A Trainingless WiFi Fingerprint Positioning Approach Over Mobile Devices

Bisio, Igor; Cerruti, M.; Lavagetto, Fabio; Marchese, Mario; Pastorino, Matteo; Randazzo, Andrea; Sciarrone, Andrea

doi:10.1109/lawp.2014.2316973

Cited by 84 publications

(38 citation statements)

References 20 publications

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“…Nevertheless, this simple idea produces inaccurate radio maps, which decrease the accuracy of the location estimation. Consequently, many other attempts have been proposed, including the free-calibration methods [13,14,15,16,17,18,19,20], user-aided methods [21,22] and the auto-update technologies [23]. …”

Section: Introductionmentioning

confidence: 99%

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A Novel Method for Constructing a WIFI Positioning System with Efficient Manpower

Yang

Xiu

2015

Sensors

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With the rapid development of WIFI technology, WIFI-based indoor positioning technology has been widely studied for location-based services. To solve the problems related to the signal strength database adopted in the widely used fingerprint positioning technology, we first introduce a new system framework in this paper, which includes a modified AP firmware and some cheap self-made WIFI sensor anchors. The periodically scanned reports regarding the neighboring APs and sensor anchors are sent to the positioning server and serve as the calibration points. Besides the calculation of correlations between the target points and the neighboring calibration points, we take full advantage of the important but easily overlooked feature that the signal attenuation model varies in different regions in the regression algorithm to get more accurate results. Thus, a novel method called RSSI Geography Weighted Regression (RGWR) is proposed to solve the fingerprint database construction problem. The average error of all the calibration points’ self-localization results will help to make the final decision of whether the database is the latest or has to be updated automatically. The effects of anchors on system performance are further researched to conclude that the anchors should be deployed at the locations that stand for the features of RSSI distributions. The proposed system is convenient for the establishment of practical positioning system and extensive experiments have been performed to validate that the proposed method is robust and manpower efficient.

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Section: Introductionmentioning

confidence: 99%

“…Another system, QRLoc [14], automatically collects fingerprints when smartphone users scan Quick Response (QR) codes attached at known and fixed locations such as signs and posts. This method requires placing the QR codes in advance.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for Constructing a WIFI Positioning System with Efficient Manpower

Yang

Xiu

2015

Sensors

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“…1 Although GPS is generally used outdoors, its localization accuracy weakens in indoor environments. [2][3][4] In last several years, Wi-Fi 5 and Bluetooth 6 techniques have been proposed in indoor localization. Compared to Bluetooth, Wi-Fi infrastructures are more widely deployed in indoor environments such as supermarkets, airports, shopping malls, and office building.…”

Section: Introductionmentioning

confidence: 99%

An improved neighbor-correlation-extended-Kalman-filter fusion method for indoor navigation

Yang

Cheng

2017

International Journal of Distributed Sensor Networks

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The received signal strength-based fingerprinting navigation system is able to provide location information with accuracies in the meter region under the assistance of inertial measuring units. However, the computational complexity in mobile terminal of this cooperation method is great for real-time position. The inertial measuring unit has the drawback of error drift, and not all the device has a self-contained unit. In order to obtain high-accurate and continuous navigation information for indoor general devices in small computations, a novel combination of fusing extended Kalman filter and fingerprinting navigation algorithm, including K-nearest neighbor and Pearson correlation coefficient, is proposed in this article. A prototype of the improved system has been worked in a real scenario. A laptop on a four-wheel handcart is moving at a constant speed in a building storey, and the measurement localization is acquired by fingerprinting algorithm during online phase. Meanwhile, the modification localization is produced by extended Kalman filter when the target is moving in the floor. Finally, compared to K-nearest neighbor, Pearson correlation coefficient, and a combination of both, the final modification localization value is more accurate. The results show that the mean error is 53.2%, 51%, and 25.8% lower than the other three methods.

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“…Bisio et al [18] proposed a method in which propagation map is not defined by means of sampling space but instead on pure physical models of finite-difference time-domain simulations. This approach did not properly address the effects of other devices and movable objects; thus, this method can be problematic in real life situations.…”

Section: Introductionmentioning

confidence: 99%

A Self-Adaptive Model-Based Wi-Fi Indoor Localization Method

Tuta

Jurič

2016

Sensors

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This paper presents a novel method for indoor localization, developed with the main aim of making it useful for real-world deployments. Many indoor localization methods exist, yet they have several disadvantages in real-world deployments—some are static, which is not suitable for long-term usage; some require costly human recalibration procedures; and others require special hardware such as Wi-Fi anchors and transponders. Our method is self-calibrating and self-adaptive thus maintenance free and based on Wi-Fi only. We have employed two well-known propagation models—free space path loss and ITU models—which we have extended with additional parameters for better propagation simulation. Our self-calibrating procedure utilizes one propagation model to infer parameters of the space and the other to simulate the propagation of the signal without requiring any additional hardware beside Wi-Fi access points, which is suitable for real-world usage. Our method is also one of the few model-based Wi-Fi only self-adaptive approaches that do not require the mobile terminal to be in the access-point mode. The only input requirements of the method are Wi-Fi access point positions, and positions and properties of the walls. Our method has been evaluated in single- and multi-room environments, with measured mean error of 2–3 and 3–4 m, respectively, which is similar to existing methods. The evaluation has proven that usable localization accuracy can be achieved in real-world environments solely by the proposed Wi-Fi method that relies on simple hardware and software requirements.

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A Trainingless WiFi Fingerprint Positioning Approach Over Mobile Devices

Cited by 84 publications

References 20 publications

A Novel Method for Constructing a WIFI Positioning System with Efficient Manpower

A Novel Method for Constructing a WIFI Positioning System with Efficient Manpower

An improved neighbor-correlation-extended-Kalman-filter fusion method for indoor navigation

A Self-Adaptive Model-Based Wi-Fi Indoor Localization Method

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