Radar emitter signal recognition based on multi-scale wavelet entropy and feature weighting
来源期刊：中南大学学报(英文版)2014年第11期
论文作者：LI Yi-bing(李一兵) GE Juan(葛娟) LIN Yun(林云) YE Fang(叶方)
文章页码：4254 - 4260
Key words：emitter recognition; multi-scale wavelet entropy; feature weighting; uneven weight factor; stability weight factor
Abstract: In modern electromagnetic environment, radar emitter signal recognition is an important research topic. On the basis of multi-resolution wavelet analysis, an adaptive radar emitter signal recognition method based on multi-scale wavelet entropy feature extraction and feature weighting was proposed. With the only priori knowledge of signal to noise ratio (SNR), the method of extracting multi-scale wavelet entropy features of wavelet coefficients from different received signals were combined with calculating uneven weight factor and stability weight factor of the extracted multi-dimensional characteristics. Radar emitter signals of different modulation types and different parameters modulated were recognized through feature weighting and feature fusion. Theoretical analysis and simulation results show that the presented algorithm has a high recognition rate. Additionally, when the SNR is greater than -4 dB, the correct recognition rate is higher than 93%. Hence, the proposed algorithm has great application value.
LI Yi-bing(李一兵), GE Juan(葛娟), LIN Yun(林云), YE Fang(叶方)
(Department of Information and Communication Engineering, University of Harbin Engineering, Harbin 150001, China)
Abstract:In modern electromagnetic environment, radar emitter signal recognition is an important research topic. On the basis of multi-resolution wavelet analysis, an adaptive radar emitter signal recognition method based on multi-scale wavelet entropy feature extraction and feature weighting was proposed. With the only priori knowledge of signal to noise ratio (SNR), the method of extracting multi-scale wavelet entropy features of wavelet coefficients from different received signals were combined with calculating uneven weight factor and stability weight factor of the extracted multi-dimensional characteristics. Radar emitter signals of different modulation types and different parameters modulated were recognized through feature weighting and feature fusion. Theoretical analysis and simulation results show that the presented algorithm has a high recognition rate. Additionally, when the SNR is greater than -4 dB, the correct recognition rate is higher than 93%. Hence, the proposed algorithm has great application value.
Key words:emitter recognition; multi-scale wavelet entropy; feature weighting; uneven weight factor; stability weight factor
