Identification of ground targets from sequential HRR radar signatures

X. Liao; P. Runkle; Y. Jiao; L. Carin

Identification of ground targets from sequential HRR radar signatures

X. Liao, P. Runkle, Y. Jiao, L. Carin

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

Abstract

An approach to identifying ground targets from sequential high-range-resolution (HRR) radar signatures is presented. A hidden Markov model (HMM) is employed to model the sequential information contained in multi-aspect target signatures. Dominant range-amplitude features are extracted via RELAX for dimension reduction. A new distance measure is incorporated into the HMM to allow a direct matching operation in the feature domain without requiring interpolation. The approach is applied to the dataset of ten MSTAR targets and is shown to yield an average identification rate of 90.3% using sequential information from 6 degree angular spans.

Original language	English (US)
Title of host publication	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Pages	2897-2900
Number of pages	4
State	Published - Sep 26 2001
Externally published	Yes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2021-02-09

Cite this

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title = "Identification of ground targets from sequential HRR radar signatures",

abstract = "An approach to identifying ground targets from sequential high-range-resolution (HRR) radar signatures is presented. A hidden Markov model (HMM) is employed to model the sequential information contained in multi-aspect target signatures. Dominant range-amplitude features are extracted via RELAX for dimension reduction. A new distance measure is incorporated into the HMM to allow a direct matching operation in the feature domain without requiring interpolation. The approach is applied to the dataset of ten MSTAR targets and is shown to yield an average identification rate of 90.3% using sequential information from 6 degree angular spans.",

author = "X. Liao and P. Runkle and Y. Jiao and L. Carin",

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year = "2001",

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day = "26",

language = "English (US)",

pages = "2897--2900",

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T1 - Identification of ground targets from sequential HRR radar signatures

AU - Liao, X.

AU - Runkle, P.

AU - Jiao, Y.

AU - Carin, L.

N1 - Generated from Scopus record by KAUST IRTS on 2021-02-09

PY - 2001/9/26

Y1 - 2001/9/26

N2 - An approach to identifying ground targets from sequential high-range-resolution (HRR) radar signatures is presented. A hidden Markov model (HMM) is employed to model the sequential information contained in multi-aspect target signatures. Dominant range-amplitude features are extracted via RELAX for dimension reduction. A new distance measure is incorporated into the HMM to allow a direct matching operation in the feature domain without requiring interpolation. The approach is applied to the dataset of ten MSTAR targets and is shown to yield an average identification rate of 90.3% using sequential information from 6 degree angular spans.

AB - An approach to identifying ground targets from sequential high-range-resolution (HRR) radar signatures is presented. A hidden Markov model (HMM) is employed to model the sequential information contained in multi-aspect target signatures. Dominant range-amplitude features are extracted via RELAX for dimension reduction. A new distance measure is incorporated into the HMM to allow a direct matching operation in the feature domain without requiring interpolation. The approach is applied to the dataset of ten MSTAR targets and is shown to yield an average identification rate of 90.3% using sequential information from 6 degree angular spans.

UR - http://www.scopus.com/inward/record.url?scp=0034854539&partnerID=8YFLogxK

M3 - Conference contribution

SP - 2897

EP - 2900

BT - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

ER -

Identification of ground targets from sequential HRR radar signatures

Abstract

Bibliographical note

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