Group teaching optimization with improved Chan-Taylor algorithm for 3D indoor localization

Abstract

In recent years, indoor localization has become a hot research topic and several researchers have paid attention to design accurate 3-Dimensional (3D) indoor localization techniques. Existing localization techniques cannot be sufficient for 3D indoor localization due to the existence of interferences and the need for high precision. The most widely used option for outdoor localization is known as the global positioning system (GPS), and its precision in location is often within a 10-meter margin of error. Problems arise in the "final mile" of the localization field due to the complicated impediments that buildings provide, which encourages a momentum of indoor localization. Traditional methods of indoor localization are either dependent on range or fingerprinting, which means that the predeployment process takes a significant amount of time and work to complete. The non-line of sight propagation due to the building shielding and background interferences results in high error on the application of Ultra-Wideband technologies to indoor localization. For resolving this issue, this paper presents a novel group teaching optimization algorithm with improved Chan-Taylor algorithm (GTOA-ICTA) for 3D indoor localization. The GTO-ICTA technique initially employs the GTOA for transforming the target problem into the target of variations among the determined and original positions. Besides, the ICTA technique is employed for computing the accurate positions of the targets. The integration of GTOA and ICTA techniques helps to properly achieve maximum localization performance. For validating the enhanced performance of the presented GTOA-ICTA technique, a set of simulations were performed and the results are examined interms of different aspects. A comprehensive results analysis of the proposed GTOA-ICT technique takes place in positioning error with the minimum error of 0.1245, and the proposed method has reached increased results with the maximum error of 0.9660. Moreover the GTOA-ICT has obtained minimum average error of 0.6432, least Standard deviation of 0.4387. The resultant experimental outcomes highlighted the enhanced performance of the GTOA-ICTA techniques over the recent state of art techniques interms of different measures.