Improving Indoor Localization System Using a Partitioning Technique Based on RSS and ToA

Abstract

Designing a localization system for an indoor environment faces more challenges because of multipath and interference problems. In this field, the most important techniques used for such environment, are RSS and ToA which need to be improved especially from more interference because of the huge multipath problems. In this paper, a case study of a selected building is chosen in order to apply the proposed technique of this research. Such proposal is based on the PT ‎ of the area in the case study into MZ. Each zone is allocated special values for the parameters used to estimate the target positions. WI package is used to simulate the case study area and apply such proposal based on RSS and ToA. The results confirm that the estimated locations are close to the real locations by the average error of (2.8) meter and (0.192) meter for ToA corresponding one zone and four zones ‎respectively. ‎ In contrast, the results of our experiment show that the accuracy is improved from an average error of (2.4) meter and (0.217) meter for RSS corresponding one zone and four zones ‎respectively‎. Such results confirm that dividing the case study area into more zones leads to more accuracy.

Relative Localization of Wireless Sensor Nodes by Using the RSSI and ToA based distance estimations

“…According to the test results evaluated by using mobile phones, 5 m. positioning accuracy for pedestrian speeds is achieved, however, the method is focused on the outdoor applications and indoor performance is rather weak. Mosleh, Abed, and Abbas present a case study based on the partitioning of the test area into multiple zones [12]. Each zone is constructed from a number of walls, doors, and windows and allocated specific propagation parameters used to estimate the target positions with a significantly reduced error.…”

Device-Free Localization Techniques: A Review

“…Numerous investigations have concentrated on utilizing wireless signals, such as Wi-Fi [5][6][7], Bluetooth [8], Zigbee [3], [9][10][11], Ultra-Wide-Band (UWB) [12,13] and infrared [14][15][16], for the purpose of DFL. These signals can be employed to analyze variations in parameters like Received Signal Strength (RSS) [17][18][19][20], Channel-State-Information (CSI) [21][22][23][24], Angle of Arrival (AoA) [25,26] and Time of Arrival (ToA) [27,28] in order to estimate the position of individuals. In particular, RSS-based techniques have been extensively explored, leveraging the decay of wireless signals to ascertain the proximity of individuals to access points.…”