A novel WiFi indoor positioning strategy based on weighted squared Euclidean distance and local principal gradient direction

Abstract

Purpose This paper aims to introduce the weighted squared Euclidean distance between points in signal space, to improve the performance of the Wi-Fi indoor positioning. Nowadays, the received signal strength-based Wi-Fi indoor positioning, a low-cost indoor positioning approach, has attracted a significant attention from both academia and industry. Design/methodology/approach The local principal gradient direction is introduced and used to define the weighting function and an average algorithm based on k-means algorithm is used to estimate the local principal gradient direction of each access point. Then, correlation distance is used in the new method to find the k nearest calibration points. The weighted squared Euclidean distance between the nearest calibration point and target point is calculated and used to estimate the position of target point. Findings Experiments are conducted and the results indicate that the proposed Wi-Fi indoor positioning approach considerably outperforms the weighted k nearest neighbor method. The new method also outperforms support vector regression and extreme learning machine algorithms in the absence of sufficient fingerprints. Research limitations/implications Weighted k nearest neighbor approach, support vector regression algorithm and extreme learning machine algorithm are the three classic strategies for location determination using Wi-Fi fingerprinting. However, weighted k nearest neighbor suffers from dramatic performance degradation in the presence of multipath signal attenuation and environmental changes. More fingerprints are required for support vector regression algorithm to ensure the desirable performance; and labeling Wi-Fi fingerprints is labor-intensive. The performance of extreme learning machine algorithm may not be stable. Practical implications The new weighted squared Euclidean distance-based Wi-Fi indoor positioning strategy can improve the performance of Wi-Fi indoor positioning system. Social implications The received signal strength-based effective Wi-Fi indoor positioning system can substitute for global positioning system that does not work indoors. This effective and low-cost positioning approach would be promising for many indoor-based location services. Originality/value A novel Wi-Fi indoor positioning strategy based on the weighted squared Euclidean distance is proposed in this paper to improve the performance of the Wi-Fi indoor positioning, and the local principal gradient direction is introduced and used to define the weighting function.

An RSSI Classification and Tracing Algorithm to Improve Trilateration-Based Positioning

“…Applying artificial intelligence (including statistical inference methods) for RSSI processing is a new trend, e.g., using a k-means clustering algorithm for singular RSS tracing [22] and filtering, and RSSI fingerprint matching [23,24]. Moreover, support vector machine (SVM) [43], artificial neural network (ANN) [44,45] and deep learning [46] have been proposed to aid RSSI purification and high positioning accuracy.…”

“…RSSI-based IPS is generally divided into two categories: trilateration-based IPS and fingerprint-based IPS [ 10 , 18 , 19 ]. The fingerprint-based IPS [ 20 , 21 , 22 ] gets more concern in terms of the number of references retrieved, where the number is 1,910,000 vs. 30,000 from Google Scholar. It also gains positive effect by using unsupervised machine-learning algorithms to reduce the data dimensionality, and fingerprint matching calculations required [ 23 ].…”

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“…However, the accuracy deteriorates significantly because GNSS signals are unreliable or blocked in indoor environments. To provide a reliable, stable position service in indoor environments, many types of indoor positioning technologies such as wireless fidelity (Wi-Fi) [1][2][3], Bluetooth low energy (BLE) beacons [4,5], radio frequency identification (RFID) [6,7], ultrasonic [8], infrared [9], ultra-wideband (UWB) [10,11], pseudolite [12,13], computer vision [14,15] had been proposed by experts and scholars.…”

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