Robust inertial navigation system/ultra wide band integrated indoor quadrotor localization employing adaptive interacting multiple model-unbiased finite impulse response/Kalman filter estimator

2020 ◽  
Vol 98 ◽  
pp. 105683 ◽  
Author(s):  
Yuan Xu ◽  
Yuriy S. Shmaliy ◽  
Xiyuan Chen ◽  
Yueyang Li ◽  
Wanfeng Ma
Keyword(s):  
Kalman Filter ◽  
Impulse Response ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Finite Impulse Response ◽  
Inertial Navigation ◽  
Wide Band ◽  
Ultra Wide Band ◽  
Interacting Multiple Model ◽  
Multiple Model

Real-time accurate pedestrian tracking using extended finite impulse response filter bank for tightly coupling recent inertial navigation system and ultra-wideband measurements

2018 ◽  
Vol 232 (4) ◽  
pp. 464-472 ◽  
Author(s):  
Yuan Xu ◽  
Hamid Reza Karimi ◽  
Yueyang Li ◽  
Fengyu Zhou ◽  
Lili Bu
Keyword(s):  
Impulse Response ◽  
Navigation System ◽  
Filter Bank ◽  
Inertial Navigation System ◽  
Finite Impulse Response ◽  
Inertial Navigation ◽  
Finite Impulse Response Filter ◽  
Ultra Wideband ◽  
Pedestrian Tracking ◽  
Impulse Response Filter

To satisfy the increasing demands of the accuracy for the human localization, in this work, we propose a pedestrian tracking method by tightly coupling recent inertial navigation system–based and ultra-wideband–based measurements. In this mode, the difference between the distances derived from the inertial navigation system–based and ultra-wideband–based system is used as the observation of the data fusion filter. Moreover, in order to improve the performance of the extended finite impulse response filter, which depends on the averaging horizon ([Formula: see text]) when the error state vector ([Formula: see text]) is determined due to the model, the extended finite impulse response filter bank is employed to be the fusion center for pedestrian tracking, which used the Mahalanobis distance to find the optimal [Formula: see text] at each time index [Formula: see text]. Test experiments illustrate that the extended finite impulse response filter bank–based tightly coupled inertial navigation system/ultra-wideband–integrated method is able to achieve real-time estimation, and its accuracy is similar to the extended finite impulse response with the ideal [Formula: see text] which is calculated off-line.

An Improved and Efficient Algorithm for SINS/GPS/Doppler Integrated Navigation Systems

2012 ◽  
Vol 245 ◽  
pp. 323-329 ◽  
Author(s):  
Muhammad Ushaq ◽  
Jian Cheng Fang
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Sensor ◽  
Inertial Navigation ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Efficient Manner ◽  
Position Errors

Inertial navigation systems exhibit position errors that tend to grow with time in an unbounded mode. This degradation is due, in part, to errors in the initialization of the inertial measurement unit and inertial sensor imperfections such as accelerometer biases and gyroscope drifts. Mitigation to this growth and bounding the errors is to update the inertial navigation system periodically with external position (and/or velocity, attitude) fixes. The synergistic effect is obtained through external measurements updating the inertial navigation system using Kalman filter algorithm. It is a natural requirement that the inertial data and data from the external aids be combined in an optimal and efficient manner. In this paper an efficient method for integration of Strapdown Inertia Navigation System (SINS), Global Positioning System (GPS) and Doppler radar is presented using a centralized linear Kalman filter by treating vector measurements with uncorrelated errors as scalars. Two main advantages have been obtained with this improved scheme. First is the reduced computation time as the number of arithmetic computation required for processing a vector as successive scalar measurements is significantly less than the corresponding number of operations for vector measurement processing. Second advantage is the improved numerical accuracy as avoiding matrix inversion in the implementation of covariance equations improves the robustness of the covariance computations against round off errors.

scholarly journals Application of Federal Kalman Filter with Neural Networks in the Velocity and Attitude Matching of Transfer Alignment

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Lijun Song ◽  
Zhongxing Duan ◽  
Bo He ◽  
Zhe Li
Keyword(s):  
Neural Networks ◽  
Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Estimation Error ◽  
Inertial Navigation ◽  
Transfer Alignment ◽  
The Neural Networks ◽  
Initial Attitude ◽  
Noise Statistics

The centralized Kalman filter is always applied in the velocity and attitude matching of Transfer Alignment (TA). But the centralized Kalman has many disadvantages, such as large amount of calculation, poor real-time performance, and low reliability. In the paper, the federal Kalman filter (FKF) based on neural networks is used in the velocity and attitude matching of TA, the Kalman filter is adjusted by the neural networks in the two subfilters, the federal filter is used to fuse the information of the two subfilters, and the global suboptimal state estimation is obtained. The result of simulation shows that the federal Kalman filter based on neural networks is better in estimating the initial attitude misalignment angle of inertial navigation system (INS) when the system dynamic model and noise statistics characteristics of inertial navigation system are unclear, and the estimation error is smaller and the accuracy is higher.

Incremental predictive Kalman filter for alignment of inertial navigation system

2018 ◽  
pp. 095441001879432
Author(s):  
Mahdi Fathi ◽  
Nematollah Ghahramani ◽  
Mohammad Ali Shahi Ashtiani ◽  
Ali Mohammadi ◽  
Mohsen Fallah
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation

Error Modelling and Optimal Estimation of Laser Scanning Aided Inertial Navigation System in GNSS-Denied Environments

2018 ◽  
Vol 72 (3) ◽  
pp. 741-758 ◽  
Author(s):  
W.I. Liu ◽  
Zhixiong Li ◽  
Zhichao Zhang
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Laser Scanning ◽  
Inertial Navigation System ◽  
Laser Scanner ◽  
Inertial Navigation ◽  
Integration Algorithm ◽  
Biased Estimation ◽  
State Estimations ◽  
Estimation And Filtering

A Laser Scanning aided Inertial Navigation System (LSINS) is able to provide highly accurate position and attitude information by aggregating laser scanning and inertial measurements under the assumption that the rigid transformation between sensors is known. However, a LSINS is inevitably subject to biased estimation and filtering divergence errors due to inconsistent state estimations between the inertial measurement unit and the laser scanner. To bridge this gap, this paper presents a novel integration algorithm for LSINS to reduce the inconsistences between different sensors. In this new integration algorithm, the Radial Basis Function Neural Networks (RBFNN) and Singular Value Decomposition Unscented Kalman Filter (SVDUKF) are used together to avoid inconsistent state estimations. Optimal error estimation in the LSINS integration process is achieved to reduce the biased estimation and filtering divergence errors through the error state and measurement error model built by the proposed method. Experimental tests were conducted to evaluate the navigation performance of the proposed method in Global Navigation Satellite System (GNSS)-denied environments. The navigation results demonstrate that the relationship between the laser scanner coordinates and the inertial sensor coordinates can be established to reduce sensor measurement inconsistencies, and LSINS position accuracy can be improved by 23·6% using the proposed integration method compared with the popular Extended Kalman Filter (EKF) algorithm.

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