# Target detection performance bounds in compressive imaging

@article{Krishnamurthy2012TargetDP, title={Target detection performance bounds in compressive imaging}, author={Kalyani Krishnamurthy and Rebecca M. Willett and Maxim Raginsky}, journal={EURASIP Journal on Advances in Signal Processing}, year={2012}, volume={2012}, pages={1-19} }

This article describes computationally efficient approaches and associated theoretical performance guarantees for the detection of known targets and anomalies from few projection measurements of the underlying signals. The proposed approaches accommodate signals of different strengths contaminated by a colored Gaussian background, and perform detection without reconstructing the underlying signals from the observations. The theoretical performance bounds of the target detector highlight…

## 13 Citations

Compressive measurement designs for estimating structured signals in structured clutter: A Bayesian Experimental Design approach

- Computer Science2013 Asilomar Conference on Signals, Systems and Computers
- 2013

Experimental results on synthetic data demonstrate that the proposed approach outperforms traditional random compressive measurement designs, which are agnostic to the prior information, as well as several other knowledge-enhanced sensing matrix designs based on more heuristic notions.

Information-Theoretic Compressive Measurement Design for Micro-Doppler Signatures

- Computer Science2020 Sensor Signal Processing for Defence Conference (SSPD)
- 2020

In this work, we utilise gradient-ascent multi-objective optimisation within an information-theoretic compressive sensing framework to classify micro-Doppler (m-D) signatures in the presence of…

Noise Folding in Compressed Sensing

- Computer ScienceIEEE Signal Processing Letters
- 2011

For the vast majority of measurement schemes employed in compressed sensing, the two models are equivalent with the important difference that the signal-to-noise ratio (SNR) is divided by a factor proportional to p/n, where p is the dimension of the signal and n is the number of observations.

Compressive phase-only filtering - pattern recognition at extreme compression rates

- Computer ScienceArXiv
- 2016

We introduce a compressive pattern recognition method for non-adaptive WalshHadamard or discrete noiselet-based compressive measurements and show that images measured at extremely high compression…

Sparse image measurement with an optical single-pixel detector using various schemes of image sampling

- Mathematics2015 17th International Conference on Transparent Optical Networks (ICTON)
- 2015

The optimal measurement and reconstruction basis is established in order to obtain the best quality of the image reconstruction with possibly few measurements and is introduced into the experimental single-pixel camera set-up for imaging in the visible wavelength range.

Target-detection strategies

- Computer Science
- 2012

Eight inventive paradigms, each with deep philosophical underpinnings, are described in relation to their effect on target detector design, including neural networks, deformable templates, and adaptive filtering.

Gradient of Mutual Information in Linear Vector Gaussian Channels in the Presence of Input Noise

- Computer Science2020 28th European Signal Processing Conference (EUSIPCO)
- 2021

It is demonstrated that the derived expressions can outperform approximate gradient terms when integrated within a gradient ascent multi-objective optimisation approach.

Sparsity and Structure in Hyperspectral Imaging : Sensing, Reconstruction, and Target Detection

- Physics, Environmental ScienceIEEE Signal Processing Magazine
- 2014

Hyperspectral imaging is a powerful technology for remotely inferring the material properties of the objects in a scene of interest with much more accuracy than is possible with conventional three-color images.

Semi-supervised and unsupervised kernel-based novelty detection with application to remote sensing images

- Computer Science
- 2014

This thesis addresses the development of methods for novelty detection and one-class classification with few or no labeled information and proposes a method seeking a sparse and low-rank representation of the data mapped in a non-linear feature space.

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