Impact of Radio Map Size on Indoor Localization Accuracy

Abstract

AbstractNowadays Indoor Positioning Systems (IPS) are attracting attention in literature because of Global Positioning System (GPS) challenge to track and navigate indoors. These IPSs intend to provide information about a wireless object’s current position indoor. GPS-based localization is the most successful Location-Based Service (LBS) application deployed in an outdoor environment. However, GPS faces a challenge of the line of sight indoor. GPS is affected extensively by the multipath-effects. IPS technologies such as Wi-Fi are deployed for indoor localization, in an attempt to alleviate the GPS indoor challenges. Most IPS employs the Fingerprinting algorithm, whereby a radio-map is generated during the offline phase by collecting measurements of Received Signal Strength Indicator (RSSI) at known locations and, positioning of devices at an unknown location is performed during the online phase by utilizing Machine Learning (ML) Algorithms. The radio-map dataset plays a major role in the accuracy performance of the classifiers deployed during the online phase. RSSI fluctuates indoors because of fading, interferences, and shadowing, therefore, the correction of the radio-map RSSI measurements is mandatory to improve the classifiers performance. In this paper, we looked into the impact of the size of the calibration radio-map on the accuracy of the predictive model. We applied the Mean and Standard Deviation filter on three datasets of different sizes to reduce the RSSI instability at each required point and conducted comparative performance on how ML classification models perform on the three radio-map different in size. The radio-map was generated using our EMPsys application. The results of the simulations show that the accuracy of the Kernel Naïve Bayes significantly improved with filter as the radio-map size increased, from 64.4% with 453 observations in the first scenario to 95.4% with 1054 observations in the third scenario. We, therefore, conclude that the performance of the classifier to be used during the online phase of the fingerprinting algorithm relies on both the size of the radio-map and the filtering methods used to correct the RSSI measurements of the radio-map.KeywordsFingerprintingMeanStandard deviationRSSIGPSWi-FiIPSLBS