Compressive noise radar for urban sensing

Mahesh C. Shastry; Ram Mohan Narayanan; Muralidhar Rangaswamy

doi:10.1109/SAM.2014.6882448

Compressive noise radar for urban sensing

Mahesh C. Shastry, Ram Mohan Narayanan, Muralidhar Rangaswamy

Institute for Computational and Data Sciences (ICDS)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Ultra-wideband (UWB) noise-like transmit waveforms are ideally suited for implementing compressive radar range-profile imaging systems. Due to useful properties such as robustness to interference, simplicity of hardware design, and high range resolution, UWB noise radar has been successfully applied to urban-sensing scenarios. Compressive noise radar systems can achieve resolutions comparable to conventional systems at lower sampling rates, thus enabling super-resolution in radar imaging. In this paper, compressive noise radar imaging is experimentally shown to be feasible for urban sensing applications. S-band through - The-wall imaging results demonstrate the feasibility of using compressive noise radar for sensing obscured targets of interest in urban scenarios.

Original language	English (US)
Title of host publication	2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop, SAM 2014
Publisher	IEEE Computer Society
Pages	485-488
Number of pages	4
ISBN (Print)	9781479914814
DOIs	https://doi.org/10.1109/SAM.2014.6882448
State	Published - Jan 1 2014
Event	2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop, SAM 2014 - A Coruna, Spain Duration: Jun 22 2014 → Jun 25 2014

Publication series

Name	Proceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop
ISSN (Electronic)	2151-870X

Other

Other	2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop, SAM 2014
Country	Spain
City	A Coruna
Period	6/22/14 → 6/25/14

All Science Journal Classification (ASJC) codes

Signal Processing
Control and Systems Engineering
Electrical and Electronic Engineering

Access to Document

10.1109/SAM.2014.6882448

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Shastry, M. C., Narayanan, R. M., & Rangaswamy, M. (2014). Compressive noise radar for urban sensing. In 2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop, SAM 2014 (pp. 485-488). [6882446] (Proceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop). IEEE Computer Society. https://doi.org/10.1109/SAM.2014.6882448

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abstract = "Ultra-wideband (UWB) noise-like transmit waveforms are ideally suited for implementing compressive radar range-profile imaging systems. Due to useful properties such as robustness to interference, simplicity of hardware design, and high range resolution, UWB noise radar has been successfully applied to urban-sensing scenarios. Compressive noise radar systems can achieve resolutions comparable to conventional systems at lower sampling rates, thus enabling super-resolution in radar imaging. In this paper, compressive noise radar imaging is experimentally shown to be feasible for urban sensing applications. S-band through - The-wall imaging results demonstrate the feasibility of using compressive noise radar for sensing obscured targets of interest in urban scenarios.",

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Shastry, MC, Narayanan, RM & Rangaswamy, M 2014, Compressive noise radar for urban sensing. in 2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop, SAM 2014., 6882446, Proceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop, IEEE Computer Society, pp. 485-488, 2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop, SAM 2014, A Coruna, Spain, 6/22/14. https://doi.org/10.1109/SAM.2014.6882448

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

2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop, SAM 2014. IEEE Computer Society, 2014. p. 485-488 6882446 (Proceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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