Strapdown Inertial Navigation Systems for Positioning Mobile Robots-MEMS Gyroscopes Random Errors Analysis Using Allan Variance Method

Adaptive Dynamic Analysis of MEMS Gyroscope Random Noise Based on PID-DAVAR

As a MEMS gyroscope is susceptible to environmental interference, its performance is degraded due to random noise. Accurate and rapid analysis of random noise of MEMS gyroscope is of great significance to improve the gyroscope's performance. A PID-DAVAR adaptive algorithm is designed by combining the PID principle with DAVAR. It can adaptively adjust the length of the truncation window according to the dynamic characteristics of the gyroscope's output signal. When the output signal fluctuates drastically, the length of the truncation window becomes smaller, and the mutation characteristics of the intercepted signal are analyzed detailed and thoroughly. When the output signal fluctuates steadily, the length of the truncation window becomes larger, and the intercepted signals are analyzed swiftly and roughly. The variable length of the truncation window ensures the confidence of the variance and shortens the data processing time without losing the signal characteristics. Experimental and simulation results show that the PID-DAVAR adaptive algorithm can shorten the data processing time by 50%. The tracking error of the noise coefficients of angular random walk, bias instability, and rate random walk is about 10% on average, and the minimum error is about 4%. It can accurately and promptly present the dynamic characteristics of the MEMS gyroscope's random noise. The PID-DAVAR adaptive algorithm not only satisfies the requirement of variance confidence but also has a good signal-tracking ability.

Customizable Stochastic High-Fidelity Model of the Sensors and Camera Onboard a Fixed Wing Autonomous Aircraft

The navigation systems of autonomous aircraft rely on the readings provided by a suite of onboard sensors to estimate the aircraft state. In the case of fixed wing vehicles, the sensor suite is usually composed by triads of accelerometers, gyroscopes, and magnetometers, a Global Navigation Satellite System (GNSS) receiver, and an air data system (Pitot tube, air vanes, thermometer, and barometer), and it is often complemented by one or more digital cameras. An accurate representation of the behavior and error sources of each of these sensors, together with the images generated by the cameras, is indispensable for the design, development, and testing of inertial, visual, or visual-inertial navigation algorithms. This article presents realistic and customizable models for each of these sensors; a ready-to-use C++ implementation is released as open-source code so non-experts in the field can easily generate realistic results. The pseudo-random models provide a time-stamped series of the errors generated by each sensor based on performance values and operating frequencies obtainable from the sensor's data sheets. If in addition, the simulated true pose (position plus attitude) of the aircraft is provided, the camera model generates realistic images of the Earth's surface that resemble those taken with a real camera from the same pose.

Developing and Investigating the Analyzers of Kinematic Viscosity and Density of Petroleum Products on Throttle Bridge Transducers

Measuring the viscosity and density of petroleum products is important for their proper production, transportation and application. Viscosity and density are the main parameters determining the composition and structure of petroleum products. Therefore, in the industry, to control the quality of petroleum products during various technological processes, automatic and non-automatic devices are used for their measurement. The accuracy of measuring the viscosity and density of petroleum products is an important factor. The authors analyzed different methods of measuring the viscosity and density of petroleum products and synthesized three versions of throttle bridge transducers. These versions implement differential measurement methods and have different numbers of laminar and turbulent throttles. The authors obtained new equations of static conversion functions by channels of measuring the kinematic viscosity and density of petroleum products of the proposed throttle bridge transducers. The authors developed a block diagram and designed measuring equipment to study experimentally the static characteristics of the throttle bridge transducers. The authors determined that the maximal relative deviations of the results of experimental studies from numerical calculations of a static conversion factor by channels of kinematic viscosity and density were 5.88% and 8.76%, respectively. The authors developed two versions of automatic devices for measuring the kinematic viscosity and density of petroleum products based on the proposed throttle bridge transducers. The first version is an automatic analyzer with tracking astatic balancing. The second version is an automatic analyzer with deployment balancing. The authors developed constructions of both versions of automatic analyzers and obtained the results of experimental measuring of the kinematic viscosity and density of petroleum products in different ranges.

Analytical Assessment of the Propagation of Colored Sensor Noise in Strapdown Inertial Navigation

Knowledge of the propagation of sensor errors in strapdown inertial navigation is crucial for the design of inertial and integrated navigation systems. The propagation of initialization errors and deterministic sensor errors is well covered in the literature. If considered at all, the propagation of inertial sensor noise has typically been assessed for un-correlated (white) Gaussian noise. Real inertial sensor noise, however, is time-correlated (colored) and best described by a combination of different stochastic processes. In this paper, we demonstrate how a navigation system's response to colored noise input differs from the response to bias-like or white noise inputs. We present a method for assessing the navigation error from various inertial sensor noise processes without the need for time-consuming Monte Carlo simulations and demonstrate its application and validity with real sensor data. The proposed method is used to determine in which scenarios the sensor's real noise can be approximated by simple white Gaussian noise. The results indicate that neglecting colored sensor noise is justified for many applications, but should be checked individually for each sensor configuration and mission.