University of Oulu

J. Sui, Z. Liu, L. Liu, B. Peng, T. Liu and X. Li, "Online Non-Cooperative Radar Emitter Classification From Evolving and Imbalanced Pulse Streams," in IEEE Sensors Journal, vol. 20, no. 14, pp. 7721-7730, 15 July15, 2020, doi: 10.1109/JSEN.2020.2981976

Online non-cooperative radar emitter classification from evolving and imbalanced pulse streams

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Author: Sui, Jingping1,2; Liu, Zhen1; Liu, Li3,4;
Organizations: 1College of Electronic Science and Engineering, National University of Defense Technology, Changsha, China, 410073
2Department of Computer Science, Aalto University, Espoo, Finland, 02150
3College of System Engineering, National University of Defense Technology, Changsha, China, 410073
4Center for Machine Vision and Signal Analysis, University of Oulu, Oulu, Finland, 90014
5National University of Defense Technology, Changsha, China, 410073
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 1.6 MB)
Persistent link:
Language: English
Published: Institute of Electrical and Electronics Engineers, 2020
Publish Date: 2020-08-13


Recent research treats radar emitter classification (REC) problems as typical closed-set classification problems, i.e., assuming all radar emitters are cooperative and their pulses can be pre-obtained for training the classifiers. However, such overly ideal assumptions have made it difficult to fit real-world REC problems into such restricted models. In this paper, to achieve online REC in a more realistic way, we convert the online REC problem into dynamically performing subspace clustering on pulse streams. Meanwhile, the pulse streams have evolving and imbalanced properties which are mainly caused by the existence of the non-cooperative emitters. Specifically, a novel data stream clustering (DSC) algorithm, called dynamic improved exemplar-based subspace clustering (DI-ESC), is proposed, which consists of two phases, i.e., initialization and online clustering. First, to achieve subspace clustering on subspace-imbalanced data, a static clustering approach called the improved ESC algorithm (I-ESC) is proposed. Second, based on the subspace clustering results obtained, DI-ESC can process the pulse stream in real-time and can further detect the emitter evolution by the proposed evolution detection strategy. The typically dynamic behavior of emitters such as appearing, disappearing and recurring can be detected and adapted by the DI-ESC. Extinct experiments on real-world emitter data show the sensitivity, effectiveness, and superiority of the proposed I-ESC and DI-ESC algorithms.

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Series: IEEE sensors journal
ISSN: 1530-437X
ISSN-E: 1558-1748
ISSN-L: 1530-437X
Volume: 20
Issue: 14
Pages: 7721 - 7730
DOI: 10.1109/JSEN.2020.2981976
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
213 Electronic, automation and communications engineering, electronics
Funding: This work is supported by the National Natural Science Foundation of China 61701510 and 61801488.
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