Adaptive Kalman Filtering for Low-cost INS/GPS

2003 ◽  
Vol 56 (1) ◽  
pp. 143-152 ◽  
Author(s):  
Christopher Hide ◽  
Terry Moore ◽  
Martin Smith
Keyword(s):  
Kalman Filter ◽  
Kalman Filtering ◽  
Inertial Sensor ◽  
Low Cost ◽  
Simulation Software ◽  
Multiple Model ◽  
On Line ◽  
Stochastic Properties ◽  
Dynamic Alignment ◽  
Filtering Techniques

GPS and low-cost INS sensors are widely used for positioning and attitude determination applications. Low-cost inertial sensors exhibit large errors that can be compensated using position and velocity updates from GPS. Combining both sensors using a Kalman filter provides high-accuracy, real-time navigation. A conventional Kalman filter relies on the correct definition of the measurement and process noise matrices, which are generally defined a priori and remain fixed throughout the processing run. Adaptive Kalman filtering techniques use the residual sequences to adapt the stochastic properties of the filter on line to correspond to the temporal dependence of the errors involved. This paper examines the use of three adaptive filtering techniques. These are artificially scaling the predicted Kalman filter covariance, the Adaptive Kalman Filter and Multiple Model Adaptive Estimation. The algorithms are tested with the GPS and inertial data simulation software. A trajectory taken from a real marine trial is used to test the dynamic alignment of the inertial sensor errors. Results show that on line estimation of the stochastic properties of the inertial system can significantly improve the speed of the dynamic alignment and potentially improve the overall navigation accuracy and integrity.

Kalman filter-based yaw angle estimation by fusing inertial and magnetic sensing: a case study using low cost sensors

Sensor Review ◽  
2015 ◽  
Vol 35 (3) ◽  
pp. 244-250 ◽  
Author(s):  
Pedro Neto ◽  
Nuno Mendes ◽  
A. Paulo Moreira
Keyword(s):  
Kalman Filter ◽  
Inertial Sensor ◽  
Low Cost ◽  
Human Robot Interaction ◽  
Magnetic Sensor ◽  
Robot Interaction ◽  
Magnetic Sensing ◽  
Content Type ◽  
Reliable Estimation ◽  
Yaw Angle

Purpose – The purpose of this paper is to achieve reliable estimation of yaw angles by fusing data from low-cost inertial and magnetic sensing. Design/methodology/approach – In this paper, yaw angle is estimated by fusing inertial and magnetic sensing from a digital compass and a gyroscope, respectively. A Kalman filter estimates the error produced by the gyroscope. Findings – Drift effect produced by the gyroscope is significantly reduced and, at the same time, the system has the ability to react quickly to orientation changes. The system combines the best of each sensor, the stability of the magnetic sensor and the fast response of the inertial sensor. Research limitations/implications – The system does not present a stable behavior in the presence of large vibrations. Considerable calibration efforts are needed. Practical implications – Today, most of human–robot interaction technologies need to have the ability to estimate orientation, especially yaw angle, from small-sized and low-cost sensors. Originality/value – Existing methods for inertial and magnetic sensor fusion are combined to achieve reliable estimation of yaw angle. Experimental tests in a human–robot interaction scenario show the performance of the system.

Experimental Evaluation of the Preview Servo Scheme for a Two-Axis Positioning System

1984 ◽  
Vol 106 (1) ◽  
pp. 1-5 ◽  
Author(s):  
M. Tomizuka ◽  
D. Dornfeld ◽  
X.-Q. Bian ◽  
H.-G. Cai
Keyword(s):  
Kalman Filter ◽  
Kalman Filtering ◽  
Experimental Evaluation ◽  
Experimental Results ◽  
Positioning System ◽  
Velocity Control ◽  
Filtering Theory ◽  
On Line ◽  
Sharp Corners ◽  
Welding System

A preview servo scheme for position and velocity control is implemented on a two-axis welding table. The Kalman filtering theory is used to estimate the velocity from position measurements, and a cornering scheme is proposed to attain smaller path errors at sharp corners. The experimental results show that the preview-servo scheme with the Kalman filter and corner preview features is suitable for on-line control of the welding system.

scholarly journals Low-Cost Monitoring System of Sensors for Evaluating Dynamic Solicitations of Semitrailer Structure

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Pablo Luque ◽  
Daniel A. Mántaras ◽  
Aida Rodríguez ◽  
Hugo Malón ◽  
Luis Castejón ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Monitoring System ◽  
Extended Kalman Filter ◽  
Numerical Models ◽  
Low Cost ◽  
Three Dimensional ◽  
Cost Benefit ◽  
Simulation Software ◽  
Measurement Unit ◽  
Definition Of

Analysis of the fatigue life of a semitrailer structure necessitates identification of the loads and dynamic solicitations in the structure. These forces can be introduced in computer simulation software (multibody + finite element) for analysing the response of different design solutions to them. These numerical models must be validated and some parameters need to be measured directly in a field test with real vehicles under various driving conditions. In this study, a low-cost monitoring system is developed for application to a real fleet of semitrailers. According to the definition of the numerical model, the guidance of a virtual vehicle is defined by the three-dimensional kinematics of the kingpin. For characterisation of these movements, a monitoring system having a low-cost inertial measurement unit (IMU) and global positioning system (GPS) antennas is developed with different configurations to enable analysis of the best cost-benefit (result accuracy) solution, and an extended Kalman filter (EKF) that characterises the kinematic guidance of the kingpin is proposed. A semitrailer was equipped with the experimental low-cost monitoring system and high-precision sensors (IMU, GPS) in order to validate the results obtained by the experimental low-cost monitoring system and the inertial-extended Kalman filter developed. The validated system has applicability in the low-cost monitoring of a fleet of real vehicles.

State Feedback Observer for Yaw Rate Based on the Nonlinear Vehicle Model

2011 ◽  
Vol 52-54 ◽  
pp. 523-528
Author(s):  
Wei Da Wang ◽  
Li Juan Yuan ◽  
Wei Zhang
Keyword(s):  
Kalman Filter ◽  
State Feedback ◽  
Low Cost ◽  
Estimation Algorithm ◽  
The State ◽  
Vehicle Model ◽  
Yaw Rate ◽  
On Line ◽  
Nonlinear Vehicle Model ◽  
Rate Sensor

Researching the estimating algorithm for the vehicle yaw rate is providing complement to the sensor measure and reference of the sensor diagnosis. If the estimating precision is good enough, the yaw rate sensor will be canceled and the cost of VDC system will be cut down. Based on the eight-DOF vehicle model and a modified Dugoff tire model which is simple and accurate, the estimation algorithm of vehicle yaw rate is proposed in this paper. The state feedback observer for yaw rate is designed and compared to the observer based on Kalman filter. The simulation results indicate that this algorithm can calculate vehicle yaw rate in real-time and the estimating precision is better than the Kalman filter in nonlinear condition. Therefore, the state feedback observer in this paper is proposing a low-cost and more practical idea for estimating the vehicle yaw rate on-line.

scholarly journals PREDIKSI CURAH HUJAN BULANAN BERDASARKAN SUHU PERMUKAAN LAUT NINO 3.4 : SUATU PENDEKATAN DENGAN METODE FILTER KALMAN(MONTHLY RAINFALL PREDICTION BASED ON SEA SURFACE TEMPERATURE NINO 3.4 : THE APPROACH WITH KALMAN FILTERING)

Agromet ◽  
2005 ◽  
Vol 19 (2) ◽  
pp. 43
Author(s):  
Woro Estinigtyas ◽  
S. Suciantini ◽  
G. Irianto
Keyword(s):  
Kalman Filter ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Correlation Coefficient ◽  
Kalman Filtering ◽  
Sea Surface ◽  
Deterministic Models ◽  
Rainfall Prediction ◽  
Dependent Variables ◽  
On Line

Many approaches have been applied to forecast climate using statistical and deterministic models using independent and dependent variables empirically. It is more practical to analyze the parameters, but it needs validation anytime and anywhere. Kalman filtering unites physical and statistical model approaches to stochastic model renewable anytime for objective of on line forecasting. Based on research, sea surface temperature Nino 3.4 have high correlation with rainfall in Indonesia, so it is used to forecast rainfall in Cirebon as area study. Rainfall clustering in Cirebon results 6 groups with rainfall average 1400-1500 mm/year for dry area and 3000-3200 mm/year for wet area. Validation have correlation coefficient validation value more than 94%, correlation coefficient model value more than 78% and fit model value more than 38%. The result of regression gives R2 value of more than 0,8. It implies that predicting model using Kalman Filter is feasible to forecast montly rainfall based on sea surface temperature Nino 3.4. The result of rainfall prediction in Cirebon show increasing in rainfall until February 2005, with correlation coeficient value of model more than 90% and fit model more than 40%.

scholarly journals APPLICATION OF FRACTIONAL SENSOR FUSION ALGORITHMS FOR INERTIAL MEMS SENSING

2009 ◽  
Vol 14 (2) ◽  
pp. 199-209 ◽  
Author(s):  
Michailas Romanovas ◽  
Lasse Klingbeil ◽  
Martin Traechtler ◽  
Yiannos Manoli
Keyword(s):  
Kalman Filter ◽  
Kalman Filtering ◽  
Kalman Filters ◽  
Head Motion ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Data Set ◽  
Motion Data ◽  
Definition Of ◽  
The Given

The work presents an extension of the conventional Kalman filtering concept for systems of fractional order (FOS). Modifications are introduced using the Grünwald‐Letnikov (GL) definition of the fractional derivative (FD) and corresponding truncation of the history length. Two versions of the fractional Kalman filter (FKF) are shown, where the FD is calculated directly or by augmenting the state vector with the estimate of the FD. The filters are compared to conventional integer order (IO) Position (P‐KF) and Position‐Velocity (PV‐KF) Kalman filters as well as to an adaptive Interacting Multiple‐Model Kalman Filter (IMM‐KF). The performance of the filters is assessed based on a hand and a head motion data set. The feasibility of the given approach is shown.

Parallel model adaptive Kalman filtering for autonomous navigation with line-of-sight measurements

2018 ◽  
Vol 233 (11) ◽  
pp. 4017-4031
Author(s):  
Kai Xiong ◽  
Chunling Wei ◽  
Haoyu Zhang
Keyword(s):  
Kalman Filter ◽  
Kalman Filtering ◽  
Autonomous Navigation ◽  
Adaptive Estimation ◽  
Line Of Sight ◽  
Parallel Model ◽  
Multiple Model ◽  
Filtering Algorithm ◽  
Robust Kalman Filter ◽  
Multiple Model Adaptive Estimation

In this paper, a parallel model adaptive Kalman filtering algorithm is presented for multiple sensors estimation fusion when the measurement noise statistics are uncertain. As a typical adaptive filtering algorithm, the multiple model adaptive estimation tries to reduce the dependency of the filter on the noise parameters. It utilizes multiple models with different noise levels to estimate the state and combines the model-dependent estimates with model probability. However, with the increase in the number of active sensors, a large number of models are required to cover the entire range of the possible noise parameter values, which can become computationally infeasible. The main goal of this work is to incorporate the noise statistic estimator in the framework of the multiple model adaptive estimation, such that only two models are required for each sensor, which significantly reduce the complexity of the estimator. The advantage of the presented algorithm to deal with the model uncertainty is studied analytically. The high performance of the parallel model adaptive Kalman filtering for autonomous satellite navigation using inter-satellite line-of-sight measurements is illustrated in comparison with a robust Kalman filter, an intrinsically Bayesian robust Kalman filter, and the traditional multiple model adaptive estimation.

scholarly journals Computing Engine Efficiencies by Neural Nets

1995 ◽  
Author(s):  
T. W. Long
Keyword(s):  
Kalman Filter ◽  
Kalman Filtering ◽  
Estimation Method ◽  
Engine Performance ◽  
Neural Nets ◽  
Neural Network Approach ◽  
Current Estimation ◽  
Filter Output ◽  
On Line ◽  
Good Agreement

The Performance Seeking Control (PSC) system for the Pratt & Whitney 1128 Engine requires estimates of engine performance; however, the current estimation method of Kalman Filtering imposes a heavy computation overhead. Thus fast on-line applications may be limited. A neural network approach to estimating engine performance is investigated as an alternative method. This neuro-estimator emulates the Kalman Filter based on off-line training. Good agreement between Kalman Filter output and the neuro-estimator output was achieved for steady state conditions.

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