Bistatic Radar Coincidence Imaging Based on Sparse Bayesian Learning

Rui LI; Qun ZHANG; Linghua SU; Jia LIANG; Ying LUO

doi:10.11999/JEIT180933

Volume 41 Issue 12

Dec. 2019

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Article Navigation > Journal of Electronics & Information Technology > 2019 > 41(12): 2865-2872

Rui LI, Qun ZHANG, Linghua SU, Jia LIANG, Ying LUO. Bistatic Radar Coincidence Imaging Based on Sparse Bayesian Learning[J]. Journal of Electronics & Information Technology, 2019, 41(12): 2865-2872. doi: 10.11999/JEIT180933

Citation:

Rui LI, Qun ZHANG, Linghua SU, Jia LIANG, Ying LUO. Bistatic Radar Coincidence Imaging Based on Sparse Bayesian Learning[J]. Journal of Electronics & Information Technology, 2019, 41(12): 2865-2872. doi: 10.11999/JEIT180933

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Bistatic Radar Coincidence Imaging Based on Sparse Bayesian Learning

doi: 10.11999/JEIT180933

Rui LI^{1
,
,},
Qun ZHANG^1,2,
Linghua SU¹,
Jia LIANG¹,
Ying LUO¹

1.
Institute of Information and Navigation, Air Force Engineering University, Xi’an 710077, China
2.
Key Laboratory of Wave Scattering and Remote Sensing Information, Fudan University, Shanghai 200433, China

Funds: The National Natural Science Foundation of China (61631019), The Natural Science Foundation Research Program of Shaanxi Province (2016JM4008, 2018JM6072)

Received Date: 2018-09-30
Rev Recd Date: 2019-02-25

Available Online: 2019-03-14

Publish Date: 2019-12-01

Abstract

Abstract

Bistatic radar has the advantages of high concealment and strong anti-interference performance, and plays an important role in modern electronic warfare. Based on the principle of radar coincidence imaging, the problem of bistatic radar coincidence imaging of moving targets is studied. Firstly, based on the bistatic radar system that uses uniform linear array as the transmitting and receiving antenna, the characteristics of the moving target radar echo signal are analyzed under the condition of transmitting random frequency modulation signal, and a bistatic radar coincidence imaging parametric sparse representation model is established. Secondly, an iterative coincidence imaging algorithm based on sparse Bayesian learning is proposed for the parametric sparse representation model established. Based on the Bayesian model, the sparse reconstructed signal is obtained by Bayesian inference, so that the moving target imaging and accurate estimation of motion parameters can be achieved. Finally, the effectiveness of the proposed method is verified by simulation experiments.
- Bistatic radar,
- Radar coincidence imaging,
- Sparse Bayesian learning,
- Parametric sparse representation

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References(23)

References

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