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Abstract The Advanced Receiver Autonomous Integrity Monitoring (ARAIM) provides an Aircraft‐Based Augmentation System function for aircraft Global Navigation Satellite System equipment. Currently, the consideration of temporal correlations of test statistics is an important update to the ARAIM baseline algorithm. However, due to the tight budgets of integrity and continuity, the updated ARAIM Fault Detection and Exclusion (FDE) is not satisfactory in availability coverage. The baseline algorithm is based on equal allocation of integrity and continuity, which causes ARAIM to be susceptible to faults and satellite outages. An availability optimisation of ARAIM FDE based on dynamic budget allocation is proposed, aiming to make full use of the prior knowledge provided by the Integrity Support Message (ISM). The contribution of each solution separation test in the ARAIM FDE to the total continuity and integrity risks is estimated, and the continuity and integrity allocation constraints are obtained. The impacts of faults and satellite outages on availability are analysed, and a predictive Protection Level (PL) for ARAIM FDE is proposed for use as an objective function for optimising risk allocation. Then, a risk allocation method based on the multiplier penalty function method is proposed. The performances of the baseline algorithm and the proposed method under the maximum Number of Effective Samples are simulated. For dual‐constellation H‐ARAIM, the optimised Horizontal PL (HPL) is 40% lower than that of the baseline algorithm on average, and the average number of critical satellites supporting RNP 0.3 is less than 1 worldwide. For the three‐constellation V‐ARIAM, the optimised HPL is 27% lower than that of the baseline algorithm on average, and the optimised Vertical PL (VPL) is 18% lower. The proposed method has high availability coverage and can better respond to the requirements for high‐performance navigation services.

Abstract A main challenge within inertial navigation systems involves attitude determination, which refers to the process of estimating the angles that define orientations. A new algorithm is presented to estimate a reliable and proper model for a low‐cost attitude and heading reference system (AHRS), while also, including long‐term global navigation satellite system (GNSS) outages with various dynamical manoeuvres. In the proposed approach, the initial AHRS model is continuously refined at each time step through the incorporation of complementary terms using a new prediction method. These complementary terms are determined using attitude information from the accelerometers output and the GNSS as reference systems. The proposed estimation technique undergoes mathematical analysis to ascertain stochastic stability and is further assessed through real‐world aerial experimental tests conducted with hardware‐in‐the‐loop mechanisation. The obtained results demonstrate a significant enhancement in the reliability and accuracy of the AHRS through the proposed estimation algorithm, even under GNSS blockages. The comparative results highlight the higher performance of the suggested data fusion scheme in providing a reliable and accurate model for the AHRS in normal conditions and assisting a powerful neural network‐based algorithm to provide reliable heading information even during long‐term GNSS outages under dynamical manoeuvres.

Abstract The authors propose an innovative solution to address challenges in terrain‐referenced navigation (TRN). The suggested solution is the interactive multiple‐model particle filter with a classification error minimisation strategy (IMM‐CPF) based on decision theory. TRN is a technique that estimates position by comparing measured terrain altitude to the digital elevation model and critically depends on obtaining accurate altitude measurements. However, these measurements can be easily contaminated to not only from sensor errors but also from vegetation effects. The TRN measurement noise model is characterised as a multi‐modal density, and it reveals an overlap between two density functions, with the mixture weight parameter varying based on surface environmental conditions. This variability can potentially degrade estimation accuracy. The proposed approach integrates truncated likelihoods into the mode estimation process to enhance mode estimation capability using a classification error minimisation strategy. The proposed strategy is based on decision theory and has been modified to be suited in the IMMPF form. The effectiveness of the proposed IMM‐CPF method is verified through simulations conducted under diverse surface conditions, demonstrating significant improvements in estimation accuracy compared to conventional algorithms. Furthermore, the significance of this method is presented in terms of computational cost and robustness.

Abstract An integrity monitoring of integrated navigation in the presence of slowly growing errors (SGEs) is proposed. SGEs with gradual growth over time are the worst category of latent failures. Tightly coupled inertial navigation system (INS)/Global Navigation Satellite System integration is presented by taking INS errors, bias and drift of receiver clock, baro‐altitude, pseudo‐range errors and ionospheric errors into consideration. Based on the RTCA performance standards DO‐229 and DO‐384, the simplified multi‐filters solution separation is employed for fault detection and calculation of corresponding outputs. For the fault exclusion part, the authors introduce a post pseudo‐range residual and measurement averaging method that is designed to quickly and effectively address SGEs and large measurement noises. Furthermore, the authors have designed a reconstruction logic for the filters that guarantees the continuity of the civil aviation system by considering the variation of visible satellites and historical information. The simulation results demonstrate that the proposed approach satisfies the performance requirements for civil aviation of En‐route and is able to correctly exclude faults and respond quickly in the presence of different levels of SGEs and measurement noises.

The under-canopy environment, which is inherently inaccessible to humans, necessitates the use of unmanned aerial vehicles (UAVs) for data collection. The implementation of UAV autonomous navigation in such environments faces challenges, including dense obstacles, GNSS signal interference, and varying lighting conditions. This paper introduces a UAV autonomous navigation method specifically designed for under-canopy environments. Initially, image enhancement techniques are integrated with neural network-based visual feature extraction. Subsequently, employs a high-dimensional error-state optimizer coupled with a low-dimensional height filter to achieve high-precision localization of the UAV in under-canopy environments. Furthermore, proposes a boundary sampling autonomous exploration algorithm and an advanced Rapidly exploring Random Tree (RRT) path planning algorithm. The objective is to enhance the reliability and safety of UAV operations beneath the forest canopy, thereby establishing a technical foundation for surveying vertically stratified natural resources.

The present work describes the development of a graphical environment to represent typical flight instruments on a computer screen. The instruments’ behavior is displayed according to information regarding the aircraft’s flight conditions. Some of the instruments represented in this work, such as the altimeter, the vertical speed indicator, the aircraft speed indicator, and the Mach indicator, use air pressure principles. The algorithms and routines developed for the screen display are created using the C++ programming language and compiled independently to be included as libraries to improve the software performance. The algorithms developed for this purpose also include the corresponding relationship between the physical variables, such as the speed and displacement, and the standard atmosphere to provide an equivalent value. These algorithms are successfully tested using data information to simulate three hypothetical flights, which are divided into maneuvers with the aircraft in a stopped position, running on the ground, taking off and flying away, including some changes in directions. Moreover, the routines for displaying the aircraft path with the instruments’ animation are also successfully tested by comparison. Finally, an approach analysis as a function of the step time (Δt) used for calculation of the aircraft displacement to evaluate the efficiency of the numerical method integrated in the simulator is also described. It is proved that the aircraft instrument representation is appropriate according to the input data of the analyzed flights, and an improvement in the calculation can be easily obtained, making it possible to represent any flight condition on the aircraft instruments.

Guiding and controlling aircraft within an airport is a decision-making process based on safety and efficiency in a highly dynamic and stochastic environment. Currently, many airports rely on manual monitoring and command to provide appropriate taxiing paths for aircraft. With the increasing complexity of airport structures and flight volumes, there is a need for an algorithm that can autonomously search for the shortest taxiing paths while satisfying the specific taxiing regulations and maintaining safe separations between aircraft in a dynamic scenario. We propose an improved approach based on the Q-Learning algorithm, a reinforcement learning method, to provide taxiing path guidance for aircraft. The Q-Learning algorithm exhibits adaptability in dynamic and stochastic environments. However, the traditional Q-Learning algorithm lacks the iteration stability and computational efficiency required in high-dynamic scenarios, and the shortest paths found often fail to meet the requirements due to the specific regulations of airport control. We first make three improvements to the Q-Learning algorithm to address these challenges. These improvements include optimizing Q-table exploration, resetting initial Q-table values, and introducing a dynamic exploration factor to enhance the algorithm’s computational efficiency and accuracy. We also incorporate conflict avoidance strategies related to civil aviation regulations to ensure that the final path adheres to airport control regulations. Finally, we validate the fused and improved algorithm in a gridded airport environment model. Compared to traditional methods, the results demonstrate that the improved algorithm provides more efficient taxiing guidance for aircraft while ensuring operational safety. Furthermore, the algorithm strategically avoids conflicts with other moving aircraft, thereby increasing the utilization of airport taxiing resources.

A commonly used definition of spatial disorientation (SD) in aviation is “an erroneous sense of one’s position and motion relative to the plane of the earth’s surface”. There exists a wide range of SD use-cases dictated by situational factors, therefore SD has been predominantly studied using reduced motion detection experimental contexts in isolation. The study of SD by use-case makes it difficult to understand general SD occurrence and thus provide viable solutions. To investigate SD in a generalized manner, a two-part Human Activity Recognition (HAR) study was performed. In Part I, a generalized SD perception dataset was created using whole-body experimental motion detection methods in a naturalistic flight context; joystick response was measured during rotational or translational vestibular stimulation. Results showed that SD occurred less for faster speeds than slower speeds, and specific orientations and axes were more difficult to detect motion. Part II evaluated supervised and unsupervised model parameters, including: model architecture, data use-case, feature-type, feature quantity, ground-truth labeling, unsupervised labeling. Long-Short Term Memory (LSTM), Random Forest (RF), and Transformer Encoder models most accurately predicted SD with mean accuracy of 0.84, 0.82, and 0.77 respectively. Using permutation importance (PIM), a dependency score for time, frequency, and time & frequency feature-types quantified the amount that each model architecture depended on a feature-type. The lenient ground-truth label best characterized features, and K-medoids clustering using position and velocity features most accurately replicated ground-truth labels.

Abstract The existing terrain aided navigation (TAN) has the disadvantage that the navigation performance is limited by the size of matching area and terrain features. Therefore, this study proposes a method of optimal selection and adaptability analysis of matching area for TAN. According to the heading angle of the carrier, inertial navigation system positioning error and performance index, the optimal matching area is selected in real time. First, the matching area selection and adaptability analysis are carried out, and the influence of the matching area on the positioning performance is analysed by using the Beijing Internal TAN II algorithm. Then, the model parameters of the optimal matching area are determined through theoretical calculation and simulation. Finally, it is compared with the typical matching area selecting method. The test results show that the optimal selection and adaptability analysis method can improve the computational efficiency of TAN system, and improve the positioning accuracy of TAN system.

Scene matching plays a vital role in the visual positioning of aircraft. The position and orientation of aircraft can be determined by comparing acquired real-time imagery with reference imagery. To enhance precise scene matching during flight, it is imperative to conduct a comprehensive analysis of the reference imagery’s matchability beforehand. Conventional approaches to image matchability analysis rely heavily on features that are manually designed. However, these features are inadequate in terms of comprehensiveness, efficiency, and taking into account the scene matching process, ultimately leading to unsatisfactory results. This paper innovatively proposes a core approach to quantifying matchability by utilizing scene information from imagery. The first proposal for generating image matchability samples through a simulation of the matching process has been developed. The RSPNet network architecture is designed to effectively leverage regional scene perception in order to accurately predict the matchability of reference imagery. This network comprises two core modules: saliency analysis and uniqueness analysis. The attention mechanism employed by saliency analysis module extracts features at different levels and scales, guaranteeing an accurate and meticulous quantification of image saliency. The uniqueness analysis module quantifies image uniqueness by comparing neighborhood scene features. The proposed method is compared with traditional and deep learning methods for experiments based on simulated datasets, respectively. The results demonstrate that RSPNet exhibits significant advantages in terms of accuracy and reliability.

Abstract The false alarm of a fault detection module in aircraft will interfere with flight. If there is a time‐offset between the inertial navigation system (INS) and air data system (ADS), the probability of a false alarm (PFA) of the fault detection module will increase, which is ignored in existing approaches. To address the problem, a fault detection approach is proposed applied to INS/ADS with a time‐offset. An INS/ADS fault detection model based on kinematic equations is developed, and we combine an unscented Kalman filter (UKF) with Runge‐Kutta to deal with the non‐linear and discretisation problem. A time‐offset estimator is designed and the observability of time‐offset is analysed, showing that time‐offset is observable only in the manoeuvre phase. A fault detection architecture applied to INS/ADS with a time‐offset is designed, which solves the problem of the high PFA of INS/ADS fault detection under a time‐offset. Simulation results show that under multiple time‐offset scenarios, the root mean square error of the proposed approach can reach 0.0134 s minimally, and after time alignment, the PFA of the fault detection can be reduced to 1.8%.

As the atmospheric pollution becomes an increasingly serious problem, finding accurately the location of pollutant sources is still challenging. In the present work, a probability-based tracking strategy is proposed for guiding two cooperative unmanned aerial vehicles (UAVs) within a quest area to find an atmospheric pollutant source. This tracking strategy implies deploying algorithmically two phases: exploration and exploitation. During the exploration phase each vehicle follows a trajectory based on plane coordinates generated from a Hammersley sequence. The overlapping between UAVs’ trajectories is avoided by splitting guidance points into two groups by using the k-means algorithm. The navigation trajectories are smoothed by an TSP solver and a cubic spline planning algorithm. The exploitation phase redirects the search to specific locations where the probability of finding the source is higher. This is achieved by considering the quest area as a mesh, where each cell is assigned a probability computed with information collected by the UAVs measurement system. Every time a high concentration is found, the probabilities are recalculated, and flight trajectories are adjusted. The trajectories are semicircular, and the radius is decreased when a new high concentration is found. Simulation data of the proposed tracking strategy shows promising results on the accuracy achieved in the finding of the pollutant source, in comparison with three other tracking strategies: leader-follower, random walk with particle swarm optimization, and a hill climb traceability algorithm.

This paper proposes a new high-accuracy calculation method to estimate collision probability to ground obstacles during Instrument Landing System (ILS) approach. The current collision risk model (CRM) used to evaluate the ground obstacle collision probability was developed in the 1970s. In this CRM, a shadowing technique is used to avoid over-estimation of the risk when there are multiple obstacles located close to each other. However, this shadowing still overestimates the risk due to several conservative assumptions. This paper proposes mathematical modifications to the calculation of shadowing to address the risk overestimation. To validate the proposed method, 106 sets of aircraft trajectories are generated so that the actual risk can be estimated by counting the number of collisions. The result shows that the risk calculated by the proposed method is much closer to the actual risk compared to the one calculated by the current CRM. The proposed model will help introduce ILS approach at obstacles-sensitive airports.

Trajectories optimisation is a major research topic in air transport and air traffic management, due to its profound impact both on passengers, airlines and the environment in general, and consequently on the perceived value and cost of air transportation. While the challenges associated to the optimisation of the en-route part of a flight are well understood, relative less attention has been devoted to the last part, i.e. the approach and landing. Here we show how open large-scale data sets of aircraft trajectories can be used to characterise the efficiency of flights landing at an airport, measured through the time and distance flown below 10,000 feet. The yielded picture is highly heterogeneous, with the time spent at low altitude varying from an average of 10 minutes for Zurich, up to 16 minutes for London Heathrow. Flights arriving at the same airport also experience highly different times, e.g. from 12 to 20 minutes for London Heathrow, depending on factors like traffic volumes, time of the year and of the day, and on interactions with other traffic patterns and airports. From a more general perspective, this contribution illustrates how the availability of large data sets can be used to improve our understanding of the real behaviour of the system, and especially its deviation from what planned.

This paper introduces a technique to transform between geometric and barometric estimates of altitude and vice-versa. Leveraging forecast numerical weather models, the method is unbiased and has a vertical error with a standard deviation of around 30 m (100 ft), regardless of aircraft altitude, which makes it significantly more precise than established comparable conversion functions. This result may find application in various domains of civil aviation, including vertical RNP, systemized airspace, and automatic landing systems.

We need a coordinated control method to continuously track a moving target using a group of UAVs. In this paper, we study the predicted reference point guidance method, where the target is considered to move with a constant velocity in a very short time window and the trajectories of the UAVs are designed as several tiny arcs around the target. The control law of the UAV is divided into roll angle control and velocity control. Simulations are used to verify that the proposed control laws have smaller standoff distances and phase angle control errors than the Lyapunov vector field guidance and model-based predictive control, and the wind is considered in the simulation, too. Therefore, we show that our proposed method has higher steady state accuracy among existing techniques.

This paper presents an innovative method of locating airplanes, which uses only voice communication between an air traffic controller and the pilot of an aircraft. The proposed method is described in detail along with its practical implementation in the form of a technology demonstrator (proof of concept), included in the voice communication system (VCS). A complete analysis of the performance of the developed method is presented, including the results of simulation and measurement tests in real conditions. The obtained results are very optimistic and indicate that the proposed solution may constitute an alternative method of locating aircraft in emergency conditions, i.e., a backup solution in the case of failure of other positioning systems.

In this paper, solutions for precise maneuvering of an autonomous small (e.g., 350-class) Unmanned Aerial Vehicles (UAVs) are designed and implemented from smart modifications of non expensive mass market technologies. The considered class of vehicles suffers from light load, and, therefore, only a limited amount of sensors and computing devices can be installed on-board. Then, to make the prototype capable of moving autonomously along a fixed trajectory, a “cyber-pilot”, able on demand to replace the human operator, has been implemented on an embedded control board. This cyber-pilot overrides the commands thanks to a custom hardware signal mixer. The drone is able to localize itself in the environment without ground assistance by using a camera possibly mounted on a 3 Degrees Of Freedom (DOF) gimbal suspension. A computer vision system elaborates the video stream pointing out land markers with known absolute position and orientation. This information is fused with accelerations from a 6-DOF Inertial Measurement Unit (IMU) to generate a “virtual sensor” which provides refined estimates of the pose, the absolute position, the speed and the angular velocities of the drone. Due to the importance of this sensor, several fusion strategies have been investigated. The resulting data are, finally, fed to a control algorithm featuring a number of uncoupled digital PID controllers which work to bring to zero the displacement from the desired trajectory.

This paper presents the sharing analysis of the 694–790 MHz frequency band for Mobile services IMT and Aeronautical radio-navigation services (ARNS) using SEAMCAT (established by CEPT) software based on the statistical simulation (Monte-Carlo) method. In 2012 the World Radiocommunication Conference (WRC-12) decided to allocate the 694–790 MHz frequency band (the so-called 700 MHz band) to mobile services IMT (excluding aeronautical mobile) after WRC-15 conference. But this agreement raises electromagnetic compatibility problems, which should be solved until WRC-15 [1]. This study was carried out in two phases: first applying theoretical analysis, then statistical Monte-Carlo simulations with SEAMCAT software tool in order to verify results obtained in theoretical approach. Analytical calculations shows that the required protection distances between ARNS stations and the MS base stations are 132 km. The obtained results from SEAMCAT simulations indicate that separation distance should be above 100 km. These results illustrate that the systems are not electromagnetically compatible. The possible mitigation technic could be antenna pattern correction.