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DME Interference mitigation for L-DACS1 based on system identification and sparse representation 被引量:6

DME Interference mitigation for L-DACS1 based on system identification and sparse representation
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摘要 L-band digital aeronautical communication system 1(L-DACS1) is a promising candidate data-link for future air-ground communication, but it is severely interfered by the pulse pairs(PPs) generated by distance measure equipment. A novel PP mitigation approach is proposed in this paper. Firstly, a deformed PP detection(DPPD) method that combines a filter bank, correlation detection, and rescanning is proposed to detect the deformed PPs(DPPs) which are caused by multiple filters in the receiver. Secondly, a finite impulse response(FIR) model is used to approximate the overall characteristic of filters, and then the waveform of DPP can be acquired by the original waveform of PP and the FIR model. Finally, sparse representation is used to estimate the position and amplitude of each DPP, and then reconstruct each DPP. The reconstructed DPPs will be subtracted from the contaminated signal to mitigate interference. Numerical experiments show that the bit error rate performance of our approach is about 5 dB better than that of recent works and is closer to interference-free environment. L-band digital aeronautical communication system 1(L-DACS1) is a promising candidate data-link for future air-ground communication, but it is severely interfered by the pulse pairs(PPs) generated by distance measure equipment. A novel PP mitigation approach is proposed in this paper. Firstly, a deformed PP detection(DPPD) method that combines a filter bank, correlation detection, and rescanning is proposed to detect the deformed PPs(DPPs) which are caused by multiple filters in the receiver. Secondly, a finite impulse response(FIR) model is used to approximate the overall characteristic of filters, and then the waveform of DPP can be acquired by the original waveform of PP and the FIR model. Finally, sparse representation is used to estimate the position and amplitude of each DPP, and then reconstruct each DPP. The reconstructed DPPs will be subtracted from the contaminated signal to mitigate interference. Numerical experiments show that the bit error rate performance of our approach is about 5 dB better than that of recent works and is closer to interference-free environment.
出处 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2016年第6期1762-1773,共12页 中国航空学报(英文版)
基金 supported in part by the National Natural Science Foundation (Nos. U1533107 and U1433105) the Civil Aviation Science and Technology Innovation Foundation (No. MHRD20130217) the Fundamental Research Funds for the Central Universities of CAUC (No. 3122016D003)
关键词 DME interference L-DACS1 Least square approximations Proximal gradient algorithm Sparse representation DME interference L-DACS1 Least square approximations Proximal gradient algorithm Sparse representation
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