An improved adaptive Kalman filtering algorithm for advanced robot navigation system based on GPS/INS

Author(s):  
Xiaochuan Zhao ◽  
Yi Qian ◽  
Min Zhang ◽  
Jinzhe Niu ◽  
Yuxiang Kou
2015 ◽  
Vol 740 ◽  
pp. 596-599 ◽  
Author(s):  
Shi Qi An ◽  
Jun Kai Zhang

According to the principle and the method of initial alignment of strapdown inertial navigation system, proposed based on Sage-Husa adaptive kalman filter algorithm. The measured simulation data, compared with those of kalman filtering algorithm, show that the optimized algorithm can optimize the noise estimation, revise accumulated error of strapdown inertial navigation system, and greatly improve the navigation accuracy.


Author(s):  
Bingya Zhao ◽  
Ya Zhang

This paper studies the distributed secure estimation problem of sensor networks (SNs) in the presence of eavesdroppers. In an SN, sensors communicate with each other through digital communication channels, and the eavesdropper overhears the messages transmitted by the sensors over fading wiretap channels. The increasing transmission rate plays a positive role in the detectability of the network while playing a negative role in the secrecy. Two types of SNs under two cooperative filtering algorithms are considered. For networks with collectively observable nodes and the Kalman filtering algorithm, by studying the topological entropy of sensing measurements, a sufficient condition of distributed detectability and secrecy, under which there exists a code–decode strategy such that the sensors’ estimation errors are bounded while the eavesdropper’s error grows unbounded, is given. For collectively observable SNs under the consensus Kalman filtering algorithm, by studying the topological entropy of the sensors’ covariance matrices, a necessary condition of distributed detectability and secrecy is provided. A simulation example is given to illustrate the results.


2020 ◽  
Vol 53 (2) ◽  
pp. 3577-3582
Author(s):  
Hao Chen ◽  
Jianan Wang ◽  
Chunyan Wang ◽  
Dandan Wang ◽  
Jiayuan Shan ◽  
...  

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