Compressive sensing meets noise radar

Mahesh C. Shastry, Ram Mohan Narayanan, Muralidhar Rangaswamy

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

In this chapter, we discuss how noise radar systems are suitable for realizing practically the promises of compressive sensing in radar imaging, in general, and in urban-sensing applications, in particular. Noise radar refers to radio frequency imaging systems that employ transmit signals that are generated to resemble random noise waveforms. Noise radar has recently been successfully applied to urban sensing applications such as through-the-wall sensing (Amin 2011). Recent advances in the field of compressive sensing provide us with techniques to overcome the challenges of waveform design, sampling, and bandwidth constraints. We review existing literature related to these problems and present new results that enable for us to leverage compressive sensing and sparsity to improve noise radar systems. We model compressively sampled noise radar imaging as a problem of inverting linear system with a circulant random system matrix. We demonstrate the feasibility of this model by applying it to experimental data acquired using a millimeter wave ultrawideband noise radar system. Our principal contributions lie in developing theory and algorithms for imaging and detection strategies in compressively sampled noise radar imaging. We outline an approach based on extreme value statistics that works by empirically estimating the distribution of the residue of instances of the estimation algorithm. False alarms are treated as statistically rare events for estimating event probabilities in the compressive detection problem. We extrapolate the distribution of the residue from a small number of recovery instances to calibrate compressive noise radar systems. For deploying compressively sensed noise radar systems in real applications, it is necessary to develop convenient approaches to calibrate and characterize recovery performance.

Original languageEnglish (US)
Title of host publicationCompressive Sensing for Urban Radar
PublisherCRC Press
Pages429-459
Number of pages31
ISBN (Electronic)9781466597853
ISBN (Print)9781466597846
DOIs
StatePublished - Jan 1 2017

Fingerprint

Spurious signal noise
Radar systems
radar
Radar imaging
imaging radar
Recovery
Millimeter waves
Ultra-wideband (UWB)
Imaging systems
waveforms
estimating
Linear systems
recovery
Statistics
Sampling
Bandwidth
Imaging techniques
false alarms
linear systems
random noise

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Earth and Planetary Sciences(all)
  • Physics and Astronomy(all)

Cite this

Shastry, M. C., Narayanan, R. M., & Rangaswamy, M. (2017). Compressive sensing meets noise radar. In Compressive Sensing for Urban Radar (pp. 429-459). CRC Press. https://doi.org/10.1201/b17252
Shastry, Mahesh C. ; Narayanan, Ram Mohan ; Rangaswamy, Muralidhar. / Compressive sensing meets noise radar. Compressive Sensing for Urban Radar. CRC Press, 2017. pp. 429-459
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Shastry, MC, Narayanan, RM & Rangaswamy, M 2017, Compressive sensing meets noise radar. in Compressive Sensing for Urban Radar. CRC Press, pp. 429-459. https://doi.org/10.1201/b17252

Compressive sensing meets noise radar. / Shastry, Mahesh C.; Narayanan, Ram Mohan; Rangaswamy, Muralidhar.

Compressive Sensing for Urban Radar. CRC Press, 2017. p. 429-459.

Research output: Chapter in Book/Report/Conference proceedingChapter

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Shastry MC, Narayanan RM, Rangaswamy M. Compressive sensing meets noise radar. In Compressive Sensing for Urban Radar. CRC Press. 2017. p. 429-459 https://doi.org/10.1201/b17252