Fuzzy support vector machines for device-free localization

Chiang, Yi-Yuan; Hsu, Wang-Hsin; Yeh, Sheng‐Cheng; Li, Yi‐Chen; Wu, Jung-Shyr

doi:10.1109/i2mtc.2012.6229338

Cited by 10 publications

(7 citation statements)

References 10 publications

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“…UWB technology for HMT can be used in two ways. The first approach is to analyze changes in the UWB channel to detect the presence [ 109 , 110 ], monitor motion [ 48 , 60 , 90 , 91 , 109 , 111 ] or even classify the human activity [ 112 , 113 ] in indoors environment or behind the obstacles, such as walls [ 92 , 114 , 115 , 116 ]. For instance, by monitoring the response in the UWB channel using the UWB radar method, it is even possible to detect balance, posture change, or oscillation while standing [ 57 ].…”

Section: State Of the Art: Emerging Technologies For The Wbs Applimentioning

confidence: 99%

Wireless Body Sensor Communication Systems Based on UWB and IBC Technologies: State-of-the-Art and Open Challenges

Čuljak

Vasić

Mihaldinec

et al. 2020

Sensors

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In recent years there has been an increasing need for miniature, low-cost, commercially accessible, and user-friendly sensor solutions for wireless body area networks (WBAN), which has led to the adoption of new physical communication interfaces providing distinctive advantages over traditional wireless technologies. Ultra-wideband (UWB) and intrabody communication (IBC) have been the subject of intensive research in recent years due to their promising characteristics as means for short-range, low-power, and low-data-rate wireless interfaces for interconnection of various sensors and devices placed on, inside, or in the close vicinity of the human body. The need for safe and standardized solutions has resulted in the development of two relevant standards, IEEE 802.15.4 (for UWB) and IEEE 802.15.6 (for UWB and IBC), respectively. This paper presents an in-depth overview of recent studies and advances in the field of application of UWB and IBC technologies for wireless body sensor communication systems.

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Section: State Of the Art: Emerging Technologies For The Wbs Applimentioning

confidence: 99%

Wireless Body Sensor Communication Systems Based on UWB and IBC Technologies: State-of-the-Art and Open Challenges

Čuljak

Vasić

Mihaldinec

et al. 2020

Sensors

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“…Although both of the fingerprinting and machine learning-based DFL approaches are based on pattern matching, machine learning approach needs to determine the relevant parameters to build the data-driven model for DFL, while the fingerprinting approach estimates the target's location by matching the radio map. Chiang et al [33] proposed an improved fuzzy SVM and applied it to DFL, but the undergoing SVM involves a quadratic programming problem, which is computationally expensive. Song et al [34] proposed a scheme for DFL based on Gaussian process (GP) and particle filter, but similar to SVM, GP is still time-consuming, and hard to deal with DFL with big data.…”

Section: Device-free Localizationmentioning

confidence: 99%

Multilayer probability extreme learning machine for device-free localization

Zhang

Xiao

et al. 2020

Neurocomputing

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Device-free localization (DFL) is becoming one of the new techniques in wireless localization field, due to its advantage that the target to be localized does not need to attach any electronic device. One of the key issues of DFL is how to characterize the influence of the target on the wireless links, such that the target's location can be accurately estimated by analyzing the changes of the signals of the links. Most of the existing related research works usually extract the useful information from the links through manual approaches, which are labor-intensive and time-consuming. Deep learning approaches have attempted to automatically extract the useful information from the links, but the training of the conventional deep learning approaches are time-consuming, because a large number of parameters need to be fine-tuned multiple times. Motivated by the fast learning speed and excellent generalization performance of extreme learning machine (ELM), which is an emerging training approach for generalized single hidden layer feedforward neural networks (SLFNs), this paper proposes a novel hierarchical ELM based on deep learning theory, named multilayer probability ELM (MP-ELM), for automatically extracting the useful information from the links, and implementing fast and accurate DFL. The proposed MP-ELM is stacked by ELM autoencoders, so it also keeps the very fast learning speed of ELM. In addition, considering the uncertainty and redundant links existing in DFL, MP-ELM outputs the probabilistic estimation of the target's location instead of the deterministic output. The validity of the proposed MP-ELM-based DFL is evaluated both in the indoor and the outdoor environments, respectively. Experimental results demonstrate that the proposed MP-ELM can obtain better performance compared with classic ELM, multilayer ELM (ML-ELM), hierarchical ELM (H-ELM), deep belief network (DBN), and deep Boltzmann machine (DBM).

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“…The 'Energy-Efficient High-Precision Multi-Target-Adaptive' (E-HIPA) algorithm used compressive sensing and an adaptive orthogonal matching pursuit algorithm to track multiple targets, using a sparse link network [20]. Chiang et al [21] integrated fuzzy logic into a support vector machine (SVM) based DFL approach to improve the classification accuracy of a pure SVM DFL approach by 7.8%. Mager et al [22] sought to improve the accuracy of fingerprint-based approaches as the database degrades due to environmental changes.…”

Section: A Fingerprintingmentioning

confidence: 99%

SpringLoc: A Device-Free Localization Technique for Indoor Positioning and Tracking Using Adaptive RSSI Spring Relaxation

et al. 2019

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Device-free localization (DFL) algorithms using the received signal strength indicator (RSSI) metrics have become a popular research focus in recent years as they allow for location-based service using commercial-off-the-shelf (COTS) wireless equipment. However, most existing DFL approaches have limited applicability in realistic smart home environments as they typically require extensive offline calibration, large node densities, or use technology that is not readily available in commercial smart homes. In this paper, we introduce SpringLoc and a DFL algorithm that relies on simple parameter tuning and does not require offline measurements. It localizes and tracks an entity using an adaptive spring relaxation approach. The anchor points of the artificial springs are placed in regions containing the links that are affected by the entity. The affected links are determined by comparing the kernel-based histogram distance of successive RSSI values. SpringLoc is benchmarked against existing algorithms in two diverse and realistic environments, showing significant improvement over the state-of-the-art, especially in situations with low-node deployment density. INDEX TERMS Device-free localization (DFL), histogram distance, indoor positioning systems (IPS), smart homes, spring-relaxation.

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Fuzzy support vector machines for device-free localization

Cited by 10 publications

References 10 publications

Wireless Body Sensor Communication Systems Based on UWB and IBC Technologies: State-of-the-Art and Open Challenges

Wireless Body Sensor Communication Systems Based on UWB and IBC Technologies: State-of-the-Art and Open Challenges

Multilayer probability extreme learning machine for device-free localization

SpringLoc: A Device-Free Localization Technique for Indoor Positioning and Tracking Using Adaptive RSSI Spring Relaxation

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