Segment Clustering Radar Signal Sorting

Guo, Qiang; Xu, Wei; Wang, Changhong; Guan, Di

doi:10.1109/cso.2009.439

Cited by 6 publications

(2 citation statements)

References 2 publications

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“…Select the carrier frequency (RF), pulse width (PW) and the direction of arrival (DOA) as the characteristic parameters, three attributes of information constituted a characterization vector [8]. Add 30 % of the total radar pulses number noise pulse.…”

Section: Simulation Analysismentioning

confidence: 99%

A New Method of Unknown Radar Signals Sorting

Zhang

Tian

2013

Lecture Notes in Electrical Engineering

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Aimed at the problem of worse real-time in the sorting of unknown radar signals, a new signal sorting method based on the DBSCAN is put forward. Using the parameter distribution characteristics of radar pulse data, the method firstly searches a number of reference points to properly represent original data points, and then, make use of their density connected character to cluster the reference points. Because of the decrease of the data involved in clustering, the method overcomes the immense calculated quantities of original algorithm, and enhances the clustering speed of unknown radar signal sorting. Computer simulation results show that the proposed algorithm can effectively sort the unknown radar signals.

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Section: Simulation Analysismentioning

confidence: 99%

A New Method of Unknown Radar Signals Sorting

Zhang

Tian

2013

Lecture Notes in Electrical Engineering

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“…As an unsupervised classification method, the clustering technique does not need the prior information of radar emitters, while it requires excellent similarity of the same emitter signals and differences of different emitter signals. Clustering methods include fuzzy clustering [7], [8], support vector clustering [9], [10] and dynamic clustering based on the nuclear field [11]. As a classification algorithm, supervised classification requires prior information and knowledge.…”

Section: Introductionmentioning

confidence: 99%

A Novel Radar Signals Sorting Method-Based Trajectory Features

Guo

et al. 2019

IEEE Access

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When several foreign fighters with the same type enter detection range, the electronic warfare (EW) receivers will intercept many the same type radar emitter signals. If the intercepted pulse is processed by the traditional sorting methods, the number of emitters cannot be identified. The main reason is that the same type of radar has similar parameters. It will cause a devastating influence on subsequent strategic decisions. A novel sorting method based on the trajectory features is proposed to solve the aforementioned problems. First, the trajectory features of the intercepted pulse signal are extracted. Then, the segmentation method is utilized to preprocess the signals, which enhances the computing efficiency and improves the sorting accuracy. Meanwhile, a prediction framework based on long short-term memory (LSTM) recurrent neural network is established to forecast pulses. Finally, the radar stagger pulses are sorted by forecast pulses. The simulation results show that the proposed method can recognize the number of emitters and achieve high sorting accuracy. It provides a new idea for the radar signals sorting of the same type. INDEX TERMS Signal sorting, trajectory features, recurrent neural networks (RNNs), long short-term memory (LSTM).

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A novel and efficient approach for automatic classification of radar emitter signals

Aziz

2013

2013 IEEE Aerospace Conference

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Segment Clustering Radar Signal Sorting

Cited by 6 publications

References 2 publications

A New Method of Unknown Radar Signals Sorting

A New Method of Unknown Radar Signals Sorting

A Novel Radar Signals Sorting Method-Based Trajectory Features

A novel and efficient approach for automatic classification of radar emitter signals

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