Abstract
Aiming at the problem of indoor environment, signal non-line-of-sight propagation and other factors affect the accuracy of indoor locating, an algorithm of indoor fingerprint localization based on the eight-neighborhood template is proposed. Based on the analysis of the signal strength of adjacent reference points in the fingerprint database, the methods for the eight-neighborhood template matching and generation were studied. In this study, the indoor environment was divided into four quadrants for each access point and the expected values of the received signal strength indication (RSSI) difference between the center points and their eight-neighborhoods in different quadrants were chosen as the generation parameters. Then different templates were generated for different access points, and the unknown point was located by the Euclidean distance for the correlation of RSSI between each template and its coverage area in the fingerprint database. With the spatial correlation of fingerprint data taken into account, the influence of abnormal fingerprint on locating accuracy is reduced. The experimental results show that the locating error is 1.0 m, which is about 0.2 m less than both K-nearest neighbor (KNN) and weighted K-nearest neighbor (WKNN) algorithms.
Highlights
Indoor localization has become more and more significant in location based service (LBS) [1,2].Bluetooth [3,4,5,6], WIFI [7,8] and ultra-wide band (UWB) [9,10,11] are widely used in indoor localization systems based on received signal strength indication (RSSI) from the known access points (APs).Bluetooth low energy (BLE) has attracted increasing interests for its low-cost, low-power consumption and ubiquitous availability in mobile devices [12]
The unknown points are located with the best reference points (RPs), which are chosen based on the fingerprint database and the acquired RSSI from different APs
To avoid the abnormal RSSI’s influence on the locating accuracy, we propose a method for fingerprint indoor localization based on eight-neighborhood template matching (ENTM), which generates eight-neighborhood templates with the RSSI values acquired at the unknown point, and the template matching is applied to choose the best RP as the estimation of location
Summary
Indoor localization has become more and more significant in location based service (LBS) [1,2]. The unknown points are located with the best RPs, which are chosen based on the fingerprint database and the acquired RSSI from different APs. Due to the complex indoor environment, signal non-of-sight propagation and other factors, the RSSI does not always follow the signal propagation mode [14]. To avoid the abnormal RSSI’s influence on the locating accuracy, we propose a method for fingerprint indoor localization based on eight-neighborhood template matching (ENTM), which generates eight-neighborhood templates with the RSSI values acquired at the unknown point, and the template matching is applied to choose the best RP as the estimation of location.
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