Optimization-Based Robust Localization of JADE Agents in Indoor Environments

This paper describes and evaluates a localization algorithm that was originally designed to overcome known issues of classic geometric localization algorithms, and that is now implemented in the localization add-on module of JADE. The algorithm is designed to support self-localization of agents running on smart devices in known indoor environments, and its current implementation acquires needed ranging information from ordinary WiFi infrastructures, with no need of dedicated infrastructures. First, the agent estimates the distances of the smart device where it is running from WiFi access points by using received signal strength during ordinary network discovery. Then, the agent uses computed distance estimates to generate estimates of its position by solving an appropriate optimization problem using particle swarm optimization. The robustness of the algorithm is discussed in the last part of the paper by comparing the performance of the algorithm against the performance of a classic algorithm in a representative scenario. Presented experimental results emphasize that the described algorithm is more robust than the classic alternative because it does not suffer from well-known numerical instability problems of geometric localization algorithms.