This paper presents an innovative model for integrating thermal compensation of gyro bias error into an augmented state Kalman filter. The developed model is applied in the Zero Velocity Update filter for inertial units manufactured by exploiting Micro Electro-Mechanical System (MEMS) gyros. It is used to remove residual bias at startup. It is a more effective alternative to traditional approach that is realized by cascading bias thermal correction by calibration and traditional Kalman filtering for bias tracking. This function is very useful when adopted gyros are manufactured using MEMS technology. These systems have significant limitations in terms of sensitivity to environmental conditions. They are characterized by a strong correlation of the systematic error with temperature variations. The traditional process is divided into two separated algorithms, i.e., calibration and filtering, and this aspect reduces system accuracy, reliability, and maintainability. This paper proposes an innovative Zero Velocity Update filter that just requires raw uncalibrated gyro data as input. It unifies in a single algorithm the two steps from the traditional approach. Therefore, it saves time and economic resources, simplifying the management of thermal correction process. In the paper, traditional and innovative Zero Velocity Update filters are described in detail, as well as the experimental data set used to test both methods. The performance of the two filters is compared both in nominal conditions and in the typical case of a residual initial alignment bias. In this last condition, the innovative solution shows significant improvements with respect to the traditional approach. This is the typical case of an aircraft or a car in parking conditions under solar input.

In recent years, the improvement of advanced micro-fabrication techniques has allowed the development of Micro Electro-Mechanical Systems (MEMS) inertial sensors. These sensors have the advantages of small volume, light-weight, high reliability and low-cost, so they result as the most common sensors used to perform the flight attitude calculation for Unmanned Aerial Systems (UASs). Even if they are small size and light-weight sensors, they suffer more than other higher grade gyros for some types of errors such as turn-on to turn-on bias, in-run bias, bias drift, scale factor drift and other environment dependent errors. In particular, the performance of MEMS inertial sensors is greatly affected by temperature variations, due to the sensitivity of silicon's material properties and gyro's packaging and electronics to temperature. This paper investigates the calibration of temperature effects on MEMS gyroscope bias drift, proposing an innovative calibration method, based on the use of Back-Propagation (BP) Neural Networks to model the nonlinear relationship between MEMS gyroscope null-voltage and temperature. BP Neural Networks have the advantages of nonlinear fitting, regardless of the mathematical model of the sensor and various non-linear factors. First of all, the reference model for bias trend vs. temperature is reported. Subsequently, the proposed innovative calibration method is compared with the traditional polynomial fitting technique. Finally, the performance of the proposed technique is discussed.

This paper presents a software application to derive system specifications for drones operating inside the future Unmanned Traffic Management system. It describes a software tool able to support the decision-making process, which is aimed at defining drone configurations that are customized on mission needs while accounting for requirements related to flight authorization. This tool is dedicated to support drone end users to find out the most appropriate configuration for a drone system used to carry out a specific mission. The tool aims at developing a standard approach to find out a suitable solution in terms of equipment to be used and operation framework, also in view of automatic authorization procedures by regulation entities. It can be used in a future intelligent transportation system for drones. The advantage is the reduction of the effort to manage the large number of different drone mission conditions.