Abstract
This paper presents an ensemble learning particle swarm optimization (ELPSO) algorithm for real-time indoor localization based on ultra-wideband (UWB). Indoor localization problem can be formulated as an optimization problem to predict the target. The proposed algorithm expands the original PSO into ELPSO under superbest guide, which is a parameter employed to identify the top gbest by learning from three individual algorithms and updated asynchronously. The performance of the proposed ELPSO is evaluated by using the CEC2005 benchmark and compared with each individual algorithm and other state-of-the-art optimization algorithms. The feasibility of the proposed ELPSO is demonstrated in both 2D and 3D UWB indoor localization system generating promising results.
Highlights
With the popularization of smart devices and the development of mobile Internet, there is an increasing demand for indoor positioning
5 Experimental results and discussion A set of experiments was conducted to verify the performance of the proposed algorithm ensemble learning particle swarm optimization (ELPSO) and its application in UWB indoor localization
6 Conclusions In this paper, we propose the ELPSO, a particle swarm optimization algorithm composed of three variants of Particle swarm optimization (PSO) under super best guide
Summary
With the popularization of smart devices and the development of mobile Internet, there is an increasing demand for indoor positioning. Diverse technologies have been developed for precise indoor localization. GPS location signals are not able to penetrate buildings; they are unable to work indoors. In order to overcome the GPS positioning defects and realize the accurate positioning in the complex indoor environment, many practical indoor localization schemes are introduced, such as infrared, WIFI, Bluetooth, ZigBee, ultrasound, radio frequency identification (RFID), and ultra-wideband (UWB). WIFI [2], Bluetooth [3], and ZigBee [4] can only locate the area of about a few tens of meters, and its positioning accuracy can only reach 3 m, unable to meet the indoor mobile positioning demand.
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