Radio tomographic imaging and tracking of stationary and moving people via kernel distance

Zhao, Yang; Patwari, Neal; Phillips, Jeff M.; Venkatasubramanian, Suresh

doi:10.1145/2461381.2461410

Cited by 122 publications

(89 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another category of localization approaches is based on RSS fingerprint matching [32][33][34]. Researchers collect and extract temporal RSS indicators generated by a single person at each preset indoor location.…”

Section: System Modelmentioning

confidence: 99%

“…In addition, multiperson localization is still a challenging issue for RSS fingerprint matching-based DFL. However, the RSS fingerprint matching-based methods have been confirmed with good performance for single-person localization in the indoor environment [32][33][34].…”

Section: System Modelmentioning

confidence: 99%

“…The ellipse model-based methods make great efforts to build the refined spatial sampling model of each link and use the regularization algorithms to resolve ill-posed image reconstruction [16,17,21,[29][30][31]. The RSS fingerprint-based approaches seek to model the fading feature of the subject at each predefined location, and the localization is cast into a pattern-matching problem [32][33][34]. The above methods have been shown to be effective both in outdoor and indoor environments.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Enhanced Sparse Representation-Based Device-Free Localization with Radio Tomography Networks

Liu

Luo

Liang

2018

JSAN

View full text Add to dashboard Cite

Abstract:The sparse distribution of targets in monitored areas is an important prior for device-free localization (DFL) with radio tomography networks. In this article, our goal is to develop an enhanced sparse representation-based DFL method that takes the full potential of sparsity for location reconstruction. An expanded sensing matrix spanning the concatenation of a sampling matrix and a unit error-correcting base is proposed for modelling the measurement process. The sampling matrix can either be composed of the ellipse model from calibrated networks or the received signal strength (RSS) fingerprint-based model induced by training samples with one person at predefined locations. Thus, the sparsity of targets is enhanced under the expanded sensing matrix and the 1 -minimization-based approximations are derived for the recovery of locations. Experimental studies in an open outdoor scenario, in a line-of-sight (LOS) indoor scenario, and in a non-line-of-sight (NLOS) indoor scenario, are conducted to verify the efficacy of the proposed method.

show abstract

Section: System Modelmentioning

confidence: 99%

Section: System Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Enhanced Sparse Representation-Based Device-Free Localization with Radio Tomography Networks

Liu

Luo

Liang

2018

JSAN

View full text Add to dashboard Cite

show abstract

“…This method suffers from intensive computation and is not suitable for real-time localization. Radio Tomography Image (RTI) based methods [9], [13]- [15] are more effective. They localize the target in the RF propagation area using attenuation of RSS values caused by the target.…”

Section: Related Workmentioning

confidence: 99%

“…However, packet loss caused by WiFi interference is still not addressed. Their later work in [9] proposes a method to improve the localization accuracy by calculating kernel distance. In this method, with the kernel distance between a long-term histogram and a short-term histogram, it is able to obtain the temporal RSS changes caused by the target.…”

Section: Related Workmentioning

confidence: 99%

Dual-Band Sensor Network for Accurate Device-Free Localization in Indoor Environment with WiFi Interference

Man-yi

Wang

Ding

et al. 2015

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

SUMMARYRadio Frequency based Device-Free Localization (RFDFL) is an emerging localization technique without requirements of attaching any electronic device to a target. The target can be localized by means of measuring the shadowing of received signal strength caused by the target. However, the accuracy of RFDFL deteriorates seriously in environment with WiFi interference. State-of-the-art methods do not efficiently solve this problem. In this paper, we propose a dual-band method to improve the accuracy of RFDFL in environment without/with severe WiFi interference. We introduce an algorithm of fusing dual-band images in order to obtain an enhanced image inferring more precise location and propose a timestamp-based synchronization method to associate the dual-band images to ensure their one-one correspondence. With real-world experiments, we show that our method outperforms traditional single-band localization methods and improves the localization accuracy by up to 40.4% in real indoor environment with high WiFi interference.

show abstract

HCAR: Human continuous activity recognition using latent structure features

Zhao

Yang

et al. 2019

Trans Emerging Tel Tech

View full text Add to dashboard Cite

With the rapid development of the Internet of Things and the improvement of computing power, edge-computing becomes an emergency computing paradigm that communicates between the terminal and cloud. One of the most representative works of edge-computing is to achieve human-activity recognition at the edge side, as it has lower latency and it could reduce transmission costs, compared with processing at the cloud side. However, existing approaches have many drawbacks: (1) they can merely recognize separated actions as it is incompetence for continuous activity recognition; and (2) they are not robust to action transformation and environmental noise due to the value-feature-based matching strategy. In this paper, we propose HCAR, a structure-feature-based human continuous activity recognition system, which is insensitive to action transformation and environmental noise. Firstly, we leverage word2vec to embed the CSI sequences to CSI value space. Secondly, we select representative features from the embedded vectors and use HMM-LDA to cluster them into different action categories. Lastly, for each new coming sequence, we calculate the Hellinger distance and bi-modality coefficient to different categories and then identify the corresponding action(s). We implement HCAR by Intel 5300 NIC to evaluate the activity recognition precision in different cases. The experiments show that HCAR can recognize actions corresponding to the unsegmented CSI sequence with high accuracy, ie, >90%.Recently, with the wide application of mobile-computing and Internet-of-Things (IoT) technology, computer-human interaction (CHI) has become an indispensable component of social life and has a far-reaching influence for daily work and life of human beings, eg, smart home, 1 intelligent transportation, 2 and services combination. 3 Here, a question becomes as follows: where and how to place the CHI computing plan? On the one hand, terminal devices, eg, AP and receiver, the resources may be insufficient for calculation. On the other hand, computing at the cloud side and then feeding back to the terminal is time consuming and communication bandwidth wasting. Edge computing pushes the computing power away from the cloud and puts it closer to the users. Human activity interaction and recognition, one of the most representative researches of edge computing. [4][5][6] There are two main categories to implement human activity recognition, ie, camera based and noncamera based. The former 7-9 can catch human activities in a fast and exact way. In addition, camera-based solutions can implement continuous action recognition with ease. However, camera-based solutions have three main weakness: (1) they are vulnerable to privacy attack since images can offer users' over-detailed profiles;

show abstract

Radio tomographic imaging and tracking of stationary and moving people via kernel distance

Cited by 122 publications

References 28 publications

Enhanced Sparse Representation-Based Device-Free Localization with Radio Tomography Networks

Enhanced Sparse Representation-Based Device-Free Localization with Radio Tomography Networks

Dual-Band Sensor Network for Accurate Device-Free Localization in Indoor Environment with WiFi Interference

HCAR: Human continuous activity recognition using latent structure features

Contact Info

Product

Resources

About