Device-Free Localization via Dictionary Learning with Difference of Convex Programming

Li, Xiang; Ding, Shuxue; Li, Zhenni; Tan, Benying

doi:10.1109/jsen.2017.2730226

Cited by 15 publications

(5 citation statements)

References 39 publications

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“…For single-target localization, fingerprints can be exhaustively collected, but for multitarget localization, building the database becomes impractical (Sabek and Youssef 2012;Sabek, oustafa Youssef, and Vasilakos 2015). Some methods address this issue by training on data from a single person and testing on multiple people, achieving good results by using probabilistic classification models (Xu et al 2012), sequential counting and parallel localization of each target (Xu et al 2013), crosscalibration (Sabek and Youssef 2012), conditional random Markov field (Sabek, oustafa Youssef, and Vasilakos 2015), and dictionary learning (Li et al 2017). However, these methods trained on data from a single person assume target sparsity, which limits their accuracy with nearby targets.…”

Section: Related Workmentioning

confidence: 99%

Multitarget Device-Free Localization via Cross-Domain Wi-Fi RSS Training Data and Attentional Prior Fusion

Fan,

Tian,

Dubey

et al. 2024

AAAI

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Device-free localization (DFL) using easily-obtained Wi-Fi received signal strength (RSS) has wide real-world applications for not requiring people to carry trackable devices. However, accurate multitarget DFL remains challenging due to the unknown number of targets, multipath interference (MPI), especially between nearby targets, and limited real-world data. In this study, we pioneeringly propose a transformer-based learning method with Wi-Fi RSS as input, and an attentional prior fusion module, to simultaneously locate an unknown number of people at random positions. To overcome the multitarget data collection challenges, we contribute a large-scale cross-domain real-simulation-augmentation training dataset with one and two real-world nearby non-person objects at limited positions and up to five simulated and augmented randomly distributed targets. Experimental results demonstrate our method's improved accuracy, generalization ability, and robustness with fewer Wi-Fi nodes than previous methods.

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Section: Related Workmentioning

confidence: 99%

Multitarget Device-Free Localization via Cross-Domain Wi-Fi RSS Training Data and Attentional Prior Fusion

Fan,

Tian,

Dubey

et al. 2024

AAAI

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“…Here, 442 atoms represent 34 redundant individual locations. From the phase transition graph of sparse coding [45], we learn that the low dimension m may influence the success rate when finding the accurate sparse solution. The high coherence among different signals may result in a severe situation.…”

Section: Data Preprocessingmentioning

confidence: 99%

Efficient indoor localization with wireless sensor networks via learnable sparse coding

2023

Meas. Sci. Technol.

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In this study, the indoor localization problem for human movement is built as a sparse representation, and a learnable sparse coding algorithm is proposed by introducing a nonconvex penalty function as the sparse constraints. The good properties of the penalty are able to help reduce the iteration cost to find a sparse solution indicating the target locations. The sparse code can achieve its optimal value from the trained neural network model with parametrization. It is instead of a piece of algorithmic processing in the conventional methods requiring a large iteration. A real-world experiment using a Bluetooth Low Energy wireless sensor network validates the proposed method and shows the improvement of the error reduction at the first five iteration points on average compared to Iterative Shrinkage and Thresholding Algorithm (ISTA), and achieves competitive performance in the localization tasks for Learned-ISTA-based algorithms.

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“…Employing a different regularizer can drive a different type of sparse coding algorithm. During the various kinds of regularizers [11][12][13], the most famous regularizers are l 1 norm and have been popularly employed in many DFL related works [10,14]. Usually, since the elements' indices of sparse solution are associated with the grids' ID, the target is located by selecting the maximum in a sparse solution while the l 1 norm is used as the regularizer.…”

Section: Introductionmentioning

confidence: 99%

“…Contrasting to the related works [14,15] and our previous work [10] on employing l 1 norm, in this paper, we proposed to exploit the new regularizer via l 2,1 norm for measuring the block-sparsity, so that the DFL algorithm can achieve a robust process in the challenging environments. That is, the collected multiple samples are considered as one group in the sensing matrix when an obstacle is at each grid.…”

Section: Introductionmentioning

confidence: 99%

Improved Sparse Coding Algorithm with Device-Free Localization Technique for Intrusion Detection and Monitoring

et al. 2019

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Device-free localization (DFL) locates target in a wireless sensors network (WSN) without equipping with wireless devices or tags, which is an emerging technology in the fields of intrusion detection and monitoring. In order to achieve an accurate result of DFL, the conventional works adopt l 1 norm as a regularizer to take the full potential of sparsity for locating targets. Contrasting to the previous works, we exploit the l 2 , 1 norm as the regularizer and devise an efficient optimization method with a proximal operator-based scheme, which leads the proposed improved-sparse-coding algorithm with proximal operator (ISCPO). Compared with the state-of-the-art methods that adopt l 1 norm as the regularizer, the proposed algorithm can improve the joint sparsity of sparse solution. Experimental results on our real testbeds of indoor DFL show that, in scenarios of living room and corridor, the proposed approach can achieve high localization accuracies of about 100% and 90%, respectively. In addition, the proposed ISCPO algorithm outperforms the compared state-of-the-art methods and has a more robust performance in challenged environments for target localization.

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Device-Free Localization via Dictionary Learning with Difference of Convex Programming

Cited by 15 publications

References 39 publications

Multitarget Device-Free Localization via Cross-Domain Wi-Fi RSS Training Data and Attentional Prior Fusion

Multitarget Device-Free Localization via Cross-Domain Wi-Fi RSS Training Data and Attentional Prior Fusion

Efficient indoor localization with wireless sensor networks via learnable sparse coding

Improved Sparse Coding Algorithm with Device-Free Localization Technique for Intrusion Detection and Monitoring

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