Enhance RSS‐Based Indoor Localization Accuracy by Leveraging Environmental Physical Features

Peng, Xiang; Ji, Peng; Zhang, Dian

doi:10.1155/2018/8956757

Cited by 18 publications

(10 citation statements)

References 38 publications

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“…To derive a motion in free space, we consider first the state of the art in radio-based localization. In general localization research works are mainly classified into two categories: Range-based schemes [7][8][9][10][11] and range-free schemes [12][13][14][15]. Range-based schemes estimate the target's position by triangulation or trilateration, given the estimated distance between the target and at least three reference points.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian Inference Approach for Location-Based Micro Motions using Radio Frequency Sensing

Maluf

Elnakeeb

Silverman

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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The Bayesian inference is leveraged for target tracking from radio signals collected from access points (APs). The density of the moving object as well as its distance from the transmitted wireless signal substantially affect the signal strength arriving at the receiving end. The target tracking objective is formulated as an inference problem, by which we show how the Bayesian framework can be exploited to infer the parameters of interest for a given physics model. The channel state information (CSI) is collected from wireless APs, on which the inference is carried out. We employ a non-linear forward physics model of propagation, where we differentially infer the location, the velocity, and the fractional area of the moving surfaces in 3D space, versus time. The optimization is conducted via a Levenberg-Marquardt algorithm with analytically derived Jacobian and prior. The proposed model easily scales for any given number of access points. Experiments are conducted on Cisco wireless 4800 Access Point series; operating at 5 GHz radio frequency, and the probabilistic results for position and effective surface area estimates are provided, as well as numerical results for the point spread function from the statistics of the surface location.

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Section: Introductionmentioning

confidence: 99%

A Bayesian Inference Approach for Location-Based Micro Motions using Radio Frequency Sensing

Maluf

Elnakeeb

Silverman

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…It adopts a statistical model to depict change rules of the signal by using the variance of RSS caused by user motion. Xiang et al [27] propose an approach to enhance the traditional RSS-based indoor localization precision by using features of light, temperature, and humidity information. Wilson and Patwari [28] propose radio tomographic imaging (RTI) technology for imaging the attenuation.…”

Section: Introductionmentioning

confidence: 99%

A Survey on Human Behavior Recognition Using Channel State Information

et al. 2019

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Recently, device-free human behavior recognition has become a hot research topic and has achieved significant progress in the field of ubiquitous computing. Among various implementation, behavior recognition based on WiFi CSI (channel state information) has drawn wide attention due to its major advantages. This paper investigates more than 100 latest CSI based behavior recognition applications within the last 6 years and presents a comprehensive survey from every aspect of human behavior recognition. Firstly, this paper reviews general behavior recognition applications using the WiFi signal and presents the basic concept of CSI and the fundamental principle of CSI-based behavior recognition. This paper analyzes the key components and core characteristics of the system architecture of human behavior recognition using CSI. Afterward, we divide the sensing procedures into many steps and summarize the typical studies from these steps, including base signal selection, signal preprocessing, and identification approaches. Next, based on the recognition technique, we classify the applications into three groups, including patternbased, model-based, and deep learning-based approach. In every group, we categorize the state-of-the-art applications into three groups, including coarse-grained specific behavior recognition, fine-grained specific behavior recognition, and activity inference. It elaborates the typical behavior recognition applications from five aspects, including experimental equipment, experimental scenario, behavior, classifier, and system performance. Then, this paper presents comprehensive discussions about representative applications from the implementation view and outlines the major consideration when developing a recognition system. Finally, this article concludes by analyzing the open issues of CSI-based behavior recognition applications and pointing out future research directions. INDEX TERMS Channel state information (CSI), deep learning, human behavior recognition, model, pattern, WiFi.

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“…By contrast, range-free techniques are mainly based on fingerprint matching [10][11][12][13][14][15][16]. Fingerprint matching is a classification technique that utilizes deep-learning techniques to recognize and match the unique signal features form the target device, at the given location, to a database of previously recorded data [14]. Even though the matched location is returned with relatively high precision, fingerprinting is time-consuming and sensitive to any changes in the environment, which requires continuous database maintenance.…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Empirical AoA Localization Technique for Indoor Applications

Alma'aitah

Alsaify

Bani-Hani

2019

Sensors

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Small and pervasive devices have been increasingly used to identify and track objects automatically. Consequently, several low-cost localization schemes have been proposed in the literature based on angle of arrival (AoA), time difference of arrival (TDoA), received signal strength indicator (RSSI) or their combinations. In this paper, we propose a three-dimensional empirical AoA localization (TDEAL) technique for battery-powered devices. The proposed technique processes the AoA measurements at fixed reader nodes to estimate the locations of the tags. The proposed technique provides localization accuracy that mitigates non-linear empirical errors in AoA measurements. We utilize two omni-directional antenna arrays at each fixed reader node to estimate the location vector. With multiple location estimations from different fixed reader nodes, each estimated location is assigned a weight that is inversely proportional to the AoA phase-difference error. Furthermore, the actual AoA parabolic formula of the location is approximated to a cone to simplify the location calculation process. The proposed localization technique has a low hardware cost, low computational requirements, and precise location estimates. Based on the performance evaluation, significant location accuracy is achieved by TDEAL; where, for instance, an average error margin of less than 13 cm is achieved using 10 readers in an area of 10 m × 10 m . TDEAL can be utilized to provide reference points when integrated with a relative (e.g., inertial navigation systems) localization systems.

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Enhance RSS‐Based Indoor Localization Accuracy by Leveraging Environmental Physical Features

Cited by 18 publications

References 38 publications

A Bayesian Inference Approach for Location-Based Micro Motions using Radio Frequency Sensing

A Bayesian Inference Approach for Location-Based Micro Motions using Radio Frequency Sensing

A Survey on Human Behavior Recognition Using Channel State Information

Three-Dimensional Empirical AoA Localization Technique for Indoor Applications

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