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Joint Multi-Target Identification and Classification in Cognitive Radar Sensor Networks

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Abstract

We investigate the problem of jointly classifying and identifying multiple targets in radar sensor networks where the maximum number of categories and the maximum number of targets in each category are obtained a priori based on statistical data. However, the actual number of targets in each category and the actual number of target categories being present at any given time are assumed unknown. It is assumed that a given target only belongs to one category and one identification number. The target signals are moreover modeled as zero-mean complex Gaussian processes. In this paper, we propose a joint multi-target identification and classification (JMIC) algorithm for radar surveillance using the cognitive radar network. The existing target categories are first classified and then the targets in each category are accordingly identified. Simulation results are presented to evaluate the feasibility and effectiveness of the proposed JMIC algorithm in a query surveillance region.

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Acknowledgements

This work was supported in part by the U.S. Office of Naval Research (ONR) under Grant N00014-11-1-0071, and National Science Foundation (NSF) under Grants CNS-0964713, CCF-0956438, CNS-1050618.

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Authors and Affiliations

  1. Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, 76019, USA

    Hong-Sam T. Le & Qilian Liang

  2. College of Physics and Electronic Information, Tianjin Normal University, Tianjin, 300384, China

    Qilian Liang

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  1. Hong-Sam T. Le
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  2. Qilian Liang
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Correspondence to Qilian Liang.

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Le, HS.T., Liang, Q. Joint Multi-Target Identification and Classification in Cognitive Radar Sensor Networks. Int J Wireless Inf Networks 18, 100–107 (2011). https://doi.org/10.1007/s10776-011-0145-1

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  • DOI: https://doi.org/10.1007/s10776-011-0145-1

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