Spatiotemporal Radio Tomographic Imaging with Bayesian Compressive Sensing for RSS-Based Indoor Target Localization

Shang, Baolin; Tan, Jiaju; Xia, Hong; Guo, Xuemei; Wang, Guoli; Liu, Gonggui; Xue, Shouren

doi:10.1007/978-3-319-68542-7_45

Cited by 4 publications

(3 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The deployment method and algorithm design of anchor nodes are a research focus of target localization. In experiment tests performed by many researchers, the coordinate location distribution of anchor nodes generally occurs in the shape of a square, rectangle, and triangle [14][15][16]. Therefore, before experimentation, it is necessary to fix the location of the node in advance.…”

Section: Related Workmentioning

confidence: 99%

Wireless Sensor Network Target Localization Algorithm Based on Two‐ and Three‐Dimensional Delaunay Partitions

Shao

2021

Journal of Sensors

View full text Add to dashboard Cite

The target localization algorithm is critical in the field of wireless sensor networks (WSNs) and is widely used in many applications. In the conventional localization method, the location distribution of the anchor nodes is fixed and cannot be adjusted dynamically according to the deployment environment. The resulting localization accuracy is not high, and the localization algorithm is not applicable to three-dimensional (3D) conditions. Therefore, a Delaunay-triangulation-based WSN localization method, which can be adapted to two-dimensional (2D) and 3D conditions, was proposed. Based on the location of the target node, we searched for the triangle or tetrahedron surrounding the target node and designed the localization algorithm in stages to accurately calculate the coordinate value of the target. The relationship between the number of target nodes and the number of generated graphs was analysed through numerous experiments, and the proposed 2D localization algorithm was verified by extending it the 3D coordinate system. Experimental results revealed that the proposed algorithm can effectively improve the flexibility of the anchor node layout and target localization accuracy.

show abstract

Section: Related Workmentioning

confidence: 99%

Wireless Sensor Network Target Localization Algorithm Based on Two‐ and Three‐Dimensional Delaunay Partitions

Shao

2021

Journal of Sensors

View full text Add to dashboard Cite

show abstract

“…Then, if the reasonable prior distributional information of the target's sparse shadowing in the RTI system is utilised under the framework of SBL, even the simple noise models are enough to quantify the multipath noise [34]. After that, the robustness of the RTI system towards the multipath‐induced imaging degradation is also enhanced [35].…”

Section: Introductionmentioning

confidence: 99%

Exploring the spatial correlation in radio tomographic imaging by block‐structured sparse Bayesian learning

Tan

Zhao

Guo

et al. 2023

IET Signal Processing

Self Cite

View full text Add to dashboard Cite

“…In the reconstruction-based method, as the target only occupies a small area in the sensing network, the target-induced shadow fading can be treated as sparse [13]. Then, the methods based on sparse reconstruction, such as Compressive Sensing [14], Sparse Bayesian Leaning [15], and spatiotemporal Sparse Bayesian Leaning [16], are applied for RTI reconstruction. Meanwhile, the calculation for reconstructing the high-dimensional image is so high that the real-time DFL performance is limited.…”

Section: Introductionmentioning

confidence: 99%

Efficient Recognition of Informative Measurement in the RF-Based Device-Free Localization

Tan

Guo

Zhao

et al. 2019

Sensors

Self Cite

View full text Add to dashboard Cite

Device-Free Localization (DFL) based on the Radio Frequency (RF) is an emerging wireless sensing technology to perceive the position information of the target. To realize the real-time DFL with lower power, Back-projection Radio Tomographic Imaging (BRTI) has been used as a lightweight method to achieve the goal. However, the multipath noise in the RF sensing network may interfere with the measurement and the BRTI reconstruction performance. To resist the multipath interference in the observed data, it is necessary to recognize the informative RF link measurements that are truly affected by the target appearance. However, the existing methods based on the RF link state analysis are limited by the complex distribution of the RF link state and the high time complexity. In this paper, to enhance the performance of RF link state analysis, the RF link state analysis is transformed into a decomposition problem of the RF link state matrix, and an efficient RF link recognition method based on the low-rank and sparse decomposition is proposed to sense the spatiotemporal variation of the RF link state and accurately figure out the target-affected RF links. From the experimental results, the RF links recognized by the proposed method effectively reflect the target-induced RSS measurement variation with less time. Besides, the proposed method by recognizing the informative measurement is helpful to improve the accuracy of BRTI and enhance the efficiency in actual DFL applications.

show abstract

Spatiotemporal Radio Tomographic Imaging with Bayesian Compressive Sensing for RSS-Based Indoor Target Localization

Cited by 4 publications

References 10 publications

Wireless Sensor Network Target Localization Algorithm Based on Two‐ and Three‐Dimensional Delaunay Partitions

Wireless Sensor Network Target Localization Algorithm Based on Two‐ and Three‐Dimensional Delaunay Partitions

Exploring the spatial correlation in radio tomographic imaging by block‐structured sparse Bayesian learning

Efficient Recognition of Informative Measurement in the RF-Based Device-Free Localization

Contact Info

Product

Resources

About