A New RSS Fingerprinting-Based Location Discovery Method Under Sparse Reference Point Conditions

Li, Ang; Fu, Jingqi; Yang, An-Shik; Shen, Huaming

doi:10.1109/access.2019.2893398

Cited by 12 publications

(5 citation statements)

References 38 publications

(54 reference statements)

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“…The basic idea is to use the spatial differences of the access points in different locations, and use the wireless signal characteristics of the reference point as the characteristics of the physical location to determine the location. This feature is called fingerprint, and the received signal strength(RSS) is a typical fingerprint feature [5][6][7].Begin the Introduction two lines below the Keywords. The manuscript should not have headers, footers, or page numbers.…”

Section: Introductionmentioning

confidence: 99%

Fingerprint map construction based on multi-chain interpolation

2022

International Conference on Signal Processing and Communication Security (ICSPCS 2022)

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The widespread deployment of wireless devices in indoor environments has made location-based services a hot research topic. Indoor positioning based on received signal strength is the key to providing accurate location services among them. But it needs to build a fingerprint database for the sensing area. Therefore, whether it is to establish or update the fingerprint map, a large number of RSS values of the reference points need to be sampled. This process is time-consuming and laborintensive, with a huge amount of work. To solve this problem, researchers collect RSSs of some reference points and use them to interpolate other points to form a map of the entire area. This method can effectively reduce the time to create and update the map, but it also reduces the positioning accuracy. According to the propagation characteristics of wireless signals, the signal of the insertion points are formed by the superposition of multiple directional signals. Therefore, the correlation of neighboring points should not be considered only, but should be expanded in different directions. According to the propagation characteristics of actual signals, this paper designs a method based on multi-chain interpolation, which combines the influence of different propagation links on the insertion point to evaluate the signal strength. The basic idea of this method is to perform interpolation calculation in different directions under the given sampling rules. Then the predicted values of insertion points are obtained by using inverse distance weighting. Next, the corresponding signal attenuation Models are obtained by fitting in each direction and the errors are calculated as the direction weights. Finally, the estimation values of the insertion points are obtained. Through repeated iteration, the fingerprint database composed of real points and virtual points is finally formed. Two sampling models are used in this paper. And the sampling rates are 25% and 50% of that of full sampling, which means that the workload of map construction is reduced by 75% and 50% respectively. According to large-scale experiments, the positioning accuracy of the two MCI sampling methods is 13.58% and 4.74% higher than that of the full sampling method respectively. Compared with the classical interpolation method, the MCI method has better stability. Especially when the sampling amount is small, the advantage is more obvious. When the sampling amount is only 25%, the average accuracy is 18.50% higher than that of the full sampling method.

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Section: Introductionmentioning

confidence: 99%

Fingerprint map construction based on multi-chain interpolation

2022

International Conference on Signal Processing and Communication Security (ICSPCS 2022)

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“…LPS conception allows the proximity between targets and sensors to reduce adverse effects on the physical properties measured to compute location. LPS are distinguished and classified by the physical property measured: time [9], angle [10], power [11], phase [12], frequency [13] or combinations of them [14] [15]. Among these systems, Time-Based Positioning Systems have the better combination between accuracy, stability, robustness and easy-to-implement hardware design.…”

Section: Introductionmentioning

confidence: 99%

Optimized Cost-Effective Node Deployments in Asynchronous Time Local Positioning Systems

2020

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Asynchronous Time Local Positioning Systems are emerging as a decisive tool for high-demanded accuracy applications. Its relevance relies on the unnecessary synchronism of the system devices and the ad-hoc node deployment for fitting the design requirements in irregular scenarios. In this paper, we provide a new methodology for obtaining optimized cost-effective asynchronous node deployments based on system accuracy, enhanced primary and emergency operating conditions and security robustness. In addition, we perform a deep analysis of the NP-Hard node location problem and we propose a new Cramér-Rao Bound (CRB) error characterization considering Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) system connections and clock instabilities for evaluating the quality of a node deployment. We apply a Genetic Algorithm optimization in an irregular scenario of simulations to display this innovative methodology with a trade-off between resolution in the search in the space of solutions and the achievement of time-effective results. Results show that deployments with 4 and 5 coordinator sensors fulfill the design requirements in the proposed scenario in both primary and emergency conditions (1.14 and 1.70 meters and 0.89 and 1.47 meters of mean errors respectively) while 5 coordinator sensor configurations outperform 4 coordinator sensor configurations in system security robustness proving their preeminence in this study.

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“…Localization can be done in different ways, however, many of these methods can be clustered into one category [4]- [7]. The salient feature of this type is cite survey in certain scene before localization.…”

Section: Introductionmentioning

confidence: 99%

A SOCP-Based Automatic Visual Fingerprinting Method for Indoor Localization System

Yin¹,

Tan

et al. 2019

IEEE Access

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With the development of fingerprinting-based visual localization technology, a problem with this method is standing out, which is it takes great expenses in fingerprint collection. Recently, a few studies proposed some methods to alleviate this problem. However, the accuracy of the existing method is relatively low under some scenarios such as wide field of vision. In this paper, we propose a novel automatic visual fingerprinting (AVF) method for an indoor visual localization system. We consider the performance of AVF greatly hinges on visual odometry (VO) and ego-motion estimation (EME) block, which are two different ways of estimating fingerprint coordinates. Since both visual odometry and ego-motion estimation model are inaccurate, we build the least square model by second-order cone programming (SOCP). Our SOCP-based method is proposed to deal with the serious cumulative error and the random error introduced by VO and EME model, respectively. The purpose of this paper is improving the accuracy of the database generated by the AVF method under wide field of vision scenarios. Although the time costs are relatively higher than our compared method, fortunately, it is only on the off-line stage. The simulation results show that our method can provide a reliable image-location database with the consumer-grade smartphone camera.

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A New RSS Fingerprinting-Based Location Discovery Method Under Sparse Reference Point Conditions

Cited by 12 publications

References 38 publications

Fingerprint map construction based on multi-chain interpolation

Fingerprint map construction based on multi-chain interpolation

Optimized Cost-Effective Node Deployments in Asynchronous Time Local Positioning Systems

A SOCP-Based Automatic Visual Fingerprinting Method for Indoor Localization System

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