performance enhancement of gps/ins integrated navigation system using wavelet based de-noising method

accuracy of inertial navigation system (ins) is limited by inertial sensors imperfections. before using inertial sensors signals in the data fusion algorithm, noise removal method should be performed, in which, wavelet decomposition method is used. in this method the raw data is decomposed into high and low frequency data sets. in this study, wavelet multi-level resolution analysis (wmra) technique is used as an efficient pre-filter method for inertial measurements to improve the performance of ins. this technique improves navigation accuracy, eliminating high frequency noise of inertial measurements. optimum values of the level of decomposition are selected to obtain minimum error. successfully performing the de-noising method improves the sensors’ signal-to-noise ratios and removes short term errors mixed with motion dynamics and finally provides cleaner and more reliable data to the ins. performance of an error state kalman filter based gps/ins integrated navigation system with the loosely coupled structure is studied using real measurement while gps outages. results show that the average value of the root mean square of the position errors using the wmra procedure is reduced about 14% compared to those using the raw inertial measurements.