algorithm called Orthogonal Matching Pursuit (OMP) can reliably recover a signal with m nonzero entries in dimension d given O(mExpand
...
1
2
3
...