Online cubature Kalman filter Rauch–Tung–Striebel smoothing for indoor inertial navigation system/ultrawideband integrated pedestrian navigation

2017 ◽  
Vol 232 (4) ◽  
pp. 390-398 ◽  
Author(s):  
Yuan Xu ◽  
Xiyuan Chen
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Position Error ◽  
Smoothing Algorithm ◽  
Position Information ◽  
The North ◽  
Proposed Model ◽  
Cubature Kalman Filter

Accurate position information of the pedestrians is required in many applications such as healthcare, entertainment industries, and military field. In this work, an online Cubature Kalman filter Rauch–Tung–Striebel smoothing algorithm for people’s location in indoor environment is proposed using inertial navigation system techniques with ultrawideband technology. In this algorithm, Cubature Kalman filter is employed to improve the filtering output accuracy; then, the Rauch–Tung–Striebel smoothing is used between the ultrawideband measurements updates; finally, the average value of the corrected inertial navigation system error estimation is output to compensate the inertial navigation system position error. Moreover, a real indoor test has been done for assessing the performance of the proposed model and algorithm. Test results show that the proposed model is able to reduce the sum of the absolute position error between the east direction and the north direction by about 32% compared with only the ultrawideband model, and the performance of the online Cubature Kalman filter Rauch–Tung–Striebel smoothing algorithm is slightly better than the off-line mode.

scholarly journals A Novel Information Fusion Method for Redundant Rotational Inertial Navigation Systems Based on Reduced-Order Kalman Filter

2018 ◽  
Vol 160 ◽  
pp. 07005
Author(s):  
Lin Wang ◽  
Wenqi Wu ◽  
Guo Wei ◽  
Jinlong Li ◽  
Ruihang Yu
Keyword(s):  
Kalman Filter ◽  
Information Fusion ◽  
Navigation System ◽  
Inertial Navigation System ◽  
High Reliability ◽  
Inertial Navigation ◽  
Position Error ◽  
Navigation Systems ◽  
Inertial Navigation Systems ◽  
Reduced Order

The redundant rotational inertial navigation systems can satisfy not only the high-accuracy but also the high-reliability demands of underwater vehicle on navigation system. However, different systems are usually independent, and lack of information fusion. A reduced-order Kalman filter is designed to fuse the navigation information output of redundant rotational navigation systems which usually include a dual-axis rotational inertial navigation system being master system and a single-axis rotational inertial navigation system being hot-backup system. The azimuth gyro drift of single-axis rotational inertial navigation system can be estimated by the designed filter, whereby the position error caused by that can be compensated with the aid of designed position error prediction model. As a result, the improved performance of single-axis rotational inertial navigation system can guarantee the position accuracy in the case of dual-axis system failure. Semi-physical simulation and experiment verify the effectiveness of the proposed method.

GPS/INS Integration During GPS Outages Using Machine Learning Augmented with Kalman Filter

2021 ◽  
Vol 16 ◽  
pp. 294-301
Author(s):  
Reshma Verma ◽  
Lakshmi Shrinivasan ◽  
K Shreedarshan
Keyword(s):  
Machine Learning ◽  
Kalman Filter ◽  
Global Positioning System ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Supervised Machine Learning ◽  
Positioning System ◽  
Position Information ◽  
Global Positioning

Nowadays a tremendous progress has been witnessed in Global Positioning System (GPS) and Inertial Navigation System (INS). The Global Positioning System provides information as long as there is an unobstructed line of sight and it suffers from multipath effect. To enhance the performance of an integrated Global Positioning System and Inertial Navigation System (GPS/INS) during GPS outages, a novel hybrid fusion algorithm is proposed to provide a pseudo position information to assist the integrated navigation system. A new model that directly relates the velocity, angular rate and specific force of INS to the increments of the GPS position is established. Combined with a Kalman filter the hybrid system is able to predict and estimate a pseud GPS position when GPS signal is unavailable. Field test data are collected to experimentally evaluate the proposed model. In this paper, the obtained GPS/INS datasets are pre-processed and semi-supervised machine learning technique has been used. These datasets are then passed into Kalman filtering for the estimation/prediction of GPS positions which were lost due to GPS outages. Hence, to bridge out the gaps of GPS outages Kalman Filter plays a major role in prediction. The comparative results of Kaman filter and extended Kalman filter are computed. The simulation results show that the GPS positions have been predicted taking into account some factors/measurements of a vehicle, the trajectory of the vehicle, the entire simulation was done using Anaconda (Jupyter Notebook).

Seamless GPS/Inertial Navigation System Based on Self-Learning Square-Root Cubature Kalman Filter

2021 ◽  
Vol 68 (1) ◽  
pp. 499-508 ◽  
Author(s):  
Chong Shen ◽  
Yu Zhang ◽  
Xiaoting Guo ◽  
Xiyuan Chen ◽  
Huiliang Cao ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Square Root ◽  
Cubature Kalman Filter ◽  
Self Learning

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.

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