scholarly journals A Novel Robust Interval Kalman Filter Algorithm for GPS/INS Integrated Navigation

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Chen Jiang ◽  
Shu-bi Zhang ◽  
Qiu-zhao Zhang
Keyword(s):  
Kalman Filter ◽  
Robust Estimation ◽  
Global Positioning System ◽  
Estimation Methods ◽  
Integrated Navigation ◽  
Global Positioning ◽  
Robust Kalman Filter ◽  
Noise Data ◽  
Kalman Filter Algorithm ◽  
Observation Noise

Kalman filter is widely applied in data fusion of dynamic systems under the assumption that the system and measurement noises are Gaussian distributed. In literature, the interval Kalman filter was proposed aiming at controlling the influences of the system model uncertainties. The robust Kalman filter has also been proposed to control the effects of outliers. In this paper, a new interval Kalman filter algorithm is proposed by integrating the robust estimation and the interval Kalman filter in which the system noise and the observation noise terms are considered simultaneously. The noise data reduction and the robust estimation methods are both introduced into the proposed interval Kalman filter algorithm. The new algorithm is equal to the standard Kalman filter in terms of computation, but superior for managing with outliers. The advantage of the proposed algorithm is demonstrated experimentally using the integrated navigation of Global Positioning System (GPS) and the Inertial Navigation System (INS).

An implementation of the cubature Kalman filter for estimating trajectory parameters and air data of a hypersonic vehicle

2019 ◽  
Vol 233 (12) ◽  
pp. 4554-4570
Author(s):  
Zhijian Ding ◽  
Huan Zhou ◽  
Feng Wang ◽  
Dongsheng Wu ◽  
Yingchuan Wu ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Global Positioning System ◽  
Inertial Navigation System ◽  
Hypersonic Vehicle ◽  
Positioning System ◽  
System State ◽  
Sensing System ◽  
Cubature Kalman Filter ◽  
Global Positioning ◽  
Kalman Filter Algorithm

Trajectory parameters (including the position, velocity, and attitude angles of a vehicle) and air data (consisting of the flow angles, the Mach number, and the freestream static pressure) are vital data for the analysis and evaluation process in the hypersonic flight tests. This paper describes a data fusion estimation algorithm for a flush air data sensing system/inertial navigation system/global positioning system integrated system, which is used to estimate the trajectory parameters and air data for an unpowered hypersonic vehicle. In the approach, the raw outputs of flush air data sensing system (i.e. the surface pressure measurements) are integrated with global positioning system results (the vehicle’s position and velocity) and inertial navigation system measurements (including the acceleration and the angular velocity measurements) by using a nonlinear Kalman filter algorithm. Firstly, the system state vector is defined with the trajectory parameters, the biases of the inertial sensors and the winds. Then, the system dynamic models are built based on the motion equations of an unpowered hypersonic vehicle, the inertial sensor error models and the wind model. Besides, the system measurement vector is designed with the global positioning system results and the flush air data sensing system raw outputs. Based on these works, the system state is directly estimated by using the cubature Kalman filter algorithm. After that, the air data is calculated based on the estimated values and a high-fidelity model of atmosphere. Simulation cases are implemented to assess the performance of the proposed algorithm. The results show that the proposed method could estimate the trajectory parameters and air data for hypersonic vehicle with a high precision.

Adaptive Kalman Filter for INS/GPS Integrated Navigation System

2013 ◽  
Vol 336-338 ◽  
pp. 332-335 ◽  
Author(s):  
Tian Lai Xu
Keyword(s):  
Kalman Filter ◽  
Global Positioning System ◽  
Navigation System ◽  
A Priori ◽  
Noise Characteristic ◽  
Integrated Navigation ◽  
Adaptive Kalman Filter ◽  
Integrated Navigation System ◽  
Global Positioning ◽  
Priori Information

Inertial Navigation System (INS) and Global Positioning System (GPS) are commonly integrated to overcomes each systems inadequacies and provide an accurate navigation solution. The integration of INS and GPS is usually achieved using a Kalman filter. The accuracy of INS/GPS deteriorates in condition that a priori information used in Kalman filter does not accord with the actual environmental conditions. To address this problem, an improved Sage-Husa filter is presented. In this method, the measurement noise characteristic is adjusted if and only if filtering abnormality exists, avoiding filter instability and reducing computational burden caused by adjusting noise characteristic too frequently in Sage-Husa filter. Simulations in INS/GPS integrated navigation showed improvement in positioning accuracy.

scholarly journals A New Robust Kalman Filter for SINS/DVL Integrated Navigation System

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 51386-51395 ◽  
Author(s):  
Li Luo ◽  
Yonggang Zhang ◽  
Tao Fang ◽  
Ning Li
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Robust Kalman Filter

Seamless INS/GPS Integration Based on Support Vector Machines

2013 ◽  
Vol 336-338 ◽  
pp. 277-280 ◽  
Author(s):  
Tian Lai Xu
Keyword(s):  
Support Vector Machines ◽  
Global Positioning System ◽  
Navigation System ◽  
Superior Performance ◽  
Support Vector ◽  
Integrated Navigation ◽  
Positioning Accuracy ◽  
Integrated Navigation System ◽  
Vector Machines ◽  
Global Positioning

The combination of Inertial Navigation System (INS) and Global Positioning System (GPS) provides superior performance in comparison with either a stand-alone INS or GPS. However, the positioning accuracy of INS/GPS deteriorates with time in the absence of GPS signals. A least squares support vector machines (LS-SVM) regression algorithm is applied to INS/GPS integrated navigation system to bridge the GPS outages to achieve seamless navigation. In this method, LS-SVM is trained to model the errors of INS when GPS is available. Once the LS-SVM is properly trained in the training phase, its prediction can be used to correct the INS errors during GPS outages. Simulations in INS/GPS integrated navigation showed improvements in positioning accuracy when GPS outages occur.

On Navigation Systems for Motorcycles: The Influence and Estimation of Roll Angle

2005 ◽  
Vol 58 (3) ◽  
pp. 375-388 ◽  
Author(s):  
Joshua P. Coaplen ◽  
Patrick Kessler ◽  
Oliver M. O'Reilly ◽  
Dan M. Stevens ◽  
J. Karl Hedrick
Keyword(s):  
Global Positioning System ◽  
Angular Rate ◽  
Roll Angle ◽  
Estimation Methods ◽  
Navigation Systems ◽  
Simulation Environment ◽  
Global Positioning ◽  
Heading Angle ◽  
Navigation Information ◽  
Navigation Scheme

Vehicle navigation systems use various sensors and the global positioning system (GPS) to locate a vehicle. This location is then matched to a map database to provide navigation information. Between GPS updates, the vehicle's heading angle and forward speed are used to “dead reckon” its position. Heading angle is often measured by integrating the output of a rate gyroscope. For this measurement to be equal to the vehicle's heading angle, the vehicle should not experience any rotation about its roll or pitch axes. For an automobile, the roll and pitch angles are small and may be neglected for the purposes of navigation. This article demonstrates that this same assumption is not true for a motorcycle. Through simulation, it is shown that for a motorcycle, obtaining a meaningful heading angle from a single angular rate measurement requires accounting for the motorcycle's roll angle. Methods to estimate roll angle and heading angle from available navigation measurements are presented, and two possible sensor configurations are compared. A motorcycle navigation scheme based on these roll angle estimation methods is shown to produce exceptional results in a simulation environment.

Integrated Navigation of Mobile Robot Based on Neural Network and Fault Detection

2012 ◽  
Vol 433-440 ◽  
pp. 3175-3180
Author(s):  
Hong Mei Wang ◽  
Ming Lu Zhang ◽  
Guang Zhu Meng
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Global Positioning System ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Integrated Navigation ◽  
Global Positioning ◽  
Navigation Accuracy

When global positioning system (GPS) signal outages, the integrated navigation accuracy of GPS and strap-down inertial navigation system (SINS) will decline with time, and even navigation system cannot work. To avoid this, a new design is introduced. When GPS works normally, square root filter estimates the errors of position, velocity and attitude and compensates the outputs of SINS. When GPS is out of order, back propagation neural network (BPNN) will take the place of GPS to calculate the error parameters, thus the accuracy of navigation will enhance. And in this paper, the unit of fault detection is added to detect whether GPS signal outages or not. The simulation results show the effectiveness of this method

Optical Flow-Based Monocular Vision/INS Integrated Navigation for Mobile Robot Indoors

2014 ◽  
Vol 538 ◽  
pp. 375-378 ◽  
Author(s):  
Xi Yuan Chen ◽  
Jing Peng Gao ◽  
Yuan Xu ◽  
Qing Hua Li
Keyword(s):  
Mobile Robot ◽  
Optical Flow ◽  
Global Positioning System ◽  
Autonomous Navigation ◽  
Image Sequences ◽  
Velocity Estimation ◽  
Monocular Vision ◽  
Integrated Navigation ◽  
Global Positioning ◽  
Flow Algorithm

This paper proposed a new algorithm for optical flow-based monocular vision (MV)/ inertial navigation system (INS) integrated navigation. In this mode, a downward-looking camera is used to get the image sequences, which is used to estimate the velocity of the mobile robot by using optical flow algorithm. INS is employed for the yaw variation. In order to evaluate the performance of the proposed method, a real indoor test has done. The result shows that the proposed method has good performance for velocity estimation. It can be applied to the autonomous navigation of mobile robots when the Global Positioning System (GPS) and code wheel is unavailable.

Sign in / Sign up

Export Citation Format

Share Document