h. alaeiyan

A common technique for navigation and positioning applications is the Global Positioning System (GPS)/Inertial Navigation System (INS) integration, which combines the strengths of GPS and INS to offer accurate and reliable information. As a standalone system, the performance of the INS deteriorates as time is passed. Kalman Filter (KF) is used for GPS/INS integration, and its performance is excellent for simple data. However, in a complex and natural set environment, its performance degrades when the system performs relatively long; therefore, resolving the long-time problem for the GPS/INS system is challenging. The novelty of this paper is GPS/INS integration with the Faded Kalman Filter (FKF). In the FKF, the measurement updates are weighted differently to adapt to changes in the system. This approach allows the filter to adapt to changes or uncertainties in the system dynamics. GPS/INS integration performance is significantly improved using this algorithm rather than a simple KF. An average of 45% reduces the positioning errors compared to traditional KF.

Due to the increasing use of Inertial Navigation Systems (INSs) in various fields, increasing the accuracy and efficiency of this system is significant. INS is a standalone system that works in any weather conditions and can provide continuous navigation information of position, velocity, and attitude without affecting the environment. However, due to errors in gyroscope and accelerometer sensors, the accuracy of INS sensor performance decreases over time. Therefore, the main problem in using INS for navigation systems is the unlimited errors that occur over time. In other words, over time, its performance suffers from errors that are increasing rapidly. Therefore, to achieve the desired outputs, noise removal must be performed. In this paper, according to the types of sensors, the best noise removal method has been presented. The wavelet noise removal based on the lifting scheme is utilized for de-noising. The lifting method is real-time and does not require blocking the received data. Also, we have leveraged some multi-level liftings. Further, we propose an optimization method based on the genetic algorithm to increase accuracy by combining the results of multi-level liftings. For evaluation, two types of dynamic and static data have been used. The achieved results have clarified the effectiveness of our proposed method. Therefore, a suitable method for good, reliable, and fast noise removal is presented.