An accuracy generalization benchmark for wireless indoor localization based on IMU sensor data

One major challenge in indoor localization systems is the determination of the ground truth data and generalization of accuracy results. Additionally, there isn't any existing accuracy generalization benchmark for indoor positioning systems. Existing indoor localization systems are built and validated based on the environment being considered. Therefore the results for accuracy and the precision may not be generalizable once different ground truth approaches are used to test the same system or positioning algorithm used. This calls for the need to formulate experimentation benchmarks for accuracy determination. This paper first presents selected ground truth approaches used in indoor positioning literature, and then propose benchmarking criteria based on the discrete point approach for fixing ground truth data for indoor localization with the goal of improving on the generality of the accuracy results. From our experiments, the accuracy generality analysis based on single and multiple discrete point ground truth data confirms varying accuracies using the same position estimation system. The proposed accuracy generalization benchmark is based on the similarity of accuracy results in various scenarios. Similarity of accuracies is determined based on affinity propagation.