Deep belief networks based radar signal classification system
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A threat library is used in most of the existing electronic warfare systems to identify or execute jamming against various radar signals. The conventional method uses frequency, pulse repetition interval, and pulse width sampled from the pulse description word column as characteristics of a signal. Such sampling technique cannot effectively model each radar signal when dealing with a complex signal array. In this paper, a new deep belief network model is proposed to generate a more efficient threat library for radar signal classification. The proposed model consists of independent restricted Boltzman machines (RBMs) of frequency, pulse repetition interval, pulse width respectively, and a RBM which fuses the result again. The performance of the existing system and the proposed system is evaluated by testing the signals with measurement errors and insufficient information. As a result, the proposed system shows more than 6% performance improvement over the existing system.
KeywordsThreat library DBN BP Radar signal classification
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
- Anjaneyulu L, Muthy N, Sarma N (2008) Radar emitter classification using self-organizing neural network models. International Conference on MICROWAVEGoogle Scholar
- Cho YS, Moon SC (2015) Recommender system using periodicity analysis via mining sequential patterns with time-series and FART analysis. J Converg 6:2Google Scholar
- Lee-Urban S, Trewhitt E, Bieder I, Odom J, Boone T, Whitaker E (2015) CORA: a flexible hybrid approach to building cognitive systems. Third annual conference on advances in cognitive systemsGoogle Scholar
- Lin CM, Chen YM, Hsueh CS (2014) A self-organizing interval type-2 fuzzy neural network for radar emitter identification. Int J Fuzzy Syst 16(1):20–30Google Scholar
- Petrov N, Jordanov I, Nikolov, Roe J (2013) Radar emitter signals recognition and classification with feedforward networks. Procedia Comput Sci 22: 1192–1200. (ISSN 1877–0509)Google Scholar
- Richard G, Wiley (2006) ELINT: the interception and analysis of radar signals. ARTHOUSE, pp 255–412Google Scholar