Tracking-Aided Classification of Targets Using Multihypothesis Sequential Probability Ratio Test

Yongxin Gao; Yu Liu; X. Rong Li

doi:10.1109/TAES.2017.2747958

Tracking-Aided Classification of Targets Using Multihypothesis Sequential Probability Ratio Test

Yongxin Gao
, Yu Liu
, X. Rong Li

Xi'an Jiaotong University
University of New Orleans

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

12 Scopus citations

Abstract

This paper deals with target classification by using both feature data and kinematic measurements. The problem is tackled by multihypothesis sequential testing with embedded target tracking. We implement an Armitage sequential test for nonmaneuvering and maneuvering targets. Both (centralized and distributed) fusion architectures are used for the embedded tracking. The contributions of the kinematic measurements to classification are analyzed, and classification performance improvement is shown analytically for a special case. Numerical results are provided to demonstrate the performance of our algorithms.

Original language	English
Article number	8023772
Pages (from-to)	233-245
Number of pages	13
Journal	IEEE Transactions on Aerospace and Electronic Systems
Volume	54
Issue number	1
DOIs	https://doi.org/10.1109/TAES.2017.2747958
State	Published - Feb 2018
Externally published	Yes

Keywords

Multihypothesis test
sequential probability ratio test (SPRT)
target classification
track fusion

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10.1109/TAES.2017.2747958

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abstract = "This paper deals with target classification by using both feature data and kinematic measurements. The problem is tackled by multihypothesis sequential testing with embedded target tracking. We implement an Armitage sequential test for nonmaneuvering and maneuvering targets. Both (centralized and distributed) fusion architectures are used for the embedded tracking. The contributions of the kinematic measurements to classification are analyzed, and classification performance improvement is shown analytically for a special case. Numerical results are provided to demonstrate the performance of our algorithms.",

keywords = "Multihypothesis test, sequential probability ratio test (SPRT), target classification, track fusion",

author = "Yongxin Gao and Yu Liu and Li, \{X. Rong\}",

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AB - This paper deals with target classification by using both feature data and kinematic measurements. The problem is tackled by multihypothesis sequential testing with embedded target tracking. We implement an Armitage sequential test for nonmaneuvering and maneuvering targets. Both (centralized and distributed) fusion architectures are used for the embedded tracking. The contributions of the kinematic measurements to classification are analyzed, and classification performance improvement is shown analytically for a special case. Numerical results are provided to demonstrate the performance of our algorithms.

KW - Multihypothesis test

KW - sequential probability ratio test (SPRT)

KW - target classification

KW - track fusion

UR - https://www.scopus.com/pages/publications/85029163578

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IS - 1

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ER -

Tracking-Aided Classification of Targets Using Multihypothesis Sequential Probability Ratio Test

Abstract

Keywords

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