A hybrid RSSI and AoA indoor positioning approach with adapted confidence evaluator

Abstract

Aiming at the indoor positioning system of hybrid received signal strength indicator (RSSI) and angle of arrival (AoA), this paper proposes an indoor positioning algorithm with an adaptive confidence based multi-objective optimization evaluator (ACMOOE) which improves the positioning accuracy by adjusting the influence of the two positioning methods adaptively. A multi-objective optimization algorithm based positioning model is established and particle swarm optimization is applied to reduce the positioning accuracy loss caused by the approximate transformation process. An adaptive confidence evaluation method of RSSI and AoA target is designed, which reduces the positioning accuracy loss caused by unreasonable weight setting. Finally, in order to verify the proposed algorithm, an indoor wireless sensing system is built in the actual indoor scene. Experimental results show that compared with the traditional hybrid positioning algorithm, the positioning error of the proposed ACMOOE is 0.45 m which improves the positioning accuracy by 18.7%.