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 m...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 d B, the correct recognition rate is higher than 93%. Hence, the proposed algorithm has great application value.展开更多
As an advanced generation instrument of earth observation,small footprint full waveform light detection and ranging(LiDAR) technology has been widely used in the past few years.Decomposition and radiative correction i...As an advanced generation instrument of earth observation,small footprint full waveform light detection and ranging(LiDAR) technology has been widely used in the past few years.Decomposition and radiative correction is an important step in waveform data processing,it influences the accuracy of both information extraction and further applications.Based on a stepwise strategy,this study adopts Gaussian mixture model to approximate the LiDAR waveform.In addition to waveform decomposition,a relative correction model is proposed in this paper,the model considers the transmit pulses as well as the different of the travel path for implementing LiDAR waveform relative correction.Validation of the stepwise decomposition and relative correction model are carried out on LiDAR waveform acquired over Zhangye,China.The results indicate that stepwise decomposition identified the number of peaks in LiDAR waveforms,center position and width of each peak well.The relative radiometric correction also improves the similarity of waveforms which acquired at the same target.展开更多
基金Project(61301095)supported by the National Natural Science Foundation of ChinaProject(QC2012C070)supported by Heilongjiang Provincial Natural Science Foundation for the Youth,ChinaProjects(HEUCF130807,HEUCFZ1129)supported by the Fundamental Research Funds for the Central Universities of China
文摘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 d B, the correct recognition rate is higher than 93%. Hence, the proposed algorithm has great application value.
基金supported by Major State Basic Research Development Program of China(Grant No.2007CB714406)National Key Technology R&D Program of China(Grant No.2008BAC34B03)
文摘As an advanced generation instrument of earth observation,small footprint full waveform light detection and ranging(LiDAR) technology has been widely used in the past few years.Decomposition and radiative correction is an important step in waveform data processing,it influences the accuracy of both information extraction and further applications.Based on a stepwise strategy,this study adopts Gaussian mixture model to approximate the LiDAR waveform.In addition to waveform decomposition,a relative correction model is proposed in this paper,the model considers the transmit pulses as well as the different of the travel path for implementing LiDAR waveform relative correction.Validation of the stepwise decomposition and relative correction model are carried out on LiDAR waveform acquired over Zhangye,China.The results indicate that stepwise decomposition identified the number of peaks in LiDAR waveforms,center position and width of each peak well.The relative radiometric correction also improves the similarity of waveforms which acquired at the same target.
