Indoor Wi-Fi positioning algorithm based on combination of Location Fingerprint and Unscented Kalman Filter

Khan, M Sadiq Ali; Kai, Yang; Gul, Haris Ubaid

doi:10.1109/ibcast.2017.7868127

Cited by 19 publications

(10 citation statements)

References 6 publications

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“…The reference [35] proposed a hybrid structure of the KNN WiFi fingerprint location and dead reckoning method, which achieves a reasonable level of accuracy in different scenes, but ignores the system overhead in offline and online phases and the requirement of three-dimensional location.…”

Section: B Pdr Positioning Methodsmentioning

confidence: 99%

“…Based on PDR method mentioned in document [30], this paper uses high and low thresholds to deal with the problem of gait detection in mixed motion mode to complete PDR localization. Finally, according to the fusion positioning system proposed in reference [35], MDS fingerprint positioning technology and PDR method are fused by using unscented Kalman filter to reduce the time required for each positioning of the fusion system. When walking inside the floor, the output three-dimensional position is the threedimensional coordinate contain the height of the floor.…”

Section: Wifi/mems Fusion Positioningmentioning

confidence: 99%

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A Multi-Floor Location Method Based on Multi-Sensor and WiFi Fingerprint Fusion

et al. 2020

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Section: B Pdr Positioning Methodsmentioning

confidence: 99%

Section: Wifi/mems Fusion Positioningmentioning

confidence: 99%

A Multi-Floor Location Method Based on Multi-Sensor and WiFi Fingerprint Fusion

et al. 2020

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“…The measured RSSI of a user from N Wi-Fi access points at a particular location during the experiment time is expressed as the vector δ = δ 1 , δ 2 , δ 3 , ........, δ N |δ k ∈ R k . The Euclidean distance D i between fingerprint maps and the corresponding measured values can be expressed as in [42]:…”

Section: Wi-fi Fingerprint Algorithmmentioning

confidence: 99%

A Sensor Fusion Framework for Indoor Localization Using Smartphone Sensors and Wi-Fi RSSI Measurements

2019

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Sensor fusion frameworks for indoor localization are developed with the specific goal of reducing positioning errors. Although many conventional localization frameworks without fusion have been improved to reduce positioning error, sensor fusion frameworks generally provide a further improvement in positioning accuracy. In this paper, we propose a sensor fusion framework for indoor localization using the smartphone inertial measurement unit (IMU) sensor data and Wi-Fi received signal strength indication (RSSI) measurements. The proposed sensor fusion framework uses location fingerprinting and trilateration for Wi-Fi positioning. Additionally, a pedestrian dead reckoning (PDR) algorithm is used for position estimation in indoor scenarios. The proposed framework achieves a maximum of 1.17 m localization error for the rectangular motion of a pedestrian and a maximum of 0.44 m localization error for linear motion.

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“…where the received RSSI data is recorded as R, and d denotes the distance between the target and the access point, N represents the path loss exponent that reflects the environment, and A represents the path loss at the reference distance, which is usually set as 1 m. Trilateration is one of the most widely used algorithms in positioning [14]- [16] since it can be easily realized by existing wireless infrastructures. The position of the target is determined by the intersection point formed by three circles, the radial distance of which is calculated from the RSSI by applying it to Eq.…”

Section: Weighted Trilaterationmentioning

confidence: 99%

Combination of Statistical Access Point Selection Methods Based on RSSI in Indoor Positioning System

Tateno

et al. 2019

SICE Journal of Control, Measurement, and System Integration

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Recently, as wireless infrastructure has developed widely, and smartphones have become necessary in daily life, indoor positioning devices and applications have become more and more popular. Previous studies have proposed several methods based on different wireless communication technologies. Among them, methods with received signal strength indicator (RSSI) values and trilateration methods are mainly used to obtain positioning results. However, due to abnormal RSSI values caused by noise and influence from the environment, the accuracies of these methods are not satisfying. Therefore, a new method which can reduce the positioning error is necessary. In this paper, to improve the positioning accuracy above trilateration results, an access point selection method and a kernel density estimation method are combined to obtain estimated points. Experiments are designed in actual environments, and the results of which show that the proposed method is sufficient for improving the positioning accuracy.

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Indoor Wi-Fi positioning algorithm based on combination of Location Fingerprint and Unscented Kalman Filter

Cited by 19 publications

References 6 publications

A Multi-Floor Location Method Based on Multi-Sensor and WiFi Fingerprint Fusion

A Multi-Floor Location Method Based on Multi-Sensor and WiFi Fingerprint Fusion

A Sensor Fusion Framework for Indoor Localization Using Smartphone Sensors and Wi-Fi RSSI Measurements

Combination of Statistical Access Point Selection Methods Based on RSSI in Indoor Positioning System

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