scholarly journals Positioning accuracy improvement in high‐speed GPS receivers using sequential extended Kalman filter

2021 ◽  
Author(s):  
Narges Rahemi ◽  
Mohammad Reza Mosavi
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
High Speed ◽  
Accuracy Improvement ◽  
Positioning Accuracy ◽  
Gps Receivers

scholarly journals A High-Precision, Real-Time, and Robust Indoor Visible Light Positioning Method Based on Mean Shift Algorithm and Unscented Kalman Filter

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1094 ◽  
Author(s):  
Zekun Xie ◽  
Weipeng Guan ◽  
Jieheng Zheng ◽  
Xinjie Zhang ◽  
Shihuan Chen ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Visible Light ◽  
Real Time ◽  
High Speed ◽  
Unscented Kalman Filter ◽  
Light Emitting Diode ◽  
Mean Shift ◽  
Indoor Navigation ◽  
Positioning Accuracy ◽  
Mean Shift Algorithm

Visible light positioning (VLP) is a promising technology for indoor navigation. However, most studies of VLP systems nowadays only focus on positioning accuracy, whereas robustness and real-time ability are often overlooked, which are all indispensable in actual VLP situations. Thus, we propose a novel VLP method based on mean shift (MS) algorithm and unscented Kalman filter (UKF) using image sensors as the positioning terminal and a Light Emitting Diode (LED) as the transmitting terminal. The main part of our VLP method is the MS algorithm, realizing high positioning accuracy with good robustness. Besides, UKF equips the mean shift algorithm with the capacity to track high-speed targets and improves the positioning accuracy when the LED is shielded. Moreover, a LED-ID (the identification of the LED) recognition algorithm proposed in our previous work was utilized to locate the LED in the initial frame, which also initialized MS and UKF. Furthermore, experiments showed that the positioning accuracy of our VLP algorithm was 0.42 cm, and the average processing time per frame was 24.93 ms. Also, even when half of the LED was shielded, the accuracy was maintained at 1.41 cm. All these data demonstrate that our proposed algorithm has excellent accuracy, strong robustness, and good real-time ability.

scholarly journals In-flight model parameter and state estimation using gradient descent for high-speed flight

2019 ◽  
Vol 11 ◽  
pp. 175682931983368
Author(s):  
S Li ◽  
C De Wagter ◽  
CC de Visser ◽  
QP Chu ◽  
GCHE de Croon
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Gradient Descent ◽  
High Speed ◽  
Time Window ◽  
Kinematic Model ◽  
Micro Air Vehicles ◽  
Gradient Descent Method ◽  
End Point ◽  
Air Vehicles

High-speed flight in GPS-denied environments is currently an important frontier in the research on autonomous flight of micro air vehicles. Autonomous drone races stimulate the advances in this area by representing a very challenging case with tight turns, texture-less floors, and dynamic spectators around the track. These properties hamper the use of standard visual odometry approaches and imply that the micro air vehicles will have to bridge considerable time intervals without position feedback. To this end, we propose an approach to trajectory estimation for drone racing that is computationally efficient and yet able to accurately estimate a micro air vehicle’s state (including biases) and parameters based on sparse, noisy observations of racing gates. The key concept of the approach is to optimize unknown and difficult-to-observe state variables so that the observations of the racing gates best fit with the known control inputs, estimated attitudes, and the quadrotor dynamics and aerodynamics during a time window. It is shown that a gradient-descent implementation of the proposed approach converges ∼4 times quicker to (approximately) correct bias values than a state-of-the-art 15-state extended Kalman filter. Moreover, it reaches a higher accuracy, as the predicted end-point of an open-loop turn is on average only ∼20 cm away from the actual end-point, while the extended Kalman filter and the gradient descent method with kinematic model only reach an accuracy of ∼50 cm. Although the approach is applied here to drone racing, it generalizes to other settings in which a micro air vehicle may only have sparse access to velocity and/or position measurements.

scholarly journals A New Variance-Covariance Matrix for Improving Positioning Accuracy in High-Speed GPS Receivers

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7324
Author(s):  
Narjes Rahemi ◽  
Mohammad Reza Mosavi ◽  
Diego Martín
Keyword(s):  
Kalman Filter ◽  
Covariance Matrix ◽  
High Speed ◽  
Dynamic Models ◽  
Unscented Kalman Filter ◽  
Statistical Characteristics ◽  
Spatial Correlations ◽  
Positioning Accuracy ◽  
High Speeds ◽  
Variance Covariance Matrix

One of the main challenges in using GPS is reducing the positioning accuracy in high-speed conditions. In this contribution, by considering the effect of spatial correlation between observations in estimating the covariances, we propose a model for determining the variance–covariance matrix (VCM) that improves the positioning accuracy without increasing the computational load. In addition, we compare the performance of the extended Kalman filter (EKF) and unscented Kalman filter (UKF) combined with different dynamic models, along with the proposed VCM in GPS positioning at high speeds. To review and test the methods, we used six motion scenarios with different speeds from medium to high and examined the positioning accuracy of the methods and some of their statistical characteristics. The simulation results demonstrate that the EKF algorithm based on the Gauss–Markov model, along with the proposed VCM (based on the sinusoidal function and considering spatial correlations), performs better and provides at least 30% improvement in the positioning, compared to the other methods.

Kalman Filter-Based Sensor Fusion for Improving Localization of AGV

2012 ◽  
Vol 488-489 ◽  
pp. 1818-1822 ◽  
Author(s):  
In Seong Lee ◽  
Jae Yong Kim ◽  
Jun Ha Lee ◽  
Jung Min Kim ◽  
Sung Sin Kim
Keyword(s):  
Kalman Filter ◽  
Sensor Fusion ◽  
High Precision ◽  
Navigation System ◽  
High Speed ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Positioning Accuracy ◽  
Global Positioning ◽  
Precision Device

This paper proposes localization using sensor fusion with a laser navigation and an inertial navigation system for indoor mobile. The laser navigation is a device that measures angle and distance between the robot and the reflectors. Although it is the high-precision device for indoor global positioning, there is a problem that the accuracy of laser navigation significantly drops while moving at high speed and rapid turning. To solve this problem, the laser navigation was fused to inertial navigation system through Kalman filter. For experiment, we use omnidirectional robot with Mecanum Wheels and analyze the positioning accuracy according to driving direction of the robot.

Sign in / Sign up

Export Citation Format

Share Document