An Improved Hybrid Indoor Positioning Algorithm via QPSO and MLP Signal Weighting

作     者：Edgar Scavino Mohd Amiruddin Abd Rahman Zahid Farid 

作者机构：Department of PhysicsFaculty of ScienceUniversiti Putra MalaysiaUPM Serdang43400Malaysia Department of Electrical EngineeringAbasyn UniversityKhyber-PakhtunkhwaPakistan 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2023年第74卷第1期

页      面：379-397页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 080203[工学-机械设计及理论] 0835[工学-软件工程] 0802[工学-机械工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：QPSO indoor localization fingerprinting neural networks WiFi WSN 

摘      要：Accurate location or positioning of people and self-driven devices in large indoor environments has become an important necessity The application of increasingly automated self-operating moving transportation units,in large indoor spaces demands a precise knowledge of their *** like WiFi and Bluetooth,despite their low-cost and availability,are sensitive to signal noise and fading *** these reasons,a hybrid approach,which uses two different signal sources,has proven to be more resilient and accurate for the positioning determination in indoor ***,this paper proposes an improved hybrid technique to implement a fingerprinting based indoor positioning,using Received Signal Strength information from available Wireless Local Area Network access points,together with the Wireless Sensor Networks *** signals were recorded on a regular grid of anchor points,covering the research *** optimization was performed by relative signal weighting,to minimize the average positioning error over the research *** optimization process was conducted using a standard Quantum Particle Swarm Optimization,while the position error estimate for all given sets of weighted signals was performed using aMultilayer Perceptron(MLP)neural *** to our previous research works,the MLP architecture was improved to three hidden layers and its learning parameters were finely *** experimental results led to the 20%reduction of the positioning error when a suitable set of signal weights was calculated in the optimization *** final achieved value of 0.725 m of the location incertitude shows a sensible improvement compared to our previous results.