## 1 Introduction
Researchers from the School of Automation at the Beijing Institute of Technology developed a method to filter the MEMS gyro random drift. The experimental results obtained with a MEMS gyro showed that the method reduced the high-frequency noise, restrained the gyro random drift, decreased the mean square error and increased the signal-to-noise ratio [67]. Through these techniques, the short-term noise is substantially mitigated, limiting the band of the INS signals, which means optimal low-pass filtering. Unfortunately, they have several limitations in removing the long-term sensor noise, which still influences the performance characteristics of the INS for long-term signal processing; it may be mitigated or eliminated by using a complex navigator, obtained by integrating the INS with a navigation system based on another operating principle [68; 69; 70]. From another point of view, the long-term noise is situated in the band of 0 Hz to 100 Hz, together with the frequencies characterizing the vehicle's dynamics, i.e., with the navigation useful signals measured by the sensors; as a consequence, direct filtering of this noise is not recommended and for its reduction, the data fusion algorithms from multiple sensors need to be applied. A first identified method estimates and removes the noise by fusing the INS data with another navigation system data (such as a GPS system) [2; 71; 72; 73]. The procedure uses an INS error model, which takes into account the sensor's noise; for example, the long-term noise component may be modeled by using random processes like random bias, random walk, Gauss-Markov, or periodic random processes [74]. Another method of reducing this noise is based on the data fusion between similar sensors in a redundant IMU, with sensors arranged in various configurations [69; 73; 75]. This redundant variant can be also improved with various algorithms for fault detection and isolation. Also, depending on the IMU redundant structure and the data fusion method used, this procedure may produce a reduction in both long-term and short-term components of the noise but also can bring some gains at the level of bias for the signal delivered to the navigation processor. In terms of the IMU structures, the literature revealed several variants to use and to relatively arrange the sensors in order to detect all information necessary in obtaining the solution of navigation. Having in mind that the monitoring of a vehicle in 3D space requires information regarding the vehicle's absolute acceleration and angular speed, it is necessary to have a minimum of six sensors in IMU, measuring the acceleration and angular speed components along three axes (six measurements). The simplest and very well-known way to arrange the sensors in IMU is on two right rectangular trihedrals, one for gyros and another one for accelerometers; the two formed sensor triads need to have parallel axes one against the other [2]. Another possibility to arrange the sensors in IMU derived from the need to obtain a degree of redundancy in IMU, and generated a multitude of research studies related to the sensor's optimal arrangement, also providing additional advantages represented by the possibility of detecting and isolating defects inside the IMU. The first study in this concern is attributed to [PERSON] in 1974, but the theory was thorough in 1993 by [PERSON] [75] and subsequently generated a lot of applications [76; 77; 78]; it stipulated that the sensor's optimal arrangement can be obtained by orienting their axes of sensitivity in directions perpendicular to the lateral faces of some regular polyhedra (tetrahedron, cube, dodecahedron, icosahedron, etc.) or orienting their axes of sensitivity on several cones of revolution with a common axis of symmetry, at the intersection of these cones with half-planes delimited by this axis of symmetry, so that these axes of sensitivity form equal angles between them [75].