Deep Neural Network-based Reference Signal Reconstruction for Passive Radar with Orthogonal Frequency Division Multiplexing Waveform

Zhixin ZHAO; Wenting DAI; Xin CHEN; Shihua HE; Ping’an TAO

doi:10.11999/JEIT200888

Volume 43 Issue 9

Sep. 2021

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Article Navigation > Journal of Electronics & Information Technology > 2021 > 43(9): 2735-2742

Zhixin ZHAO, Wenting DAI, Xin CHEN, Shihua HE, Ping’an TAO. Deep Neural Network-based Reference Signal Reconstruction for Passive Radar with Orthogonal Frequency Division Multiplexing Waveform[J]. Journal of Electronics & Information Technology, 2021, 43(9): 2735-2742. doi: 10.11999/JEIT200888

Citation:

Zhixin ZHAO, Wenting DAI, Xin CHEN, Shihua HE, Ping’an TAO. Deep Neural Network-based Reference Signal Reconstruction for Passive Radar with Orthogonal Frequency Division Multiplexing Waveform[J]. Journal of Electronics & Information Technology, 2021, 43(9): 2735-2742. doi: 10.11999/JEIT200888

Citation:

Zhixin ZHAO, Wenting DAI, Xin CHEN, Shihua HE, Ping’an TAO. Deep Neural Network-based Reference Signal Reconstruction for Passive Radar with Orthogonal Frequency Division Multiplexing Waveform[J]. Journal of Electronics & Information Technology, 2021, 43(9): 2735-2742. doi: 10.11999/JEIT200888

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Deep Neural Network-based Reference Signal Reconstruction for Passive Radar with Orthogonal Frequency Division Multiplexing Waveform

doi: 10.11999/JEIT200888

School of Information Engineering, Nanchang University, Nanchang 330031, China

Funds: The National Natural Science Foundation of China (61461030), The Natural Science Fund of Jiangxi Province (20202BAB202001)

Received Date: 2020-10-16
Rev Recd Date: 2021-06-12

Available Online: 2021-06-25

Publish Date: 2021-09-16

Abstract

Abstract

Considering the problem of obtaining the reference signal for passive radar with Orthogonal Frequency Division Multiplexing (OFDM) waveform, the reconstruction method based on "demodulation-remodulation" employs the waveform advantage to obtain a purer reference signal. On this basis, a Deep Neural Network (DNN) reconstruction method that combines OFDM demodulation, channel estimation, channel equalization, and constellation point inverse mapping is proposed to establish a DNN-based reference signal reconstruction scheme. This method can be used to adaptively and deeply excavate the mapping relationship between time-domain received symbols and transmission symbols through network learning, and implicitly estimate the channel response, thereby improving demodulation accuracy and reconstruction performance. Firstly, the acquisition of simulation data sets, the construction and training of DNN are studied in this paper.Then, the comparison between the DNN method and the traditional method about reference signal reconstruction performance is analyzed under the condition that the number of pilots is reduced, the cyclic prefix is removed, the symbol timing offset exists, the carrier frequency offset exists, the time domain windowing filter is performed on the high peak-to-average power ratio signal, and all the above parameters are superimposed. Finally, simulation results show the effectiveness of this method.
- Passive radar,
- Orthogonal Frequency Division Multiplexing (OFDM) waveform,
- Reference signal reconstruction,
- Deep Neural Network(DNN)

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References(18)

References

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