The procedure of determining the initial values of the attitude angles (pitch, roll, and heading) is known as the alignment. Also, it is essential to align an inertial system before the start of navigation. Unless the inertial system is not aligned with the vehicle, the information provided by MEMS (microelectromechanical system) sensors is not useful for navigating the vehicle. At the moment MEMS gyroscopes have poor characteristics and it’s necessary to develop specific algorithms in order to obtain the attitude information of the object. Most of the standard algorithms for the attitude estimation are not suitable when using MEMS inertial sensors. The wavelet technique, the Kalman filter, and the quaternion are not new in navigation data processing. But the joint use of those techniques for MEMS sensor data processing can give some new results. In this paper the performance of a developed algorithm for the attitude estimation using MEMS IMU (inertial measurement unit) is tested. The obtained results are compared with the attitude output of another commercial GPS/IMU device by Xsens. The impact of MEMS sensor measurement noises on an alignment process is analysed. Some recommendations for the Kalman filter algorithm tuning to decrease standard deviation of the attitude estimation are given. 1. Introduction Navigation can be defined as the process of determining the position, orientation, and velocity of an object. A GPS-based navigation is quick and drift free and is readily available most of the time. However, as the GPS requires direct line of sight signals from at least four GPS satellites, the navigation can be frequently interrupted in the land based applications. The GPS signal gets lost due to various factors such as the blockage by buildings, trees, and other natural and nonnatural obstructions. This affects both the amplitude and phase of the received satellite signals and causes the receiver to lose lock on the blocked satellite, meaning that it needs both to reacquire the signal and to resolve the ambiguities in the phase measurements. Both these processes take time, and if there are several satellites affected, the receiver cannot provide a position solution for a significant period of time. Also it is worth mentioning that the data rate for the GPS can be too low for the particular application. In such situations when the GPS signals are not available, a relative navigation can be performed using the inertial sensors (accelerometers and gyroscopes) and magnetometers. The strapdown inertial navigation system (SINS) has been widely used in
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