Open AccessRSS Positioning Algorithm Based on Maximum Likelihood Recursive Estimation and CKF
Author(s) -
Ningchuan Xiao,
ShengCai Shi
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1617/1/012018
Subject(s) - rss , computer science , algorithm , position (finance) , matlab , kalman filter , indoor positioning system , triangulation , nonlinear system , signal strength , shadow (psychology) , signal (programming language) , non line of sight propagation , mathematics , wireless , artificial intelligence , telecommunications , psychology , physics , geometry , finance , accelerometer , quantum mechanics , economics , psychotherapist , programming language , operating system
In order to improve the positioning accuracy of indoor and dense obstacles, a positioning algorithm based on maximum likelihood recursive estimation and cubature Kalman filter is proposed for the positioning technology based on the received signal strength. The algorithm consists of two steps: initial position estimation and mobile location. Firstly, according to the principle of triangulation, the possible target region of mobile terminal is determined and the region is divided into smaller possible target region step by step. Then, based on the hybrid cooperation of the received signal strength and time of arrival, the nonlinear CKF filter is used to realize the mobile location. Finally, MATLAB is used to simulate the algorithm, and the simulation results show that the proposed method has better positioning performance even in the shadow area.