scholarly journals Data Fusion Based on Adaptive Interacting Multiple Model for GPS/INS Integrated Navigation System

2018 ◽  
Vol 8 (9) ◽  
pp. 1682 ◽  
Author(s):  
Chuang Zhang ◽  
Chen Guo ◽  
Daheng Zhang
Keyword(s):  
Navigation System ◽  
Integrated System ◽  
Filter Method ◽  
Integration Scheme ◽  
Interacting Multiple Model ◽  
Integrated Navigation ◽  
Multiple Model ◽  
Integrated Navigation System ◽  
Comparison Results ◽  
On Line

The extended Kalman filter (EKF) as a primary integration scheme has been applied in the Global Positioning System (GPS) and inertial navigation system (INS) integrated system. Nevertheless, the inherent drawbacks of EKF contain not only instability caused by linearization, but also massive calculation of Jacobian matrix. To cope with this problem, the adaptive interacting multiple model (AIMM) filter method is proposed to enhance navigation performance. The soft-switching characteristic, which is provided by interacting multiple model algorithm, permits process noise to be converted between upper and lower limits, and the measurement covariance is regulated by Sage adaptive filtering on-line Moreover, since the pseudo-range and Doppler observations need to be updated, an updating policy for classified measurement is considered. Finally, the performance of the GPS/INS integration method on the basis of AIMM is evaluated by a real ship, and comparison results demonstrate that AIMM could achieve a more position accuracy.

scholarly journals Multiple Model Adaptive Rank Estimation for Integrated Navigation During Mars Entry

2016 ◽  
Vol 70 (2) ◽  
pp. 291-308 ◽  
Author(s):  
Qiang Xiao ◽  
Huimin Fu ◽  
Zhihua Wang ◽  
Yongbo Zhang
Keyword(s):  
Navigation System ◽  
Uncertain System ◽  
Measurement Unit ◽  
Filter Method ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Multiple Model ◽  
Integrated Navigation System ◽  
Rank Estimation ◽  
Three Degree Of Freedom

Accurate navigation systems are required for future pinpoint Mars landing missions. A radio ranging augmented Inertial Measurement Unit (IMU) integrated navigation system concept is considered for the Mars entry navigation. The uncertain system parameters associated with the Three Degree-Of-Freedom (3-DOF) dynamic model, and the measurement systematic errors are considered. In order to improve entry navigation accuracy, this paper presents the Multiple Model Adaptive Rank Estimation (MMARE) filter of radio beacons/IMU integrated navigation system. 3-DOF simulation results show that the performances of the proposed navigation filter method, 70·39 m estimated altitude error and 15·74 m/s estimated velocity error, fulfill the need of future pinpoint Mars landing missions.

scholarly journals Robust Interacting Multiple Model Unscented Particle Filter for Navigation

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Li Xue ◽  
Yulan Han ◽  
Chunning Na
Keyword(s):  
Particle Filter ◽  
Navigation System ◽  
Measurement Information ◽  
Interacting Multiple Model ◽  
Integrated Navigation ◽  
Multiple Model ◽  
Integrated Navigation System ◽  
Filter Performance ◽  
Unscented Particle Filter ◽  
Particle Filter Algorithm

In order to solve the problems of particle degradation and difficulty in selecting importance density function in particle filter algorithm, a robust interacting multiple model unscented particle filter algorithm is presented, which is based on the advantages of interacting multiple model and particle filter algorithms. This algorithm can use the unscented transformation to get the particles that contain the latest measurement information of each model and calculate the robust equivalent weight function. This robust factor is designed to adjust the estimation and variance, and the important distribution function adaptively obtained is closer to the true distribution. Then, the particles weights can be flexibly adjusted in real time by using Euclidean distance to improve the computational efficiency during the resampling process. In addition, this filter process can comprehensively describe the uncertainty of the statistics characteristic of observation noise between different models. The diversity of available particles is increased, and the filter precision is improved. The proposed algorithm is applied to the SINS/GPS integrated navigation system, and the simulation analysis results demonstrate that the algorithm can effectively improve the filter performance and the calculation precision in positioning of integrated navigation system; thus, it provides a new method for nonlinear model filter.

scholarly journals The Design and Implementation of an Inertial GNSS Odometer Integrated Navigation System Based on a Federated Kalman Filter for High-Speed Railway Track Inspection

2021 ◽  
Vol 11 (11) ◽  
pp. 5244
Author(s):  
Xinchun Zhang ◽  
Ximin Cui ◽  
Bo Huang
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
High Speed ◽  
Measurement Accuracy ◽  
Railway Track ◽  
Filter Method ◽  
Integrated Navigation ◽  
High Speed Railway ◽  
Track Geometry ◽  
Integrated Navigation System

The detection of track geometry parameters is essential for the safety of high-speed railway operation. To improve the accuracy and efficiency of the state detector of track geometry parameters, in this study we propose an inertial GNSS odometer integrated navigation system based on the federated Kalman, and a corresponding inertial track measurement system was also developed. This paper systematically introduces the construction process for the Kalman filter and data smoothing algorithm based on forward filtering and reverse smoothing. The engineering results show that the measurement accuracy of the track geometry parameters was better than 0.2 mm, and the detection speed was about 3 km/h. Thus, compared with the traditional Kalman filter method, the proposed design improved the measurement accuracy and met the requirements for the detection of geometric parameters of high-speed railway tracks.

A Single Chip Integrated Navigation System

1993 ◽  
Vol 46 (1) ◽  
pp. 95-104 ◽  
Author(s):  
Eric Aardoom ◽  
André Nieuwland
Keyword(s):  
Navigation System ◽  
System Integration ◽  
Cost Effective ◽  
Integrated System ◽  
Integrated Navigation ◽  
Single Chip ◽  
Integrated Navigation System ◽  
System Integrity ◽  
Cost Effective Approach ◽  
Application Specific

Recently, integration of different radionavigation systems has become very popular, since it improves system integrity, availability, accuracy and reliability. This paper discusses a new, flexible and cost-effective approach to system integration, centred on a single-chip application specific processor (ASP). An overview of this integrated system is presented and the application of the ASP for the implementation of a six-channel GPS, OMEGA, Loran-C and MLS receiver is given. The ASP is currently being implemented on a 180000 transistor 1·6μ, m CMOS Sea of Gates chip, and is expected to run at 100 MHz clock speed.

scholarly journals Estimation of UAV Position with Use of Smoothing Algorithms

2017 ◽  
Vol 24 (1) ◽  
pp. 127-142 ◽  
Author(s):  
Piotr Kaniewski ◽  
Rafał Gil ◽  
Stanisław Konatowski
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Position Estimation ◽  
Fixed Interval ◽  
Smoothing Algorithm ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Gnss Receiver ◽  
On Line

Abstract The paper presents methods of on-line and off-line estimation of UAV position on the basis of measurements from its integrated navigation system. The navigation system installed on board UAV contains an INS and a GNSS receiver. The UAV position, as well as its velocity and orientation are estimated with the use of smoothing algorithms. For off-line estimation, a fixed-interval smoothing algorithm has been applied. On-line estimation has been accomplished with the use of a fixed-lag smoothing algorithm. The paper includes chosen results of simulations demonstrating improvements of accuracy of UAV position estimation with the use of smoothing algorithms in comparison with the use of a Kalman filter.

Application of Kalman Filter Technology to Integrated INS/DR Navigation System for Land Vehicle

2013 ◽  
Vol 411-414 ◽  
pp. 912-916 ◽  
Author(s):  
Ying Chen ◽  
Xia Jiang Zhang ◽  
Yuan Yuan Xue ◽  
Zhen Kang ◽  
Ting Shang
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Dead Reckoning ◽  
Position Error ◽  
Integrated System ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Attitude Error ◽  
Result Show ◽  
The Difference

Strap-down INS is composed of fiber gyroscope. Position error propagation equation and position update algorithm of dead reckoning is deduced in this paper. The Kalman filter is proposed for compensation error of integrated system. The difference of velocity between INS and DR is used as the input of Kalman filter, attitude error, velocity error, position error and scale factor error are to be estimated which compensate and rectify the errors of integrated navigation system. By carrying out experiment upon vehicular navigation system in use of Kalman filter, the errors of integrated navigation system are estimated accurately. Experiment result show that the method not only can effectively improve precision of the system, but also is simple and convenient, so it is more suitable for practical application.

MEMS IMU and GPS/Beidou Integration Navigation System Using Interval Kalman Filter

2014 ◽  
Vol 568-570 ◽  
pp. 970-975 ◽  
Author(s):  
Yang Le ◽  
Xiu Feng He ◽  
Ru Ya Xiao
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Low Cost ◽  
Integrated System ◽  
Integrated Navigation ◽  
Dynamics Modeling ◽  
Integrated Navigation System ◽  
Uncertain Dynamics ◽  
Integration Navigation ◽  
Mems Imu

This paper describes an integrated MEMS IMU and GNSS system for vehicles. The GNSS system is developed to accurately estimate the vehicle azimuth, and the integrated GNSS/IMU provides attitude, position and velocity. This research is aimed at producing a low-cost integrated navigation system for vehicles. Thus, an inexpensive solid-state MEMS IMU is used to smooth the noise and to provide a high bandwidth response. The integration system with uncertain dynamics modeling and uncertain measurement noise is also studied. An interval adaptive Kalman filter is developed for such an uncertain integrated system, since the standard extended Kalman filter (SKF) is no longer applicable, and a method of adaptive factor construction with uncertain parameter is proposed for the nonlinear integrated GNSS/IMU system. The results from the actual GNSS/IMU integrated system indicate that the filtering method proposed is effective.

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