Title :
The Optimization of RSSI-Neural Network Positioning Algorithm
Author :
Zhang Xuhui ; Gao Baojiang ; Liu Yukun ; Wang Juan ; Chang Huimin
Author_Institution :
Higher Educ. Key Lab. for Meas. & Control Technol. & Instrumentations of Heilongjiang, Harbin Univ. of Sci. & Technol., Harbin, China
Abstract :
This paper optimized the RSSI neural network positioning algorithm. In-depth analysis of the principles of the Kalman filter and using the Kalman filter to filter the Received Signal Strength Indicator (RSSI) value. Analysis the structure of back propagation neural network and optimized the structure of the RSSI-neural network algorithm. MATLAB simulation verified the optimization algorithm has higher accuracy and more robustness.
Keywords :
Kalman filters; RSSI; backpropagation; mathematics computing; neural nets; optimisation; Kalman filter; MATLAB simulation; RSSI value; RSSI-neural network positioning algorithm optimization algorithm; backpropagation neural network; received signal strength indicator value; Algorithm design and analysis; Biological neural networks; Kalman filters; Mathematical model; Neurons; Signal processing algorithms; BP BP neural network; Kalman filter; RSSI; positioning algorithm;
Conference_Titel :
Instrumentation and Measurement, Computer, Communication and Control (IMCCC), 2014 Fourth International Conference on
Conference_Location :
Harbin
Print_ISBN :
978-1-4799-6574-8
DOI :
10.1109/IMCCC.2014.135
