Identification of ground targets from sequential high-range-resolution radar signatures

Xijejun Liao; Paul Runkle; Lawrence Carin

doi:10.1109/TAES.2002.1145746

Identification of ground targets from sequential high-range-resolution radar signatures

Xijejun Liao, Paul Runkle, Lawrence Carin

Research output: Contribution to journal › Article › peer-review

140 Scopus citations

Abstract

An approach to identifying targets from sequential high-range-resolution (HRR) radar signatures is presented. In particular, a hidden Markov model (HMM) is employed to characterize the sequential information contained in multiaspect HRR target signatures. Features from each of the HRR waveforms are extracted via the RELAX algorithm. The statistical models used for the HMM states are formulated for application to RELAX features, and the expectation-maximization (EM) training algorithm is augmented appropriately. Example classification resulte are presented for the ten-target MSTAR data set.

Original language	English (US)
Pages (from-to)	1230-1242
Number of pages	13
Journal	IEEE Transactions on Aerospace and Electronic Systems
Volume	38
Issue number	4
DOIs	https://doi.org/10.1109/TAES.2002.1145746
State	Published - Oct 1 2002
Externally published	Yes

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abstract = "An approach to identifying targets from sequential high-range-resolution (HRR) radar signatures is presented. In particular, a hidden Markov model (HMM) is employed to characterize the sequential information contained in multiaspect HRR target signatures. Features from each of the HRR waveforms are extracted via the RELAX algorithm. The statistical models used for the HMM states are formulated for application to RELAX features, and the expectation-maximization (EM) training algorithm is augmented appropriately. Example classification resulte are presented for the ten-target MSTAR data set.",

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AU - Carin, Lawrence

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N2 - An approach to identifying targets from sequential high-range-resolution (HRR) radar signatures is presented. In particular, a hidden Markov model (HMM) is employed to characterize the sequential information contained in multiaspect HRR target signatures. Features from each of the HRR waveforms are extracted via the RELAX algorithm. The statistical models used for the HMM states are formulated for application to RELAX features, and the expectation-maximization (EM) training algorithm is augmented appropriately. Example classification resulte are presented for the ten-target MSTAR data set.

AB - An approach to identifying targets from sequential high-range-resolution (HRR) radar signatures is presented. In particular, a hidden Markov model (HMM) is employed to characterize the sequential information contained in multiaspect HRR target signatures. Features from each of the HRR waveforms are extracted via the RELAX algorithm. The statistical models used for the HMM states are formulated for application to RELAX features, and the expectation-maximization (EM) training algorithm is augmented appropriately. Example classification resulte are presented for the ten-target MSTAR data set.

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JO - IEEE Transactions on Aerospace and Electronic Systems

JF - IEEE Transactions on Aerospace and Electronic Systems

IS - 4

ER -

Identification of ground targets from sequential high-range-resolution radar signatures

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