Michael Kohl

Abstract

Over the last three decades, gyroscopes have become increasingly miniaturized and have enabled the design of very small, light, and cost-effective inertial measurement units (IMUs). This allows new applications of inertial sensors. The motion measurement or motion capture of structures which, in contrast to classic inertial navigation, are no longer single rigid bodies but can show a variable shape is such a new application. However, this approach is only realizable if such a “flexible” structure is equipped with several spatially distributed and closely networked IMUs. At the same time, this measurement approach, similar to classic inertial navigation, requires the use of additional, complementary aiding sensors, which must also be distributed across the structure. Such systems or networks, which consist of inertial and structural sensors in parallel, are novel and not state of the art. This letter, therefore, presents a modular, experimental sensor system of this type designed as a demonstrator for basic research purposes, describes the specific challenges in designing the system, and reports on initial practical experiences in using the system on a test rig for the motion measurement of a flexible beam.

Abstract

Integrated Motion Measurement of flexible structures extends classical strapdown navigation with inertial sensors including conventional aiding techniques. The goal is to improve the navigation performance and to detect structural deformations during the navigation process for advanced motion measurement applications such as structural health monitoring or control. This is achieved by distributing accelerometers, gyroscopes, and aiding sensors over the structure and by including dominant flexible motions into the navigation system model. This paper provides an experimental proof of the functionality of such integrated motion measurement systems (IMMSs) and further investigates several system-related settings and parameters such as additional sensor types, method and number of sensor placement, together with the influence of classical estimation aspects. It is based on a previous theoretical study 1, which provided simulative proof and a first system design.

Abstract

Utilizing Micro-Electro-Mechanical-System (MEMS) Inertial-Measurement-Units (IMUs) for determining north is increasingly attractive due to their improving performance, availability, and low cost in comparison to, e.g., Fiber-Optic-Gyroscopes (FOGs). This option is particularly relevant for navigation applications employing less reliable north-finding methods such as magnetometers for indoor use. This paper compares the north-finding capability and the effect of the data acquisition time of three MEMS IMUs with different performance grades. Furthermore, this paper discusses the influence of the north-finding capability of these IMUs on the precision of pedestrian navigation as a demanding application.Initially, a simplified version of the maytagging procedure executed on a precision rate table revealed the noticeable effect of the earth rotation rate on the gyroscope output. Then, north-finding tests were conducted by applying a maytagging procedure. The data sets from each position were averaged and further processed using the method of least squares. Results are presented by comparing three performance grades at different data acquisition times. In a third step, a pedestrian navigation algorithm was modified for utilizing the IMU’ north-finding capability and the impact of earth rotation on navigation accuracy was examined. The results of the study lead to the following conclusions:•The outputs of all three MEMS IMUs reflect noticeably the earth rotation rate and succeed in finding the north direction with a maytagging procedure.•Maytagging for north-finding or similar procedures seem to be still necessary for all IMUs in test since distinguishing between bias and real sensor signal is still challenging in one-shot-measurements.•The accuracy of north-finding generally benefits from long data acquisition times. The results also indicate, however, that north-finding is also feasible with data acquisition times shorter than the duration marked by the bias instability in the Allan Variance.•The implementation of the effects of the earth rotation rate leads to a general improvement in the accuracy of the pedestrian navigation algorithm. Especially, the accuracy of the yaw-angle estimation benefits most.

Abstract

MEMS IMUs are the most common units for data collection in the field of pedestrian inertial navigation, whereas on the data processing side a Kalman filter is a broadly used algorithm for the required pedestrian motion state estimation. The paper investigates the influence of several IMU-related parameters on the accuracy of this state estimation. It complements a former study of the authors that discussed algorithmic enhancements of the data processing leading to noteworthy accuracy improvements compared to the state of the art of pedestrian inertial navigation 1. Therefore, the IMU-related parameters will be reassessed against this enhanced numerical background. The paper is based on test walks employing a special test track and a wearable overshoe equipped with two MEMS IMUs of different accuracy levels. The IMU data were then fused with a continuous-discrete total-state Kalman filter. The only aiding techniques were Zero Velocity Update (ZUPT) and Zero Angular Rate Update (ZARU) to exclude influences from other sensors than the IMUs. The paper considers the following points concerning their influence on the position and on the yaw estimation accuracy, which are especially error-prone motion states in pure ZUPT- and ZARU-aided inertial navigation: •Influence of the IMU sample rate for inertial data collection. •Influence of a pretest IMU calibration with respect to bias, scale factor, and sensor axes misalignment. •Incorporation of Allan variance sensor parameters in the Kalman filter. The parameters were used to model all inertial sensor biases by a random walk or a Gauss-Markov process of first order. The results on these points can be outlined as follows. •Higher sample rates show a significantly improved capture of the step dynamics leading to smoother and more accurate estimates of the walking path. •Against the enhanced numerical background mentioned above, the IMU calibration leads typically (but not always) to further accuracy improvements. •The state of the art in ZUPT-based pedestrian navigation does not show a clear advantage of using a Gauss-Markov process for the IMU sensor biases. This seems also to be the case for the algorithmic improvements mentioned above.

Abstract

During the last 10 to 15 years, pedestrian navigation based on zero velocity updates (ZUPT) has become very popular. One of the main reasons for this is the increasing availability of small, low-cost inertial measurement units. However, the processing of the data from these units for pedestrian navigation is almost exclusively based on algorithmic features that originate from classical inertial navigation with high-grade sensors. In addition, the historical background of the ZUPT approach presupposes also sensors with high accuracy. This leads to the problem that neither the ZUPT approach nor the algorithmic features mentioned are consistent with the accuracy level of the inertial measurement units used normally for pedestrian navigation. Therefore, the question arises of whether the usual algorithmic basics and numerical procedures employed by ZUPT-based pedestrian navigation are adequate. Supported by a literature review showing the state of the art, this study investigates the effect of basics such as the system states and the usage of Runge-Kutta method on the navigation accuracy from pedestrian inertial data aided by ZUPT. To this end, a comparative processing of a well-known, published dataset of a short walk and of own data from the authors using different data fusion algorithms was employed. The important results of this study are that the often-used omission of inertial sensor biases cannot be recommended, that a continuous-discrete Kalman filter in combination with a Runge-Kutta algorithm performs better than traditional filter configurations, and that total navigation states are an interesting alternative to classical navigation error-states.