Link Sensing-Adaptive Passive Object Localization in Wireless Sensor Networks

Tong, Xing; Wang, Ju; Chang, Liqiong; Liu, Chen; Fang, Dingyi; Wu, Chase Q.

doi:10.1155/2014/717534

Cited by 4 publications

(3 citation statements)

References 25 publications

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“…Machine learning has also been a well research topic in many fields for many years, such as image processing, computer vision, and signal processing [35]. Applying machine learning to address indoor localization is emering.…”

Section: Related Workmentioning

confidence: 99%

Localization by Fusing a Group of Fingerprints via Multiple Antennas in Indoor Environment

Guo

Ansari

2017

IEEE Trans. Veh. Technol.

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Abstract-Most existing fingerprints-based indoor localization approaches are based on some single fingerprint, such as received signal strength (RSS), channel impulse response, and signal subspace. However, the localization accuracy obtained by the single fingerprint approach is rather susceptible to the changing environment, multipath, and non-line-of-sight propagation. In this paper, we propose a novel localization framework by Fusing A Group Of fingerprinTs (FAGOT) via multiple antennas for the indoor environment. We first build a GrOup Of Fingerprints (GOOF), which includes five different fingerprints, namely, RSS, covariance matrix, signal subspace, fractional low-order moment, and fourthorder cumulant, which are obtained by different transformations of the received signals from multiple antennas in the offline stage. Then, we design a parallel GOOF multiple classifiers based on AdaBoost (GOOF-AdaBoost) to train each of these fingerprints in parallel as five strong multiple classifiers. In the online stage, we input the corresponding transformations of the real measurements into these strong classifiers to obtain independent decisions. Finally, we propose an efficient combination fusion algorithm, namely, MUltiple Classifiers mUltiple Samples (MUCUS) fusion algorithm to improve the accuracy of localization by combining the predictions of multiple classifiers with different samples. As compared with the single fingerprint approaches, our proposed approach can improve the accuracy and robustness of localization significantly. We demonstrate the feasibility and performance of the proposed algorithm through extensive simulations as well as via real experimental data using a Universal Software Radio Peripheral platform with four antennas.Index Terms-AdaBoost, group Of Fingerprints (GOOF), multiple antennas, multiple Classifiers mUltiple Samples (MUCUS) fusion localization, universal software radio peripheral (USRP).

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Section: Related Workmentioning

confidence: 99%

Localization by Fusing a Group of Fingerprints via Multiple Antennas in Indoor Environment

Guo

Ansari

2017

IEEE Trans. Veh. Technol.

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“…Some prior works have attempted to model the shadowing loss as a function of the target’s position, for example, elliptical model [1,13,14], exponential model [8,12] and diffraction model [7,19,33,34]. The exponential model is established through fitting extensive measurements collected from real experiments, which can be written as

S_{l} ((), {boldx}_{t}) = ϕ e^{- κ Δ d_{l} ((), {boldx}_{t})},

where

Δ d_{l} ((), {boldx}_{t}) = (∥∥ {boldx}_{t} - {boldα}_{i}) + (∥∥ {boldx}_{t} - {boldα}_{j}) - (∥∥ {boldα}_{i} - {boldα}_{j})

is the excess path length,

ϕ

is the maximum loss evaluated when

Δ d_{l} ((), {boldx}_{t}) = 0

and

κ

is the decaying factor.…”

Section: System Modelmentioning

confidence: 99%

“…In the past few years, received-signal-strength-based (RSS-based) DFL methods have gained a lot of attention because RSS measurements are available in most commercial off-the-shelf (COTS) wireless products, which can greatly reduce the cost of DFL systems. So far, RSS-based DFL methods have been successfully applied to environment monitoring [1,2,5,6,7], personnel tracking [2,8,9] and health-care [10,11]. …”

Section: Introductionmentioning

confidence: 99%

Bayesian Device-Free Localization and Tracking in a Binary RF Sensor Network

Wang

Liu

et al. 2017

Sensors

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Received-signal-strength-based (RSS-based) device-free localization (DFL) is a promising technique since it is able to localize the person without attaching any electronic device. This technology requires measuring the RSS of all links in the network constituted by several radio frequency (RF) sensors. It is an energy-intensive task, especially when the RF sensors work in traditional work mode, in which the sensors directly send raw RSS measurements of all links to a base station (BS). The traditional work mode is unfavorable for the power constrained RF sensors because the amount of data delivery increases dramatically as the number of sensors grows. In this paper, we propose a binary work mode in which RF sensors send the link states instead of raw RSS measurements to the BS, which remarkably reduces the amount of data delivery. Moreover, we develop two localization methods for the binary work mode which corresponds to stationary and moving target, respectively. The first localization method is formulated based on grid-based maximum likelihood (GML), which is able to achieve global optimum with low online computational complexity. The second localization method, however, uses particle filter (PF) to track the target when constant snapshots of link stats are available. Real experiments in two different kinds of environments were conducted to evaluate the proposed methods. Experimental results show that the localization and tracking performance under the binary work mode is comparable to the those in traditional work mode while the energy efficiency improves considerably.

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