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At the beginning of 2021, the measurement of the Angle of Attack and of the Angle of Sideslip is still mainly conducted with physical protruding probes. Although several alternative methods have been proposed in literature, the attention is generally focused on data-driven methods and little discussion is conducted on the mathematical problem. If the formulation that allows to associate the aerodynamic angles to other flight parameters has a closed-form solution is still an open question in the field. This paper provides a closed-form solution for a restricted problem where one of the two angle is known. Moreover, a linearized solution is provided. The result section gives evidence of the approach in simulated environment, showing the advantages of the nonlinear solution with respect to the linear one.

In this work, the SAIFE project is presented highlighting its main innovative aspects and the verification strategy up to TRL 6. In fact, the SAIFE project is born to demonstrate in operative environment a synthetic solution to estimate the aircraft flow angles, angle-of-attack and angle-of-sideslip. The flow angle synthetic solution is based on a model-free scheme named ASSE and it is briefly described. The SAIFE solution is suitable for modern aircraft or it can be adopted on board drones and urban mobility air vehicles. The SAIFE project is introduced and its main advantages with respect to the state-of-the-art are presented. The SAIFE demonstrator is designed and manufactured to be verified first in laboratory and later validated during flight tests.

Several architectures exist to measure aerodynamic angles based on physical sensors. As far as unmanned aerial vehicle (UAV) is concerned, traditional systems hardly comply with reliability and redundancy requirements due to size and weight limitations. A patented virtual sensor, based on artificial neural network (ANN) techniques, named smart-air data, attitude and heading reference system (Smart-ADAHRS) has been investigated as a good estimator for aerodynamic angles in simulated environment. This paper focuses on flight testing procedures in operative environment and data processing for the Smart-ADAHRS validation with real data. As many factors interfere during the generation of the ANN training set, an accurate choice and integration of the flight test instrumentation (FTI) system components becomes crucial. A comprehensive description has been included about the FTI equipment and its influence on the neural network performance. Differences between numerical simulation and operative environment data are detailed as final aim of this work. At the end, feasible solutions are suggested to solve the typical gap between virtual and real scenario, both in terms of data analysis and neural network architecture.

In the area of synthetic and smart sensors for flow angle estimation for UAS applications, model-based, data-driven and model-free approaches represent the state-of-the-art techniques to estimate the angle-of-attack and angle-of-sideslip. Thanks to sensor fusion techniques, a synthetic sensor is able to provide estimation of flow angles without any dedicated physical sensors. A model-free approach, based on a set of nonlinear equations, demonstrates good performances when used with flight simulated data. As shown in this work, an iterative solver could not be adequate dealing with real flight data affected by common instrument uncertainties. In order to cope with real flight data, two deterministic solvers can be adopted that are based on neural techniques: pre-trained multilayer perceptron and generalised radial basis function neural networks. The neural networks considered in this work are trained with batch and sequential algorithms. All solvers are tested with noise-free and noisy signals simulating real flight instrument noise. The aim of the present work, in fact, is to provide a preliminary benchmark between the aforementioned solvers when used to solve the proposed nonlinear scheme for flow angle estimation dealing with instrument noise.

The estimation of the Angle of Attack (AOA) and Angle of Sideslip (AOS) is crucial for flight monitoring and control. However, a gap has been identified on the data selection technique for the class of estimators based on data-driven methods, such as the synthetic sensor based on Neural Network (NN). This paper proposes a Cross Validation (CV) technique applied on a manoeuver-based partitioning method to provide evidence that a given selection of data can lead to better estimation performance, with the final aim of providing a list of manoeuvers suitable for the training phase of the estimator. Results are shown using simulated data related to the CleanSky 2 project MIDAS.

The estimation of the angle of attack and sideslip angle is of fundamental importance for the situational awareness of an aerial vehicle. Historically, several accidents occurred due to failures of the traditional protruding probes applied to measure these two angles. The MIDAS project aims to design and develop a certifiable Air Data System capable of providing the entire set of Air Data, integrating a synthetic estimation of the aerodynamic angles. All operating conditions shall be taken into account, even those related to atmospheric phenomena. The wind effects (both steady and unsteady) represent a very challenging topic when design a synthetic sensor because of its intrinsic nature. In fact, the airflow surrounding the AC can be affected by several phenomena with a very wide range of characteristics (e.g. speed and direction range) that can be hardly simulated during the design stage. It is clear that the atmosphere condition (both steady and unsteady) can affect this particular kind of sensor, and it must be analysed the error in the presence of the wind. The paper shows the estimation error due to the steady wind field and correction to be applied to previous synthetic sensors design in order to be reliable both in still air and in presence of the wind.

Nowadays, some alternative methods exist for the replacement of physical vanes (or probes) for aerodynamic angles (angle of attack and sideslip) with synthetic solutions. The results are promising and there is a growing interest for the industry in this particular solution. However, a lack of methods has been observed to estimate their performance and to compare them. The MIDAS project, funded in the Clean Sky 2 frame, will provide the aerospace community with an innovative modular digital air data system (ADS) based on synthetic sensors for aerodynamic angles. To meet the system requirement specifications given by the project leader, a method of uncertainty estimation must be implemented. This paper proposes a method of estimation of the overall uncertainty based on a consolidated metrological procedure. This method holds a certain degree of generality because it can be applied to different kinds of architecture of the synthetic sensor. In this paper, it has been applied to the preliminary design of the synthetic sensor of the MIDAS air data system and the results have been reported as example.

Heterogeneity of the small aircraft category (e.g., small air transport (SAT), urban air mobility (UAM), unmanned aircraft system (UAS)), modern avionic solution (e.g., fly-by-wire (FBW)) and reduced aircraft (A/C) size require more compact, integrated, digital and modular air data system (ADS) able to measure data from the external environment. The MIDAS project, funded in the frame of the Clean Sky 2 program, aims to satisfy those recent requirements with an ADS certified for commercial applications. The main pillar lays on a smart fusion between COTS solutions and analytical sensors (patented technology) for the identification of the aerodynamic angles. The identification involves both flight dynamic relationships and data-driven state observer(s) based on neural techniques, which are deterministic once the training is completed. As this project will bring analytical sensors on board of civil aircraft as part of a redundant system for the very first time, design activities documented in this work have a particular focus on airworthiness certification aspects. At this maturity level, simulated data are used, real flight test data will be used in the next stages. Data collection is described both for the training and test aspects. Training maneuvers are defined aiming to excite all dynamic modes, whereas test maneuvers are collected aiming to validate results independently from the training set and all autopilot configurations. Results demonstrate that an alternate solution is possible enabling significant savings in terms of computational effort and lines of codes but they show, at the same time, that a better training strategy may be beneficial to cope with the new neural network architecture.

A simplex (not redundant) Air Data System (ADS) is adopted on air vehicles to measure a set of quantities from the external environment. Generally speaking, a simplex ADS is made up of external (i.e. installed externally on the A/C fuselage) probes and vanes (LRUs) able to measure a full set of air data

The application of virtual sensor is widely discussed in literature as a cost effective solution compared to classical physical architectures. RAMS (Reliability, Availability, Maintainability and Safety) performance of the entire avionic system seem to be greatly improved using analytical redundancy. However, commercial applications are still uncommon. A complete analysis of the behavior of these models must be conducted before implementing them as an effective alternative for aircraft sensors. In this paper, a virtual sensor based on neural network called Smart-ADAHRS (Smart Air Data, Attitude and Heading Reference System) is analyzed through simulation. The model simulates realistic input signals of typical inertial and air data MEMS (Micro Electro-Mechanical Systems) sensors. A procedure to define the background noise model is applied and two different cases are shown. The first considers only the sensor noise whereas the latter uses the same procedure with the operative flight noise. Flight tests have been conducted to measure the disturbances on the inertial and air data sensors. Comparison of the Power Spectral Density function is carried out between operative and background noise. A model for GNSS (Global Navigation Satellite System) receiver, complete with constellation simulator and atmospheric delay evaluation, is also implemented. Eventually, a simple multi-sensor data fusion technique is modeled. Results show good robustness of the Smart-ADAHRS to the sensor faults and a marginal sensitivity to the temperature-related faults. Solution for this kind of degradation is indicated at the end of the paper. Influences of noise on input signals is also discussed.

The development of a reliable Sense And Avoid (SAA) system is one of the limiting aspects for the integration into civil airspace of unmanned aerial vehicles, for which the market demand is becoming viral in many fields. To overcome these limitations, it is required that the SAA system perform equally or even better than the human eye. This can be achieved integrating information from different sensors using data fusion algorithms, like Bayesian estimators or neural network techniques. SAA system degradation could arise from both single sensor shortcomings and bad numerical behaviours, injected by the specific fusion algorithm, such as real machine round-off errors or divergences introduced by approximating strongly nonlinear functions. An alternative formulation of the Square-Root unscented Kalman filter (SRUKF) based on the Joseph form of the state covariance update step, is used in order to avoid numerical instabilities induced by ill-conditioned matrix problems. The novelty of this technique lies in the exploitation of the Sigma-Point Kalman filters, which ensure a higher-order accuracy in the nonlinear inference problem solving, and in the application of the Joseph update equation, which improves numerical robustness. Moreover, this approach prevents the algorithm from failing, avoiding the downdating process of the Cholesky factor when the SRUKF is used and to take advantage by the higher numerical stability assured by a lower matrix condition number.

Unmanned Aerial Vehicles (UAV) design may involve issues on redundancy of the systems due to restricted available space and allowable weight. Virtual sensors offer great advantages from this point of view and several research projects carry out more or less complicated solutions in order to estimate a signal without applying a physical sensor. This approach brings to a reduction of the overall cost and to improve the Reliability, Availability, Maintainability and Safety (RAMS) performance. The patented technology named Smart-ADAHRS (Smart — Attitude and Heading Reference System) is a powerful technique presented during previous research for estimation of the aerodynamic angles. This algorithm is based on Artificial Neural Network (ANN) and receive inputs from on-board sensors only. Whereas previous studies considered also the signals coming from the Flight Control System (FCS), this work presents the important simplification of not considering them in the input vector. This paper resumes the previous results obtained in simulated environment with former neural network-based estimators. Then, a comparison of the results obtained by the new estimator, applying the reduced input vector in different environments, is carried out. Moreover, it re-discusses accuracy by means of a new test case that consider simulated realistic faults and noise. Eventually, a first analysis around performance in operative environment is conducted using data obtained from flight test campaigns. Results show how accuracy is preserved both in realistic situation and critical circumstances.