Support Vector Regression for Mobile Target Localization in Indoor Environments

Sensors

Shashikant V. Athawale¹  Satish R. Jondhale²  Vijay Mohan³  Jaime Lloret⁴  Bharat Bhushan Sharma⁵ 

[1] Department of Computer Engineering, AISSM College of Engineering, Pune 411001, Maharashtra, India;Department of Electronics and Telecommunication, Amrutvahini College of Engineering, Sangamner 422608, Maharashtra, India;Department of Mechatronics, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India;Instituto de Investigacion para la Gestion Integrada de Zonas Costeras, Universitat Politecnica de Valencia, Grao de Gandia, 46730 Valencia, Spain;School of Automation, Banasthali Vidyapith, Tonk 304022, Rajasthan, India;

关键词: trilateration;    received signal strength (RSS);    wireless sensor network (WSN);    localization and tracking (L&T);    support vector regression (SVR);    Kalman filter (KF);   

DOI  :  10.3390/s22010358

来源: DOAJ

【 摘 要 】

Trilateration-based target localization using received signal strength (RSS) in a wireless sensor network (WSN) generally yields inaccurate location estimates due to high fluctuations in RSS measurements in indoor environments. Improving the localization accuracy in RSS-based systems has long been the focus of a substantial amount of research. This paper proposes two range-free algorithms based on RSS measurements, namely support vector regression (SVR) and SVR + Kalman filter (KF). Unlike trilateration, the proposed SVR-based localization scheme can directly estimate target locations using field measurements without relying on the computation of distances. Unlike other state-of-the-art localization and tracking (L&T) schemes such as the generalized regression neural network (GRNN), SVR localization architecture needs only three RSS measurements to locate a mobile target. Furthermore, the SVR based localization scheme was fused with a KF in order to gain further refinement in target location estimates. Rigorous simulations were carried out to test the localization efficacy of the proposed algorithms for noisy radio frequency (RF) channels and a dynamic target motion model. Benefiting from the good generalization ability of SVR, simulation results showed that the presented SVR-based localization algorithms demonstrate superior performance compared to trilateration- and GRNN-based localization schemes in terms of indoor localization performance.

【 授权许可】

Unknown