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RISS 인기검색어
- 검색키워드
- 저자 : Pulong Nan (검색결과 5 건)
- 무료
- 기관 내 무료
- 유료
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Multi-mode Radar Signal Sorting by Means of Spatial Data Mining
Wan, Jian,Nan, Pulong,Guo, Qiang,Wang, Qiangbo The Korea Institute of Information and Commucation 2016 Journal of communications and networks Vol.18 No.5
For multi-mode radar signals in complex electromagnetic environment, different modes of one emitter tend to be deinterleaved into several emitters, called as "extension", when processing received signals by use of existing sorting methods. The "extension" problem inevitably deteriorates the sorting performance of multi-mode radar signals. In this paper, a novel method based on spatial data mining is presented to address above challenge. Based on theories of data field, we describe the distribution information of feature parameters using potential field, and makes partition clustering of parameter samples according to revealed distribution features. Additionally, an evaluation criterion based on cloud model membership is established to measure the relevance between different cluster-classes, which provides important spatial knowledge for the solution of the "extension" problem. It is shown through numerical simulations that the proposed method is effective on solving the "extension" problem in multi-mode radar signal sorting, and can achieve higher correct sorting rate.
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Multi-mode Radar Signal Sorting by Means of Spatial Data Mining
Jian Wan,Pulong Nan,Qiang Guo,Qiangbo Wang 한국통신학회 2016 Journal of communications and networks Vol.18 No.5
Formulti-mode radar signals in complex electromagneticenvironment, different modes of one emitter tend to be deinterleavedinto several emitters, called as “extension”, when processingreceived signals by use of existing sorting methods. The “extension”problem inevitably deteriorates the sorting performance ofmulti-mode radar signals. In this paper, a novel method based onspatial data mining is presented to address above challenge. Basedon theories of data field, we describe the distribution information offeature parameters using potential field, and makes partition clusteringof parameter samples according to revealed distribution features. Additionally, an evaluation criterion based on cloud modelmembership is established to measure the relevance between differentcluster-classes, which provides important spatial knowledgefor the solution of the “extension” problem. It is shown through numericalsimulations that the proposed method is effective on solvingthe “extension” problem in multi-mode radar signal sorting,and can achieve higher correct sorting rate.
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Recognition of Radar Emitter Signals Based on SVD and AF Main Ridge Slice
Qiang Guo,Pulong Nan,Xiaoyu Zhang,Yuning Zhao,Jian Wan 한국통신학회 2015 Journal of communications and networks Vol.17 No.5
Recognition of radar emitter signals is one of core elements in radar reconnaissance systems. A novel method based on singular value decomposition (SVD) and themain ridge slice of ambiguity function (AF) is presented for attaining a higher correct recognition rate of radar emitter signals in case of low signal-tonoise ratio. This method calculates the AF of the sorted signal and ascertains the main ridge slice envelope. To improve the recognition performance, SVD is employed to eliminate the influence of noise on the main ridge slice envelope. The rotation angle and symmetric Holder coefficients of the main ridge slice envelope are extracted as the elements of the feature vector. And kernel fuzzy cmeans clustering is adopted to analyze the feature vector and classify different types of radar signals. Simulation results indicate that the feature vector extracted by the proposed method has satisfactory aggregation within class, separability between classes, and stability. Compared to existing methods, the proposed feature recognition method can achieve a higher correct recognition rate.
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Method for Feature Extraction of Radar Full Pulses Based on EMD and Chaos Detection
Qiang Guo,Pulong Nan 한국통신학회 2014 Journal of communications and networks Vol.16 No.1
A novel method for extracting frequency slippage signalfrom radar full pulse sequence is presented. For the radar fullpulse sequence received by radar interception receiver, radio frequency(RF) and time of arrival (TOA) of all pulses constitute atwo-dimensional information sequence. In a complex and intensiveelectromagnetic environment, the TOA of pulses is distributedunevenly, randomly, and in a nonstationary manner, preventing existingmethods from directly analyzing such time series and effectivelyextracting certain signal features. This work applies Gaussiannoise insertion and structure function to the TOA-RF informationsequence respectively such that the equalization of time intervalsand correlation processing are accomplished. The componentswith different frequencies in structure function series are separatedusing empirical mode decomposition. Additionally, a chaos detectionmodel based on the Duffing equation is introduced to determinethe useful component and extract the changing features ofRF. Experimental results indicate that the proposed methodologycan successfully extract the slippage signal effectively in the casethat multiple radar pulse sequences overlap.
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Method for Feature Extraction of Radar Full Pulses Based on EMD and Chaos Detection
Guo, Qiang,Nan, Pulong The Korea Institute of Information and Commucation 2014 Journal of communications and networks Vol.16 No.1
A novel method for extracting frequency slippage signal from radar full pulse sequence is presented. For the radar full pulse sequence received by radar interception receiver, radio frequency (RF) and time of arrival (TOA) of all pulses constitute a two-dimensional information sequence. In a complex and intensive electromagnetic environment, the TOA of pulses is distributed unevenly, randomly, and in a nonstationary manner, preventing existing methods from directly analyzing such time series and effectively extracting certain signal features. This work applies Gaussian noise insertion and structure function to the TOA-RF information sequence respectively such that the equalization of time intervals and correlation processing are accomplished. The components with different frequencies in structure function series are separated using empirical mode decomposition. Additionally, a chaos detection model based on the Duffing equation is introduced to determine the useful component and extract the changing features of RF. Experimental results indicate that the proposed methodology can successfully extract the slippage signal effectively in the case that multiple radar pulse sequences overlap.
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