Quadrotors are widely used for surveillance, mapping, and deliveries. In several scenarios the quadrotor operates in pure inertial navigation mode resulting in a navigation solution drift. To handle such situations and bind the navigation drift, the quadrotor dead reckoning (QDR) approach requires flying the quadrotor in a periodic trajectory. Then, using model or learning based approaches the quadrotor position vector can be estimated. We propose to use multiple inertial measurement units (MIMU) to improve the positioning accuracy of the QDR approach. Several methods to utilize MIMU data in a deep learning framework are derived and evaluated. Field experiments were conducted to validate the proposed approach and show its benefits.
For the reliable control of unmanned aerial and underwater vehicles, a robust attitude solution is needed in order to control the vehicle correctly along its three axes. Emerging sensor faults endanger the attitude estimates, and thus the control of the vehicle, and must be detected as early as possible in order to counteract and mitigate, or possibly even prevent, any negative effects. A common approach to detect erroneous sensor data is the use of sensor redundancies in which measurements of three or more sensors are compared. However, this concept is not applicable in all cases, especially with regard to unmanned vehicles that are often limited by size, weight, cost and other constraints. This paper describes an algorithm, developed to detect faults and degradation in navigation sensor data for an Autonomous Underwater Vehicle (AUV) with a limited navigation sensor suit, i.e. without sensor redundancies. The algorithm aims to calculate an integrity value for each sensor channel, based on the sensor faults or degradations detected. On the one hand, the calculated integrity data is meant to be used by Bayes filter based navigation algorithms (e.g. Kalman Filter) to adjust their confidence in the measured data used to correct the state predictions. On the other hand, the integrity values are used to monitor the overall health condition of the navigation sensor suit, enabling downstream software modules to come to decisions regarding the continuation of the mission.
In this paper, the influence of phase shift induced by the front-end amplifier and the digital control circuits on the quadrature suppression and the force-to-rebalance control loops is theoretically analyzed. Due to the existence of a phase shift, the output of the resonator gyroscope is affected not only by the damping mismatch, but also by the frequency splitting of the gyroscope which is the main contribution to the nonlinearity of scale factor of a RG. Based on the analytical results and established mathematical model of scale factor, the phase delay was characterized and compensated in the room temperature. Detailed experiments of phase compensation were conducted and results were analyzed. Experimental results show that with phase compensation, the quadrature suppression signal changes far less with the variation of external angular rate than that before phase compensation. This means that the coupling between the quadrature suppression signal and the force-to-rebalance control signal is greatly decreased. In addition, residual errors of the scale factor of the resonator gyroscope were also greatly decreased. It is shown that after compensation of the phase delay the residual error is reduced by a factor of 45 and the linearity of the scale factor of the resonator gyroscope was extremely increased by more than 50 times compared with results before compensation.
Inertial measurement units and navigation systems range from low-end tactical to strategic grade, spanning a very large spectrum of sensor performance. EMCORE makes use of its own quartz MEMS gyros for tactical grade systems, while for higher-end systems we use in-house optical gyroscopes (fiber optic and ring laser gyros). These gyroscopes and systems range from 10 °/hr down to 0.0001 °/hr performance over dynamic environment. These systems require accelerometers with suitable levels of performance for each IMU/INS grade. In a previous DGON ISS 2021 paper we described the quartz-MEMS IMU progress toward navigation grade performance [1]. This improvement was based on use of a modified conventional calibration method over a dynamic thermal environment. Here we report the latest experimental results (consistent with a high-end tactical grade accelerometer) which are much-improved due to a method of self-calibration incorporating multiple resonant modes of the accelerometer structure. The results we present are for the quartz MEMS accelerometer, which demonstrate a velocity random walk of $0.1\ \mu \mathrm{g}/\surd{\text{Hz}}$ , and bias instability of 30 ng. The accelerometer bias and scale factor drift over a full temperature range (-55 °C to +90 °C) were $25\ \mu \mathrm{g}$ and 25 ppm, respectively, after we improved the conventional calibration. Using an innovative advanced operational regime, called multiple-mode operation, a new self-calibration algorithm was developed demonstrating accelerometer bias over a dynamic thermal profile of less than $2\ \mu \mathrm{g}$ , thus, making the accelerometer suitable for high-end navigation grade systems.
This paper addresses the requirements on IMUs for future small and affordable short range missiles. Current developments in this area of missile systems are presented and also some functional requirements on the missiles are explained. From these requirements, the impact on missile design and missile functions are derived and finally the impact on the requirements on the used inertial sensor systems are presented. At the end, we will have to conclude some contradictory requirements for the IMUs: smaller, better, cheaper and last but not least safer.
