Information distances for radar resolution analysis

Radmila Pribić; Geert Leus

doi:10.1109/CAMSAP.2017.8313060

Information distances for radar resolution analysis

Radmila Pribić, Geert Leus

Signal Processing Systems

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

4 Citations (Scopus)

31 Downloads (Pure)

Abstract

A stochastic approach to resolution based on information distances computed from the geometry of data models which is characterized by the Fisher information is explored. Stochastic resolution includes probability of resolution and signal-to-noise ratio (SNR). The probability of resolution is assessed from a hypothesis test by exploiting information distances in a likelihood ratio. Taking SNR into account is especially relevant in compressive sensing (CS) due to its fewer measurements. Based on this information-geometry approach, we demonstrate the stochastic resolution analysis in test cases from array processing. In addition, we also compare our stochastic resolution bounds with the actual resolution obtained numerically from sparse signal processing which nowadays is a major component of the back end of any CS sensor. Results demonstrate the suitability of the proposed stochastic resolution analysis due to its ability to include crucial features in the resolution performance guarantees: array configuration or sensor design, SNR, separation and probability of resolution.

Original language	English
Title of host publication	2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)
Place of Publication	Piscataway, NJ
Publisher	IEEE
Pages	1-5
Number of pages	5
ISBN (Electronic)	978-1-5386-1251-4
ISBN (Print)	978-1-5386-1252-1
DOIs	https://doi.org/10.1109/CAMSAP.2017.8313060
Publication status	Published - 2018
Event	2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing - Willemstad, Curaçao Duration: 10 Dec 2017 → 13 Dec 2017 Conference number: 7 http://www.cs.huji.ac.il/conferences/CAMSAP17/

Workshop

Workshop	2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing
Abbreviated title	CAMSAP
Country/Territory	Curaçao
City	Willemstad
Period	10/12/17 → 13/12/17
Internet address	http://www.cs.huji.ac.il/conferences/CAMSAP17/

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

array processing
compressive sensing
information geometry
likelihood ratio
radar
resolution

Access to Document

10.1109/CAMSAP.2017.8313060

08313060Final published version, 521 KB

Cite this

@inproceedings{e1b7d6c3b3fd42a899691c8b1fc3e6b8,

title = "Information distances for radar resolution analysis",

abstract = "A stochastic approach to resolution based on information distances computed from the geometry of data models which is characterized by the Fisher information is explored. Stochastic resolution includes probability of resolution and signal-to-noise ratio (SNR). The probability of resolution is assessed from a hypothesis test by exploiting information distances in a likelihood ratio. Taking SNR into account is especially relevant in compressive sensing (CS) due to its fewer measurements. Based on this information-geometry approach, we demonstrate the stochastic resolution analysis in test cases from array processing. In addition, we also compare our stochastic resolution bounds with the actual resolution obtained numerically from sparse signal processing which nowadays is a major component of the back end of any CS sensor. Results demonstrate the suitability of the proposed stochastic resolution analysis due to its ability to include crucial features in the resolution performance guarantees: array configuration or sensor design, SNR, separation and probability of resolution.",

keywords = "array processing, compressive sensing, information geometry, likelihood ratio, radar, resolution",

author = "Radmila Pribi{\'c} and Geert Leus",

note = "Green Open Access added to TU Delft Institutional Repository {\textquoteleft}You share, we take care!{\textquoteright} – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. ; 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP ; Conference date: 10-12-2017 Through 13-12-2017",

year = "2018",

doi = "10.1109/CAMSAP.2017.8313060",

language = "English",

isbn = "978-1-5386-1252-1",

pages = "1--5",

booktitle = "2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)",

publisher = "IEEE",

address = "United States",

url = "http://www.cs.huji.ac.il/conferences/CAMSAP17/",

}

Pribić, R & Leus, G 2018, Information distances for radar resolution analysis. in 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)., 8313060, IEEE, Piscataway, NJ, pp. 1-5, 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, Willemstad, Curaçao, 10/12/17. https://doi.org/10.1109/CAMSAP.2017.8313060

TY - GEN

T1 - Information distances for radar resolution analysis

AU - Pribić, Radmila

AU - Leus, Geert

N1 - Conference code: 7

PY - 2018

Y1 - 2018

N2 - A stochastic approach to resolution based on information distances computed from the geometry of data models which is characterized by the Fisher information is explored. Stochastic resolution includes probability of resolution and signal-to-noise ratio (SNR). The probability of resolution is assessed from a hypothesis test by exploiting information distances in a likelihood ratio. Taking SNR into account is especially relevant in compressive sensing (CS) due to its fewer measurements. Based on this information-geometry approach, we demonstrate the stochastic resolution analysis in test cases from array processing. In addition, we also compare our stochastic resolution bounds with the actual resolution obtained numerically from sparse signal processing which nowadays is a major component of the back end of any CS sensor. Results demonstrate the suitability of the proposed stochastic resolution analysis due to its ability to include crucial features in the resolution performance guarantees: array configuration or sensor design, SNR, separation and probability of resolution.

AB - A stochastic approach to resolution based on information distances computed from the geometry of data models which is characterized by the Fisher information is explored. Stochastic resolution includes probability of resolution and signal-to-noise ratio (SNR). The probability of resolution is assessed from a hypothesis test by exploiting information distances in a likelihood ratio. Taking SNR into account is especially relevant in compressive sensing (CS) due to its fewer measurements. Based on this information-geometry approach, we demonstrate the stochastic resolution analysis in test cases from array processing. In addition, we also compare our stochastic resolution bounds with the actual resolution obtained numerically from sparse signal processing which nowadays is a major component of the back end of any CS sensor. Results demonstrate the suitability of the proposed stochastic resolution analysis due to its ability to include crucial features in the resolution performance guarantees: array configuration or sensor design, SNR, separation and probability of resolution.

KW - array processing

KW - compressive sensing

KW - information geometry

KW - likelihood ratio

KW - radar

KW - resolution

UR - http://www.scopus.com/inward/record.url?scp=85051138118&partnerID=8YFLogxK

U2 - 10.1109/CAMSAP.2017.8313060

DO - 10.1109/CAMSAP.2017.8313060

M3 - Conference contribution

AN - SCOPUS:85051138118

SN - 978-1-5386-1252-1

SP - 1

EP - 5

BT - 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)

PB - IEEE

CY - Piscataway, NJ

T2 - 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing

Y2 - 10 December 2017 through 13 December 2017

ER -

Information distances for radar resolution analysis

Abstract

Workshop

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this