BLE-Based Outdoor Localization With Two-Ray Ground-Reflection Model Using Optimization Algorithms

In outdoor positioning, the global positioning system (GPS) is currently the most commonly used method. Considering the power consumption required to use GPS, it may not be efficient for tracking in smaller areas, such as outdoor grazing areas in Japan, and using wireless sensor networks seems more feasible. There are several methods for BLE-based positioning. Because the angles of arrival (AoA) and time of flight (ToF) require additional equipment, the RSSI-based localization method is the most cost-efficient. Owing to the outdoor environment, the RSSI transmission model follows a two-ray ground-reflection model, this can lead to large errors in the trilateration positioning method. On the other hand, fingerprint positioning requires the creation and maintenance of a large database. This paper proposes an RSSI-based positioning method formulated as an optimization problem. We evaluated the performance of various algorithms in two application scenarios. Our simulation results show that a high localization accuracy can be obtained using this localization method. In contrast to other methods, this approach does not necessitate the use of supplementary equipment, as is the case with AoA and ToF, nor does it require the establishment and upkeep of an RSSI fingerprint database.