High-precision Mars Entry Integrated Navigation Under Large Uncertainties

2013 ◽  
Vol 67 (2) ◽  
pp. 327-342 ◽  
Author(s):  
Shuang Li ◽  
Xiuqiang Jiang ◽  
Yufei Liu
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
High Precision ◽  
Integrated Navigation ◽  
State Variables ◽  
Parameter Uncertainties ◽  
Initial State ◽  
Navigation Algorithm ◽  
Radio Measurements ◽  
Six Degree Of Freedom

In this paper, we present a high-precision Mars entry integrated navigation algorithm under large uncertainties via a desensitised extended Kalman filter (DEKF). Firstly, a new six degree-of-freedom Mars entry dynamics model is derived based on the angular velocity outputs of a gyro, which is free of modelling errors in the aerodynamic and control torques. Secondly, both the accelerometer outputs and radio measurements between orbiters and entry vehicle are used as the observations embedded in a navigation filter to perform state estimation and suppress the measurement noise. Finally, a desensitised extended Kalman filter, exhibiting the desirable property of efficiently reducing the sensitivity of state variables with respect to model and parameter uncertainties, is adopted in order to overcome the adverse effects of initial state errors and uncertainties during Mars atmospheric entry and further improve entry navigation accuracy. The numerical simulation results show that the DEKF-based integrated navigation algorithm developed in this paper can achieve a better navigation performance with higher accuracy when compared with the standard extended Kalman filter (EKF)-based integrated navigation algorithm in the presence of larger state errors and parameter uncertainties.

A new visual/inertial integrated navigation algorithm based on sliding-window factor graph optimisation

2020 ◽  
pp. 1-17
Author(s):  
Haiying Liu ◽  
Jingqi Wang ◽  
Jianxin Feng ◽  
Xinyao Wang
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Prior Information ◽  
Sliding Window ◽  
Factor Graph ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Sliding Windows ◽  
Navigation Algorithm ◽  
Almost All

Abstract Visual–Inertial Navigation Systems (VINS) plays an important role in many navigation applications. In order to improve the performance of VINS, a new visual/inertial integrated navigation method, named Sliding-Window Factor Graph optimised algorithm with Dynamic prior information (DSWFG), is proposed. To bound computational complexity, the algorithm limits the scale of data operations through sliding windows, and constructs the states to be optimised in the window with factor graph; at the same time, the prior information for sliding windows is set dynamically to maintain interframe constraints and ensure the accuracy of the state estimation after optimisation. First, the dynamic model of vehicle and the observation equation of VINS are introduced. Next, as a contrast, an Invariant Extended Kalman Filter (InEKF) is constructed. Then, the DSWFG algorithm is described in detail. Finally, based on the test data, the comparison experiments of Extended Kalman Filter (EKF), InEKF and DSWFG algorithms in different motion scenes are presented. The results show that the new method can achieve superior accuracy and stability in almost all motion scenes.

Gas pipeline leakage detection in the presence of parameter uncertainty using robust extended Kalman filter

2021 ◽  
pp. 014233122198911
Author(s):  
Mohadese Jahanian ◽  
Amin Ramezani ◽  
Ali Moarefianpour ◽  
Mahdi Aliari Shouredeli
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Parameter Uncertainty ◽  
Oil And Gas ◽  
Estimation Error ◽  
Real Data ◽  
Gas Pipeline ◽  
Pipeline System ◽  
Parameter Uncertainties ◽  
Simulation Results

One of the most significant systems that can be expressed by partial differential equations (PDEs) is the transmission pipeline system. To avoid the accidents that originated from oil and gas pipeline leakage, the exact location and quantity of leakage are required to be recognized. The designed goal is a leakage diagnosis based on the system model and the use of real data provided by transmission line systems. Nonlinear equations of the system have been extracted employing continuity and momentum equations. In this paper, the extended Kalman filter (EKF) is used to detect and locate the leakage and to attenuate the negative effects of measurement and process noises. Besides, a robust extended Kalman filter (REKF) is applied to compensate for the effect of parameter uncertainty. The quantity and the location of the occurred leakage are estimated along the pipeline. Simulation results show that REKF has better estimations of the leak and its location as compared with that of EKF. This filter is robust against process noise, measurement noise, parameter uncertainties, and guarantees a higher limit for the covariance of state estimation error as well. It is remarkable that simulation results are evaluated by OLGA software.

A Ship Flotation Control System Based on Pump-Driven Tank

2011 ◽  
Author(s):  
Juan Guo ◽  
Meng Tang ◽  
Zaojian Zou
Keyword(s):  
Kalman Filter ◽  
Control System ◽  
Structural Damage ◽  
Model Simulation ◽  
Theoretical Calculations ◽  
Wave Disturbance ◽  
Ship Motion ◽  
State Variables ◽  
Initial State ◽  
Asymmetric Loading

Extensive development in ship motion control strategies and systems in recent decades has called for higher requirements in control system accuracy and reliability. In this paper, a ship flotation control system based on pump-driven active tank is established. A special case is discussed, where the ship is heeling under an asymmetric loading either by structural damage or asymmetric consumption of ammunition. The purpose of the control system is to keep the ship in upright floating position in waves by transferring liquid between the tanks. Kalman filter is designed to eliminate the wave disturbance, in order to identify the heeling angle caused by asymmetric loading change. The effect of wave disturbance at different wave encounter angles, wave heights, as well as ship speeds is analyzed. Tuning of filter parameters such as initial state variables, initial error covariance and noise covariance is performed to achieve better filtering performance for different parameters of waves and ship motion. To verify the control model, simulation is conducted for a 3340t ship and the simulation results are compared with the theoretical calculations. The research results show the applicability and efficiency of Kalman filter. The concept of the control system presented in the paper is helpful to improve ship stability and safety when ship upright floating condition is disturbed.

Parameter Estimation of Nonlinear Biomedical Systems Using Extended Kalman Filter Algorithm

2017 ◽  
pp. 76-99 ◽  
Author(s):  
Kamalanand Krishnamurthy
Keyword(s):  
Parameter Estimation ◽  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Computational Models ◽  
Three Dimensional ◽  
Model Parameters ◽  
State Variables ◽  
Three Dimensional Model ◽  
Biomedical Systems ◽  
Estimation Of Model Parameters

Parameter estimation is a central issue in mathematical modelling of biomedical systems and for the development of patient specific models. The technique of estimating parameters helps in obtaining diagnostic information from computational models of biological systems. However, in most of the biomedical systems, the estimation of model parameters is a challenging task due to the nonlinearity of mathematical models. In this chapter, the method of estimation of nonlinear model parameters from measurements of state variables, using the extended Kalman filter, is extensively explained using an example of the three-dimensional model of the HIV/AIDS system.

scholarly journals Design and Simulation of the Integrated Navigation System based on Extended Kalman Filter

Open Physics ◽  
2017 ◽  
Vol 15 (1) ◽  
pp. 182-187 ◽  
Author(s):  
Weidong Zhou ◽  
Jiaxin Hou ◽  
Lu Liu ◽  
Tian Sun ◽  
Jing Liu
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Navigation System ◽  
Inertial Sensors ◽  
Integrated Navigation ◽  
Gps Receiver ◽  
Noisy Environment ◽  
Integrated Navigation System ◽  
Error Covariance ◽  
Time Update

AbstractThe integrated navigation system is used to estimate the position, velocity, and attitude of a vehicle with the output of inertial sensors. This paper concentrates on the problem of the INS/GPS integrated navigation system design and simulation. The structure of the INS/GPS integrated navigation system is made up of four parts: 1) GPS receiver, 2) Inertial Navigation System, 3) Extended Kalman filter, and 4) Integrated navigation scheme. Afterwards, we illustrate how to simulate the integrated navigation system with the extended Kalman filter by measuring position, velocity and attitude. Particularly, the extended Kalman filter can estimate states of the nonlinear system in the noisy environment. In extended Kalman filter, the estimation of the state vector and the error covariance matrix are computed by steps: 1) time update and 2) measurement update. Finally, the simulation process is implemented by Matlab, and simulation results prove that the error rate of statement measuring is lower when applying the extended Kalman filter in the INS/GPS integrated navigation system.

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