Building Layout Imaging Method Using the Inter-block Coupling Sparse Bayesian Learning

JIN Liangnian; FENG Fei; LIU Qinghua; OUYANG Shan

doi:10.11999/JEIT170719

Volume 40 Issue 4

Apr. 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(4): 853-859

JIN Liangnian, FENG Fei, LIU Qinghua, OUYANG Shan. Building Layout Imaging Method Using the Inter-block Coupling Sparse Bayesian Learning[J]. Journal of Electronics & Information Technology, 2018, 40(4): 853-859. doi: 10.11999/JEIT170719

Citation:

JIN Liangnian, FENG Fei, LIU Qinghua, OUYANG Shan. Building Layout Imaging Method Using the Inter-block Coupling Sparse Bayesian Learning[J]. Journal of Electronics & Information Technology, 2018, 40(4): 853-859. doi: 10.11999/JEIT170719

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Building Layout Imaging Method Using the Inter-block Coupling Sparse Bayesian Learning

doi: 10.11999/JEIT170719

1.
2.
(Institute of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China)

Funds:

The National Natural Science Foundation of China (61461012), Guangxi Natural Science Foundation (2017GXNSFAA198050), Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing, 2016 the Fund Project of Diretor (GXKL06160106)

Received Date: 2017-07-19
Rev Recd Date: 2017-12-25
Publish Date: 2018-04-19

Abstract

Abstract

In through-wall radar building layout imaging, the existing extended target sparse imaging method can not effectively exploit the structural sparsity of the wall reflections in the scene, resulting in incoherent imaging and unobvious contour of walls. A sparse Bayesian learning method is proposed for building layout imaging by exploiting the inter-block coupling of sparse signal. On the basis of the hierarchical Gaussian prior model of block sparse signal characteristics, the inter-block coupling coefficient is further used to characterize the structured sparsity of the wall reflections. Then these coefficients are introduced into the hyperparameters controlling the prior distribution of sparse signal, thus this structured sparsity is transformed into the coupling relationship of these hyperparameters. Susequently, an Expectation-Maximization (EM) algorithm is developed to infer the Maximum A Posterior (MAP) estimate of these hyperparameters. The results of simulation and experiment show that the proposed method improves effectively the imaging quality of the building wall.
- Through-wall radar,
- Building layout imaging,
- Structured sparsity,
- Sparse Bayesian learning,
- Inter- block coupling

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References(17)

References

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