Improving Navigational Accuracy for AUVs Based on an Adaptive Modified Square-Root Cubature Kalman Filter

2014 ◽  
Vol 945-949 ◽  
pp. 1430-1434
Author(s):  
Chen Sun ◽  
Jian Long Li
Keyword(s):  
Kalman Filter ◽  
Autonomous Underwater Vehicles ◽  
Linear Measurement ◽  
Square Root ◽  
Noise Variance ◽  
Process Noise ◽  
Cubature Kalman Filter ◽  
Measurement Function ◽  
Computational Errors ◽  
Noise Statistics

This paper addresses an adaptive modified square-root cubature Kalman filter for the navigation of autonomous underwater vehicles (AUVs). The standard square-root cubature Kalman filter (SCKF) implements the CKF using square-root filtering to reduce computational errors. It can be modified due to the nonlinear system with a linear measurement function. The modification leads to a decrease computational complexity. Sage-Husa noise statistics estimator is combined with the Modified SCKF to estimate the unknown and changing system process noise variance. The experimental results show that compared with the MSCKF and the EKF algorithm, the adaptive MSCKF show the best accuracy for a real system with unknown process noise variance.

Robust Square-Root Cubature FastSLAM with Genetic Operators

Robotica ◽  
2020 ◽  
pp. 1-21
Author(s):  
Ramazan Havangi
Keyword(s):  
Experimental Data ◽  
Kalman Filter ◽  
A Priori ◽  
A Priori Knowledge ◽  
Square Root ◽  
Genetic Operators ◽  
Proposal Distribution ◽  
Cubature Kalman Filter ◽  
Noise Statistics ◽  
Better Than

SUMMARY An improved FastSLAM based on the robust square-root cubature Kalman filter (RSRCKF) with partial genetic resampling is proposed in this paper. In the proposed method, RSRCKF is used to design the proposal distribution of FastSLAM and to estimate environment landmarks. The proposed method does not require a priori knowledge of the noise statistics. In addition, to increase diversity, it uses the genetic operators-based strategy to further improve the particle diversity. In fact, a partial genetic resampling operation is carried out to maintain the diversity of particles. The proposed method is compared with other methods via simulation and experimental data. It can be seen from the results that the proposed method provides significantly more accurate and robust estimation results compared with other methods even with fewer particles and unknown a priori. In addition, the consistency of the proposed method is better than that of other methods.

Square-Root Cubature Kalman Filter and Its Application to SLAM of an Mobile Robot

ROBOT ◽  
2013 ◽  
Vol 35 (2) ◽  
pp. 186 ◽  
Author(s):  
Yifei KANG ◽  
Yongduan SONG ◽  
Yu SONG ◽  
Deli YAN ◽  
Danyong LI
Keyword(s):  
Kalman Filter ◽  
Mobile Robot ◽  
Square Root ◽  
Cubature Kalman Filter

scholarly journals Adaptively Robust Square-root Cubature Kalman Filter Based on Amending

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Chunhui Li ◽  
Jian Ma ◽  
Yongjian Yang ◽  
Bingsong Xiao
Keyword(s):  
Kalman Filter ◽  
Square Root ◽  
Cubature Kalman Filter

scholarly journals Research on Landmarks of SLAM Based on Square Root Cubature Kalman Filter

2018 ◽  
Vol 1069 ◽  
pp. 012154
Author(s):  
Dandan Wang ◽  
Kaituo Tan ◽  
Zhengbin Li ◽  
Gannan Yuan
Keyword(s):  
Kalman Filter ◽  
Square Root ◽  
Cubature Kalman Filter
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