مشروع البحث: Low cost inertial sensors calibration using allan variance
| dc.contributor.advisor | د.ابراهيم نصر جليطة | |
| dc.date.accessioned | 2026-10-13T09:13:49Z | |
| dc.date.available | 2026-10-13T09:13:49Z | |
| dc.description | In this dissertation, the Allan variance technique be used in modeling the stochastic errors of the inertial sensors. By performing a simple operation on the entire length of data, a characteristic curve is obtained whose inspection provides a systematic characterization of various random errors contained in the inertial-sensor output data. By applying the Allan-variance method to the whole data set, the implementation of the algorithm in MATLAB environment for obtaining stochastic errors in inertial sensors, results shows that the Allan variance analysis is a powerful technique to investigate the sensor error behaviors on different time scales, this analysis is an effective method for error modeling and parameter estimation, which is significant for the improve of navigation systems. | |
| dc.description.abstract | Micro-electromechanical system (MEMS) accelerometers and gyroscopes also known as inertial sensors are suitable for the inertial navigation system (INS) of many applications due to the low price, small dimensions and light weight. Some of these sensors are capable of producing incredibly accurate measurements of acceleration and angular velocity. However, there is no inertial sensor that can produce perfect measurements of acceleration and angular velocity. No matter how good, there will always be imperfections in the inertial data that it returns. These imperfections caused by several different kinds of errors, and each has a different effect on the data. The inertial sensor errors consist of deterministic and stochastic parts. The deterministic part which can be determined by calibration and therefore can be removed from the raw measurements. The stochastic part contains random errors (noises) which can not be removed from the measurements and should be modeled as stochastic processes, in order to eliminate negative effect of these random errors. Understanding and modeling each of these errors can be used to improve the overall quality of the inertial sensor data. | |
| dc.identifier | 6030 | |
| dc.identifier.uri | https://dspace.academy.edu.ly/handle/123456789/2626 | |
| dc.subject | LOW COST INERTIAL SENSORS CALIBRATION | |
| dc.title | Low cost inertial sensors calibration using allan variance | |
| dspace.entity.type | Project | |
| project.endDate | 2015 | |
| project.funder.name | الهندسة الكهربائية والحاسوب | |
| project.investigator | عبدالكريم ابوالقاسم الطاهر | |
| project.startDate | 2014 |