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

Konings, Daniel; Alam, Fakhrul; Noble, Frazer; Lai, Eseng

doi:10.1109/access.2019.2913910

Cited by 31 publications

(9 citation statements)

References 51 publications

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“…Passive localization using Radio Frequency (RF) sensing has been extensively researched in recent years [4,5]. While RF-based localization has the advantage of potentially being able to repurpose the wireless networks within the built environment, there are some inherent disadvantages like limited accuracy due to multipath reflections.…”

Section: Introductionmentioning

confidence: 99%

CapLoc: Capacitive Sensing Floor for Device-Free Localization and Fall Detection

et al. 2020

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Passive indoor positioning, also known as Device-Free Localization (DFL), has applications such as occupancy sensing, human-computer interaction, fall detection, and many other location-based services in smart buildings. Vision-, infrared-, wireless-based DFL solutions have been widely explored in recent years. They are characterized by respective strengths and weaknesses in terms of the desired accuracy, feasibility in various real-world scenarios, etc. Passive positioning by tracking the footsteps on the floor has been put forward as one of the promising options. This article introduces CapLoc, a floor-based DFL solution that can localize a subject in real-time using capacitive sensing. Experimental results with three individuals walking 39 paths on the CapLoc show that it can detect and localize a single target's footsteps accurately with a median localization error of 0.026 m. The potential for fall detection is also shown with the outlines of various poses of the subject lying upon the floor.

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Section: Introductionmentioning

confidence: 99%

CapLoc: Capacitive Sensing Floor for Device-Free Localization and Fall Detection

et al. 2020

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“…The associate editor coordinating the review of this manuscript and approving it for publication was Mu-Yen Chen . time, people's location can be inferred by comparing them against pre-constructed signal profiles, which is commonly known as fingerprinting approach [3], [4]. In most of the fingerprinting-based approaches, coarse-grained Received Signal Strength (RSS) are used as the wireless signatures for localization [5]- [9]. RSS varies over distance on the order of the signal wavelength and fluctuates over time, which degrades localization performance with lower accuracy [3].…”

Section: Introductionmentioning

confidence: 99%

“…Indoor localization with device free approach has been explored in various methods [8], [9], [19], [20], [22]- [24]. These methods consider either RSS value of wireless signals or only the amplitude of CSI.…”

Section: Introductionmentioning

confidence: 99%

“…In [8], localization is performed using probabilistic classification approaches that are based on discriminant analysis of wireless sensor-based RSS value. Another RSS based localization is proposed in [9], where an adaptive spring relaxation approach is exploited for localization. However, dependence upon the coarse RSS measurements limits the performance of the above methods.…”

Section: Introductionmentioning

confidence: 99%

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A Multi-View Discriminant Learning Approach for Indoor Localization Using Amplitude and Phase Features of CSI

Sanam

Godrich

2020

IEEE Access

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Location Based Service (LBS) is one of the important aspects of a smart city. Accurate indoor localization plays a vital role in LBS. The ability to localize various subjects in the area of interest facilitates further ubiquitous environments. Specifically, device free localization using wireless signals is getting increased attention as human location is estimated using its impact on the surrounding wireless signals without any active device tagged with subject. In this paper, we propose MuDLoc, the first multi-view discriminant learning approach for device free indoor localization using both amplitude and phase features of Channel State Information (CSI) from multiple Access Points (APs). The same location oriented CSI data can be observed by different APs, thus generating multiple distinct even heterogeneous samples. Multi-view learning is an emerging technique in machine learning which improve performance by utilizing diversity from different view data. In MuDLoc, the localization is modeled as a pattern matching problem, where the target location is predicted based on similarity measure of CSI features of an unknown location with those of the training locations. MuDLoc implements Generalized Interview and Intra-view Discriminant Correlation Analysis (GI 2 DCA), a discriminative feature extraction approach that incorporates interview and intra-view class associations while maximizing pairwise correlations across multi-view data sets. Experimental results from two cluttered environments show that MuDLoc can estimate location with high accuracy which outperforms other benchmark approaches.

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“…It performs based on wireless sensor networks (WSN) formed by several wireless sensors. In recent related papers, DFL techniques have mostly been used for the two-dimensional localization of a small-scale target (e.g., people) [1][2][3][4][5][6][7]. Specifically, C.R.…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Device-Free Localization for Vehicle

Wang

Yang

Huang

et al. 2020

Sensors

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Device-free localization (DFL) is a promising technique which could provide localization information for a target without requiring an electronic device. With the development of the smart city and smart transportation, DFL could form part of a basic technique that could be used to track and localize roadside vehicles. In this paper, some algorithms for three-dimensional (3D) DFL for vehicle surveillance are developed, including statistical methods for data, a method for communication link selection, a novel method of communication link weight allocation and some other minor approaches to obtain the location and approximate size of a static vehicle, as a basic technique of moving vehicle detection. Then, an experimental system is designed. Through security monitoring wireless sensor networks (WSN), real-time vehicle characteristics (i.e., location and size) are calculated automatically and intuitively displayed through a heat map. Experiments are performed to validate the effect of the proposal and the accuracy of the localization and size estimation.

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

Cited by 31 publications

References 51 publications

CapLoc: Capacitive Sensing Floor for Device-Free Localization and Fall Detection

CapLoc: Capacitive Sensing Floor for Device-Free Localization and Fall Detection

A Multi-View Discriminant Learning Approach for Indoor Localization Using Amplitude and Phase Features of CSI

Three-Dimensional Device-Free Localization for Vehicle

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