scholarly journals ACCURACY ENHANCEMENT OF A LOW COST INS/GPS INTEGRATION SYSTEM FOR LAND APPLICATIONS

2009 ◽  
Vol 12 (4) ◽  
pp. 30-38
Author(s):  
Tan Duc Tran ◽  
Thuy Phu Nguyen
Keyword(s):  
Kalman Filter ◽  
Good Accuracy ◽  
Low Cost ◽  
Integration System ◽  
Feed Forward ◽  
Accuracy Enhancement ◽  
Motion Constraints ◽  
Vehicle Motion ◽  
Gps System

In this paper, the design of the low cost INS/GPS integration system is addressed with good accuracy. The Strapdown INS (SINS) and Cascade Kalman filter have been tested to ensure that the system can be operated flexibly between feed forward and feedback modes due to various GPS conditions. The vehicle motion constraints are also utilized to reduce the INS error degradation during the periods of GPS unavailability. The experiment results shown that the INS/GPS system can be applied to land applications in challenging GPS environments.

THE DEVELOPMENT OF AN EMBEDDED, INTEGRATED INS/GPS SYSTEM

2005 ◽  
Vol 15 (02) ◽  
pp. 193-198 ◽  
Author(s):  
T. Y. JIANG ◽  
J. GUZMAN ◽  
H. Z. LI ◽  
Z. M. GONG
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Integrated System ◽  
Sensor Selection ◽  
Development Work ◽  
Integration System ◽  
Loosely Coupled ◽  
System A ◽  
Critical Problems ◽  
Gps System

An embedded, real-time, loosely-coupled INS/GPS integration system has been developed and used for an unmanned land vehicle's control and navigation. For this integrated system, a Kalman filter software is used for INS error damping and corrections via the weighted aiding of a GPS output. The detailed development work will be presented in this paper including algorithm simplification, sensor selection and critical problems solving. Vehicular trial is also conducted. Simulated outage in GPS availability is made to assess the bridging accuracy of this system.

Interoperability versus Integration of Omega and GPS

1990 ◽  
Vol 43 (2) ◽  
pp. 229-237
Author(s):  
Henry B. Schlachta ◽  
John Studenny
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Capital Investment ◽  
Good Accuracy ◽  
Long Beach ◽  
Modes Of Operation ◽  
Gps Navigation ◽  
Almost All ◽  
Gps System ◽  
Navigation Accuracy

This paper was presented at the 14th annual meeting of the International Omega Association, held at Long Beach, California in October 1989.The integration of Omega and GPS sensors into a single navigation system offers the advantages of good accuracy under almost all signal conditions, low capital investment, and certifiable worldwide navigation. The accuracy of the existing Omega network can be progressively improved as GPS satellite coverage is fully implemented. This is done by having the GPS system update the Omega navigator. Eventually, the same equipment can provide full GPS navigation accuracy but with Omega as a back-up.This paper proposes a method of further improving the overall accuracy and reliability of Omega-GPS navigation. The conceptsof Omega-GPS integration, interoperability, modes of operation, and Kalman filter data fusion are presented. Four interoperability modes of operation and their ability to improve navigation reliability are also discussed.

Techniques for assessing the performance of circuit materials at microwave and millimetre-wave frequencies

2003 ◽  
Vol 783 ◽  
Author(s):  
Charles E Free
Keyword(s):  
Loss Tangent ◽  
Good Accuracy ◽  
Low Cost ◽  
Resonant Cavity ◽  
New Technique ◽  
Millimetre Wave ◽  
A New Technique

This paper discusses the techniques that are available for characterising circuit materials at microwave and millimetre wave frequencies. In particular, the paper focuses on a new technique for measuring the loss tangent of substrates at mm-wave frequencies using a circular resonant cavity. The benefits of the new technique are that it is simple, low cost, capable of good accuracy and has the potential to work at high mm-wave frequencies.

Low-Cost Hardware Implementation of Human Blood Identification Device Using Feed-Forward Artificial Neural Network on FPGA

2018 ◽  
Author(s):  
Rizki Eka Putri ◽  
Denny Darlis
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Low Cost ◽  
Image Resolution ◽  
Blood Type ◽  
Feed Forward ◽  
Testing Facility ◽  
System Input ◽  
Artificial Neural ◽  
Short Time

This article was under review for ICELTICS 2018 -- In the medical world there is still service dissatisfaction caused by lack of blood type testing facility. If the number of tested blood arise, a lot of problems will occur so that electronic devices are needed to determine the blood type accurately and in short time. In this research we implemented an Artificial Neural Network on Xilinx Spartan 3S1000 Field Programable Gate Array using XSA-3S Board to identify the blood type. This research uses blood sample image as system input. VHSIC Hardware Discription Language is the language to describe the algorithm. The algorithm used is feed-forward propagation of backpropagation neural network. There are 3 layers used in design, they are input, hidden1, and output. At hidden1layer has two neurons. In this study the accuracy of detection obtained are 92%, 92%, 92%, 90% and 86% for 32x32, 48x48, 64x64, 80x80, and 96x96 pixel blood image resolution, respectively.

scholarly journals A DCM Based Attitude Estimation Algorithm for Low-Cost MEMS IMUs

2015 ◽  
Vol 2015 ◽  
pp. 1-18 ◽  
Author(s):  
Heikki Hyyti ◽  
Arto Visala
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Low Cost ◽  
Direction Cosine ◽  
Estimation Algorithm ◽  
Calibration Method ◽  
Attitude Estimation ◽  
Mems Sensors ◽  
Acceleration Sensors ◽  
Adaptive Extended Kalman Filter

An attitude estimation algorithm is developed using an adaptive extended Kalman filter for low-cost microelectromechanical-system (MEMS) triaxial accelerometers and gyroscopes, that is, inertial measurement units (IMUs). Although these MEMS sensors are relatively cheap, they give more inaccurate measurements than conventional high-quality gyroscopes and accelerometers. To be able to use these low-cost MEMS sensors with precision in all situations, a novel attitude estimation algorithm is proposed for fusing triaxial gyroscope and accelerometer measurements. An extended Kalman filter is implemented to estimate attitude in direction cosine matrix (DCM) formation and to calibrate gyroscope biases online. We use a variable measurement covariance for acceleration measurements to ensure robustness against temporary nongravitational accelerations, which usually induce errors when estimating attitude with ordinary algorithms. The proposed algorithm enables accurate gyroscope online calibration by using only a triaxial gyroscope and accelerometer. It outperforms comparable state-of-the-art algorithms in those cases when there are either biases in the gyroscope measurements or large temporary nongravitational accelerations present. A low-cost, temperature-based calibration method is also discussed for initially calibrating gyroscope and acceleration sensors. An open source implementation of the algorithm is also available.

scholarly journals Integration of GPS, Monocular Vision, and High Definition (HD) Map for Accurate Vehicle Localization

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3270 ◽  
Author(s):  
Hao Cai ◽  
Zhaozheng Hu ◽  
Gang Huang ◽  
Dunyao Zhu ◽  
Xiaocong Su
Keyword(s):  
Kalman Filter ◽  
Low Cost ◽  
Kinematic Model ◽  
Intelligent Vehicles ◽  
Monocular Vision ◽  
Measurement Unit ◽  
High Definition ◽  
Position Information ◽  
Vehicle Localization ◽  
Lateral Distance

Self-localization is a crucial task for intelligent vehicles. Existing localization methods usually require high-cost IMU (Inertial Measurement Unit) or expensive LiDAR sensors (e.g., Velodyne HDL-64E). In this paper, we propose a low-cost yet accurate localization solution by using a custom-level GPS receiver and a low-cost camera with the support of HD map. Unlike existing HD map-based methods, which usually requires unique landmarks within the sensed range, the proposed method utilizes common lane lines for vehicle localization by using Kalman filter to fuse the GPS, monocular vision, and HD map for more accurate vehicle localization. In the Kalman filter framework, the observations consist of two parts. One is the raw GPS coordinate. The other is the lateral distance between the vehicle and the lane, which is computed from the monocular camera. The HD map plays the role of providing reference position information and correlating the local lateral distance from the vision and the GPS coordinates so as to formulate a linear Kalman filter. In the prediction step, we propose using a data-driven motion model rather than a Kinematic model, which is more adaptive and flexible. The proposed method has been tested with both simulation data and real data collected in the field. The results demonstrate that the localization errors from the proposed method are less than half or even one-third of the original GPS positioning errors by using low cost sensors with HD map support. Experimental results also demonstrate that the integration of the proposed method into existing ones can greatly enhance the localization results.

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