Enhanced radio tomographic imaging with heterogeneous Bayesian compressive sensing

Huang, Kaide; Tan, Shengbo; Luo, Yubin; Guo, Xuemei; Wang, Guoli

doi:10.1016/j.pmcj.2017.03.001

Cited by 31 publications

(34 citation statements)

References 16 publications

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“…In this section, we first present the RTI experimental setups, and then provide the imaging results by the reconstruction algorithms. Several algorithms, including Tikhonov regularization [1], BCS [22], [30], HBCS [25], [26] and Laplace method [24], [31] are used to compare the proposed SBL-NA approach. Finally, we evaluate the device-free localization results based on the reconstructed images.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…However, these CS algorithms are built upon the assumption of Gaussian noise, and fail to provide satisfactory results in the scenario of different types of noise. To enhance noise robustness, we proposed a heterogeneous Bayesian compressive sensing (HBCS) method that can resist the outliers [25], [26]. However, it still cannot finely measure the unknown complex noise in rich multipath environments.…”

Section: Introductionmentioning

confidence: 99%

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Noise Adaptive Optimization Scheme for Robust Radio Tomographic Imaging Based on Sparse Bayesian Learning

Huang

Yang

2020

IEEE Access

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This paper addresses the robust signal reconstruction problem caused by different types of noise in radio tomographic imaging (RTI). Most of the existing reconstruction algorithms are built on the assumption of Gaussian noise, which is not the case for practical RTI systems, especially in indoor multipath environments. To weaken the effect of different types of noise on RTI performance, we propose a noise adaptive optimization scheme with sparse Bayesian learning (SBL). Specifically, we model the noise as a mixture of Gaussians (MoG) distribution, which provides the flexibility for describing the unknown and time-varying RTI noise. We further automatically estimate the MoG model parameters as well as the signal under a SBL framework, which makes the signal reconstruction more robust to the complex noise and even outliers. Experimental results in the context of device-free localization show that the proposed scheme can effectively reduce mislocalization and improve localization accuracy in the rich multipath environments, as compared with state-of-the-art reconstruction methods. INDEX TERMS Device-free localization, radio tomographic imaging, robust signal reconstruction, sparse Bayesian learning, mixture of Gaussians.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Noise Adaptive Optimization Scheme for Robust Radio Tomographic Imaging Based on Sparse Bayesian Learning

Huang

Yang

2020

IEEE Access

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“…The reconstruction result is usually blurred [8], [9]. Due to the reason that the wall structures are sparse in the whole SLF image, the compressive sensing (CS) methods have been used in the image reconstruction [17]- [19]. Moreover, cosidering the greater sparsity of the image in the gradient domain, TV minimization model [11] has been used to reconstruct the SLF map in through-the-wall image reconstruction.…”

Section: Rtv-pir Algorithm a The Rtv-pir Modelmentioning

confidence: 99%

Through-the-Wall Image Reconstruction via Reweighted Total Variation and Prior Information in Radio Tomographic Imaging

Guo

Liang

et al. 2020

IEEE Access

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This paper focuses on the application of radio tomographic imaging in through-the-wall image reconstruction. By using the simple wireless communication devices, e.g., Wi-Fi cards, the whole building layout can be reconstructed with only the received signal strength (RSS). For the reconstruction algorithms, the total variation (TV) minimization algorithm has been used to reconstruct the image. However, some false artifacts may exist in the image results due to the inherent defects of the algorithm. The artifacts can be misestimated as wall structures. In this paper, a reweighted total variation and prior information regularization algorithm named as RTV-PIR is proposed to reconstruct the image. This algorithm is based on the TV minimization of the image. And the prior information that the wall is only oriented horizontally or vertically is also considered simultaneously, which is used to keep the wall orientation. To verify the performance of the proposed algorithm, the simulations and experiments based on real data are conducted. It is shown that the RTV-PIR algorithm can get a better result with respect to the root mean square error (RMSE) and the structural similarity (SSIM), in comparison with the state-of-the-art algorithms.

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“…[38] explores the BCS method to achieve compressive obstacle mapping. By incorporating heterogeneous noise prior models into BCS, heterogeneous BCS [20] is developed to enhance the compressive RTI performance. Although CS-based reconstruction methods enable RTI more efficient and improve the localization accuracy to a certain extent, the target’s profile in the attenuation image would be destroyed.…”

Section: Related Workmentioning

confidence: 99%

“…However, it requires reasonable assumption of priori distribution and is computationally intensive. It is also reported that the localization accuracy of BCS is less accurate than that of Tikhonov [20], but the advantage of CS over Tikhonov is that the reconstructed image is more cleaner. Few researchers have paid their attention to study the combination of above regularizations for RTI.…”

Section: Introductionmentioning

confidence: 99%

Compressive Sensing Based Radio Tomographic Imaging with Spatial Diversity

Liu

Gao

et al. 2019

Sensors

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Radio tomographic imaging (RTI) has emerged as a promising device-free localization technology for locating the targets with no devices attached. RTI deduces the location information from the reconstructed attenuation image characterizing target-induced spatial loss of radio frequency measurements in the sensing area. In cluttered indoor environments, RF measurements of wireless links are corrupted by multipath effects and thus less robust to achieve a high localization accuracy for RTI. This paper proposes to improve the quality of measurements by using spatial diversity. The key insight is that, with multiple antennae equipped, due to small-scale multipath fading, RF measurement variation of each antenna pair behaves differently. Therefore, spatial diversity can provide more reliable and strong measurements in terms of link quality. Moreover, to estimate the location from the image more precisely and make the image more identifiable, we propose using a new reconstruction regularization linearly combining the sparsity and correlation inherent in the image. The proposed reconstruction method can remarkably reduce the image noise and enhance the imaging accuracy especially in the case of a few available measurements. Indoor experimental results demonstrate that compared to existing RTI improvement methods, our RTI solution can reduce the root-mean-square localization error at least 47% while also improving the imaging performance.

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Enhanced radio tomographic imaging with heterogeneous Bayesian compressive sensing

Cited by 31 publications

References 16 publications

Noise Adaptive Optimization Scheme for Robust Radio Tomographic Imaging Based on Sparse Bayesian Learning

Noise Adaptive Optimization Scheme for Robust Radio Tomographic Imaging Based on Sparse Bayesian Learning

Through-the-Wall Image Reconstruction via Reweighted Total Variation and Prior Information in Radio Tomographic Imaging

Compressive Sensing Based Radio Tomographic Imaging with Spatial Diversity

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