A single low cost inertial measurement unit (IMU) is often used in conjunction with GPS to increase the accuracy and improve the availability of the navigation solution for a pedestrian navigation system. This paper develops several fusion algorithms for using multiple IMUs to enhance performance. In particular, this research seeks to understand the benefits and detriments of each fusion method in the context of pedestrian navigation. Three fusion methods are proposed. First, all raw IMU measurements are mapped onto a common frame (i.e., a virtual frame) and processed in a typical combined GPS-IMU Kalman filter. Second, a large stacked filter is constructed of several IMUs. This filter construction allows for relative information between the IMUs to be used as updates. Third, a federated filter is used to process each IMU as a local filter. The output of each local filter is shared with a master filter, which in turn, shares information back with the local filters. The construction of each filter is discussed and improvements are made to the virtual IMU (VIMU) architecture, which is the most commonly used architecture in the literature. Since accuracy and availability are the most important characteristics of a pedestrian navigation system, the analysis of each filter’s performance focuses on these two parameters. Data was collected in two environments, one where GPS signals are moderately attenuated and another where signals are severely attenuated. Accuracy is shown as a function of architecture and the number of IMUs used.

https://www.mdpi.com/1424-8220/11/7/6771/pdf

Data fusion algorithms for multiple inertial measurement units.

This paper discusses the development of a low-cost, redundant, strapdown inertial measurement unit (IMU). The unit comprises four ceramic vibrating structure gyroscopes and four QLC 400 acceleromet...

A Low-Cost, Redundant Inertial Measurement Unit for Unmanned Air Vehicles:

Inertial sensor arrays present the possibility of improved and extended sensing capabilities as compared to customary inertial sensor setups. Inertial sensor arrays have been studied since the 1960s and have recently received a renewed interest, mainly thanks to the ubiquitous micro-electromechanical (MEMS) inertial sensors. However, the number of variants and features of inertial sensor arrays and their disparate applications makes the literature spread out. Therefore, in this paper we provide a brief summary and literature review on the topic of inertial sensor arrays. Publications are categorized and presented in a structured way; references to +300 publications are provide. Finally, an outlook on the main research challenges and opportunities related to inertial sensor arrays is given.

Inertial sensor arrays — A literature review

In this article, we investigate two different algorithms for the integration of GPS with redundant MEMS-IMUs. Firstly, the inertial measurements are combined in the observation space to generate a synthetic set of data which is then integrated with GPS by the standard algorithms. In the second approach, the method of strapdown navigation needs to be adapted in order to account for the redundant measurements. Both methods are evaluated in experiments where redundant MEMS-IMUs are fixed in different geometries: orthogonally-redundant and skew-redundant IMUs. For the latter configuration, the performance improvement using a synthetic IMU is shown to be 30% on the average. The extended mechanization approach provides slightly better results (about 45% improvement) as the systematic errors of the individual sensors are considered separately rather than their fusion when forming compound measurements. The maximum errors are shown to be reduced even by a factor of 2.

/pdf/redundant-mems-imu-integrated-with-gps-for-performance-2p6mmc9lwr.pdf

Redundant MEMS-IMU integrated with GPS for performance assessment in sports

Optimal redundant sensor configuration for accuracy increasing in space inertial navigation system

The problem of proper placement of the inertial sensors to optimize system performance is important to system designers. This is especially true of redundant strapdown systems that employ more sensors than the conventional, mutually orthogonal sets of three. In systems designed for a free fall environment and with no preferred direction, such as for spacecraft attitude reference, the sensor input axes should divide the three-space equally, and can thus be viewed as being normal to the faces of regular polyhedra.' In Earth-bound inertial navigators, the effect of gravity-dependent sensor errors and the generally reduced effect of azimuth errors on navigation accuracy significantly alter the situation. Both effects tend to decrease sensor elevation angles in an optimized system. Formulas are derived and curves are drawn for optimum sensor elevation and azimuth angles vs a 0-sensitive sensor error parameter, and a mission relative azimuth error parameter. Tetrad, pentad, and hexad arrays are analyzed, affording a dramatic improvement in accuracy as well as autonomous fault isolation and/or detection capability.

Arthur J. Pejsa

Papers

Optimum Skewed Redundant Inertial Navigators

Optimum skewed redundant inertial navigators