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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. [ABSTRACT FROM AUTHOR]

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.

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 FROM AUTHOR]

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 FROM AUTHOR]

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.

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 FROM AUTHOR]

This article introduces a comprehensive Mode-S radar simulation, designed to evaluate interrogation and reply signal functionality in various uplink formats (UFs) and downlink formats (DFs). Mode-S radar, crucial in modern aviation, facilitates communication between aircraft and ground systems, thereby enhancing aircraft tracking, situational awareness, and air traffic safety. Our simulation focuses on optimizing interrogation and reply sequences in the 1030- and 1090-MHz channels, respectively. It provides a practical solution for testing complex air flight scenarios, which may be impractical or costly in real life, by replicating challenging conditions in a computerized environment. This approach aids in developing advanced radar signaling algorithms for air traffic control. The simulator’s user-friendly interface displays real-time aircraft data, demonstrating its realworld applicability. Its successful operation underscores its potential to advance Mode-S radar technology and its value for research in the field. [ABSTRACT FROM AUTHOR]

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.

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. [ABSTRACT FROM AUTHOR]

In laser-based space technologies where laser beams are transmitted from a ground station, it is required for the ground station to avoid irradiating aircraft with high energy lasers. A simple, inexpensive acoustic positioning technique using four microphones is developed and its performance is evaluated. It is demonstrated to be sensitive to and effective in detecting aircraft flying at short ranges. Aircraft closer than 5 km can easily be detected and the maximum range is about 10 km. The time series of two-dimensional directional position estimates are harmonic with those of ADS-B (Automatic Dependent Surveillance-Broadcast), and the precision reaches 1 degree (standard deviation) in the best case. The processing time for data collection and computation is short enough to establish a realtime feedback mechanism to stop firing laser. With its advantages and disadvantages, effective combinations with existing methods are discussed to avoid injury to the eyes, in which the acoustic approach is found to be useful to enhance safety measures in particular of low-altitude aircraft with a large angular velocity. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

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.

This article presents the first demonstration of navigation with cellular signals of opportunity (SOPs) on a high-altitude aircraft. An extensive flight campaign was conducted by the Autonomous Systems Perception, Intelligence, and Navigation Laboratory in collaboration with the U.S. Air Force to sample ambient downlink cellular SOPs in different regions in Southern California, USA. Carrier phase measurements were produced from these signals, which were subsequently fused in an extended Kalman filter along with altimeter measurements to estimate the aircraft’s state (position, velocity, and time). Three flights are performed in three different regions: 1) rural, 2) semiurban, and 3) urban. A multitude of flight trajectories and altitudes above ground level (AGL) was exercised in the three flights: 1) a 51-km trajectory of grid maneuvers with banking and straight segments at about 5,000 ft AGL, 2) a 57-km trajectory of a teardrop descent from 7,000 ft AGL down to touchdown at the runway, and 3) a 55-km trajectory of a holding pattern at about 15,000 ft AGL. The estimated aircraft trajectory is computed for each flight and compared with the trajectory from the aircraft’s onboard navigation system, which utilized a GPS receiver coupled with an inertial navigation system and an altimeter. The cellular SOPs produced remarkable sustained navigation accuracy over the entire flight trajectories in all three flights, achieving a 3D position root mean-squared error of 10.53 m, 4.96 m, and 15.44 m, respectively. [ABSTRACT FROM AUTHOR]

Unknown closed spaces are a big challenge for the navigation of robots since there are no global and pre-defined positioning options in the area. One of the simplest and most efficient algorithms, the artificial potential field algorithm (APF), may provide real-time navigation in those places but fall into local minimum in some cases. To overcome this problem and to present alternative escape routes for a robot, possible crossing points in buildings may be detected by using object detection and included in the path planning algorithm. This study utilized a proposed sensor fusion method and an improved object classification method for detecting windows, doors, and stairs in buildings and these objects were classified as valid or invalid for the path planning algorithm. The performance of the approach was evaluated in a simulated environment with a quadrotor that was equipped with camera and laser imaging detection and ranging (LIDAR) sensors to navigate through an unknown closed space and reach a desired goal point. Inclusion of crossing points allows the robot to escape from areas where it is congested. The navigation of the robot has been tested in different scenarios based on the proposed path planning algorithm and compared with other improved APF methods. The results showed that the improved APF methods and the methods reinforced with other path planning algorithms were similar in performance with the proposed method for the same goals in the same room. For the goals outside the current room, traditional APF methods were quite unsuccessful in reaching the goals. Even though improved methods were able to reach some outside targets, the proposed method gave approximately 17% better results than the most successful example in achieving targets outside the current room. The proposed method can also work in real-time to discover a building and navigate between rooms. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Advanced air mobility (AAM) is an emerging industry focus as well as a research and development discipline. Innovations and technologies resulting from AAM will change the way that we move cargo and people in and around cities. Industry is moving fast with excitement to deploy AAM solutions. However, there are multiple technical challenges that need to be overcome before AAM becomes a reality. This article takes a closer look at the technology readiness level of AAM solutions in the area of communications, navigation, and surveillance (CNS) and identifies open research problems as well as directions to address them. In particular, we discuss current approaches and future research challenges in air corridor design, air-to-air (AA) communications, 3rd Generation Partnership Project (3GPP) support for navigation, and detect and avoid (DAA)/collision avoidance, among other areas, for supporting future AAM operations. [ABSTRACT FROM AUTHOR]

Approaching landing is the most difficult and complicated operation in the entire flight process of the aircraft, and it is also the stage where frequent flight accidents occur. According to the statistics released by International Civil Aviation Organization (ICAO), 49% of flight accidents occurred in the stage of approaching landing. The Instrument Landing System (ILS), commonly known as the blind landing system, is the main navigation equipment for guiding the aircraft's precision approach and landing in the terminal area of the airport. Therefore, the uninterrupted normal operation of the ILS is the key to ensuring the safety of aircraft landing. [ABSTRACT FROM AUTHOR]

The transport sector is experiencing a fast and revolutionary development, moving toward the deployment of autonomous vehicles. Thus, the accuracy of the position information and the integrity of the navigation system have become key factors. In this article, we address the problem of the enhancement of the integrity of the position provided by a global navigation satellite system receiver by exploiting sensor fusion. To this aim, we estimate the lateral offset and heading of the vehicle with respect to a georeferenced roadway centerline from the images supplied by an on-board camera. Moreover, we perform integrity monitoring based on the implementation of the solution separation in the parity space. The numerical results indicate that the use of sensor fusion and digital map allows us to attain a longitudinal protection level reduction with respect to the case in which sensor fusion is not exploited. More specifically, a decrease of about 70% is achieved when a single constellation is used, while reduction is less relevant, about 15%, when two constellations are employed. [ABSTRACT FROM AUTHOR]

Opportunistic navigation with cellular signals is studied and demonstrated on a high-altitude aircraft. An extensive campaign was conducted by the Autonomous Systems Perception, Intelligence, and Navigation Laboratory (ASPIN) in collaboration with the U.S. Air Force (USAF) to sample ambient cellular signals of opportunity (SOPs) at different altitudes in different regions in Southern California, USA. The carrier-to-noise ratio is characterized as a function of altitude and horizontal distance. It was found that signals from cellular transmitters can be reliably acquired and tracked for navigation purposes by receivers mounted on high dynamics aircraft flying at altitudes as high as 23,000 ft above ground level and horizontal distances as far as 100 km. The multipath channel is also characterized as a function of altitude, showing clean channels with a dominant line-of-sight component for altitudes up to 23,000 ft. To further assess the potential of cellular SOPs for aircraft navigation, a 51-km trajectory traversed over a period of 9 minutes was estimated exclusively using cellular SOPs (i.e., with no GPS or other sensors, except for barometric altimeter measurements), resulting in a three-dimensional position (3-D) 10.5-m position root-mean-squared error. [ABSTRACT FROM AUTHOR]

An innovative electro-optical navigation system called Optical Navigation System (OptoNav) for Autonomous Landing consisting of a machine vision camera, and a set of co-registered structured light beacons is proposed. OptoNav is used to directly sense the altitude and two orientation states of an air vehicle with respect to the flight path direction. Motivated by autonomous landing applications involving fixed-wing aircraft, the machine vision camera is used to process the images of the runway so as to establish a moving coordinate system affixed to the runway, making use of the perspective geometry of the camera system. Three or more structured light emitters are used to make direct measurements of the range using the geometry of the perspective projection model by making use of the co-registration of the camera with respect to the structured light emitting sources. A unique formulation of the minimal relative motion states is used to directly estimate the three states of altitude, pitch and roll angle of the airframe with respect to the moving coordinates affixed to the runway. Laboratory experiments are used to validate the utility of the OptoNav sensor system. [ABSTRACT FROM AUTHOR]

Vehicle and airborne navigation users are facing more and more complex signal interference and even spoofing. If vehicles and aircraft do not strengthen their anti-spoofing ability, their navigation performance is bound to be adversely affected. global navigation satellite system (GNSS) spoofing technology has gradually become a preferred interference method for spoofer because of its high concealment and great harm. For spoofer, user terminal increasingly adopts GNSS with inertial measurement unit (IMU): tightly-coupled GNSS/IMU, on this basis, user also configures a variety of anti-spoofing techniques to effectively deal with spoofing. Even if spoofer slowly changes user's positioning, if spoofing strategy is unreasonable, it will lead to the violation of parameter rationality of coupled filter output parameters and spoofing observation, which greatly increases the difficulty of spoofing. In view of the above problems, from the perspective of spoofer, in order to effectively counter the non cooperative target of assembling tightly-coupled GNSS/IMU by using GNSS spoofing, this paper establishes GNSS spoofing mathematical model, and proposes a slowly varying spoofing algorithm to avoid tightly-coupled GNSS/IMU with multiple anti-spoofing techniques based on the analysis of the influence mechanism of spoofing on the positioning of tightly-coupled GNSS/IMU, the algorithm proposes a measurement deviation determination method to avoid a variety of anti-spoofing techniques, which can gradually pull the positioning results of coupled system, and successfully avoid anti-spoofing techniques detection of least squares residual receiver autonomous integrity monitoring (RAIM) and parameter rationality check. The experimental results show that the algorithm can gradually change positioning of tightly-coupled GNSS/IMU within 30 s, and the north, east and down displacements basically achieve the spoofing effect, the errors with the expected offset are −0.5 m, 1.9 m and 12.7 m respectively. At the same time, the detection of the above anti-spoofing techniques is avoided. The mean value of test statistics for tightly-coupled system is reduced by 75.4% and does not exceed the alarm threshold, so as to achieve the purpose of spoofing, the effectiveness and high concealment of the spoofing algorithm are proved. [ABSTRACT FROM AUTHOR]

The Stretched-FrOnt-Leg (SFOL) pulse is a high-accuracy distance measuring equipment (DME) pulse developed to support alternative positioning and navigation for aircraft during global navigation satellite system outages. To facilitate the use of the SFOL pulse, it is best to use legacy DMEs that are already deployed to transmit the SFOL pulse, rather than the current Gaussian pulse, through software changes only. When attempting to transmit the SFOL pulse in legacy DMEs, the greatest challenge is the pulse shape distortion caused by the pulse-shaping circuits and power amplifiers (PAs) in the transmission unit such that the original SFOL pulse shape is no longer preserved. This letter proposes an inverse-learning-based DME digital predistortion method and presents successfully transmitted SFOL pulses from a testbed based on a commercial legacy DME that was designed to transmit Gaussian pulses. [ABSTRACT FROM AUTHOR]

For INS/CNS integrated navigation system, the performance of data fusion algorithms based on the Kalman filter is seriously degraded when the subsystems have discrepant sampling-frequency. Therefore, a fusion algorithm based on locally weighted linear regression (LWLR), long short-term memory (LSTM), and cubature Kalman filter (CKF) is proposed. First, we construct a deeper coupled INS/CNS system model based on the low-frequency error of the star sensor. Then, during the CNS data sampling interval, LWLR is employed to fit the CNS data, and CKF is used to fuse the fitted CNS data with INS data. And the estimated error calculated using CKF is optimized by LSTM. Finally, the experimental results show that the proposed algorithm suppresses the divergence problem caused by different sampling-frequency and improves the attitude estimation accuracy of the navigation system. [ABSTRACT FROM AUTHOR]

Aircraft attitude has always been an important technical parameter during flights, especially for stages such as aircraft take-off and landing. This article presents a monocular off-board vision system for relative attitude measurement of fixed-wing aircraft. In this system, a zoom imaging subsystem is set up to track and shoot the aircraft over a large distance range. Image sequences and corresponding servo data are transmitted to the processing computer for vision algorithms. A model-based attitude tracking algorithm, which requires the computer-aided design model of the aircraft, is performed to measure the relative attitude of the aircraft with respect to the reference platform. A 2-D visual tracking algorithm and a target detection algorithm are also employed to control the vision system. The proposed attitude tracking algorithm achieves competitive results on a publicly available dataset. The full system is validated in a real-flight experiment. The results revealed that the proposed system can run in real time on 1920×1080@40 Hz videos with an accuracy better than 0.5 $^\circ$. [ABSTRACT FROM AUTHOR]

In a pursuit evasion scenario, a missile (pursuer) launched from the ground or the air aims to intercept an aircraft (evader) flying in formation. This article considers the problem of whether an aircraft is aimed by a missile or not, based only on the line-of-sight (LOS) measurements from an on-board passive sensor. The motion of the missile is assumed to be governed by pure proportional navigation guidance. Previous works on this problem estimate the missile's state motion parameters, which, for a passive sensor, requires a numerical search, i.e., significant computation time, algorithm complexity and is ill-conditioned. We present a methodology that relies on the geometric relationship between the aircraft and the missile without state estimation. A plane is defined by the aircraft velocity vector and one aircraft-to-missile LOS vector, and if a second LOS vector at a subsequent time is in this plane, it is known that the missile aims at the aircraft. A coplanarity test is designed on the basis of these three vectors. We then provide a test statistic for inferring whether the airplane is aimed by the missile. An approximate distribution is used to set the threshold for detection and false alarm probabilities. Simulation results are presented for surface-to-air and air-to-air missile scenarios to illustrate the efficiency of the proposed method and compared to the state estimation method. [ABSTRACT FROM AUTHOR]

To explore the airspace structure and safety performance of unmanned aerial vehicle (UAV) system based on spatial digital twins (DTs), the study introduces DTs technology, and combines convolutional neural network (CNN) algorithm with UAV autonomous network. The DTs system of UAV is constructed by using wireless communication technology, and its security performance is simulated. The results show that in the analysis of the system packet loss rate, it is found that with the increase of the acquisition points, the amount of transmitted data only increases slightly, but the packet loss rate does not change significantly. In the analysis of the network performance of the unmanned aircraft system, it is found that the node energy-based weighted clustering algorithm (EWCA) can be used to increase the life of the overall network and enhance its availability by rationally controlling the number of nodes and the number of switching between clusters. As the number of nodes increases, the minimum survival time of each clustering algorithm decreases linearly. When the number of nodes is less than 600, the growth rate of cluster head is higher; When the number of nodes is more than 600, the curve growth is relatively smooth. In the analysis of the probability of network safety interruption, it is found that using the model constructed, when the energy acquisition coefficient is close to 0.5, the energy conversion efficiency is higher, the signal-to-noise ratio is larger. Also, when the number of intermediate nodes is increased to 10, the UAV has the best network safety performance. Therefore, through the research, it is found that the UAV DTs system constructed can significantly improve the safety performance of the UAV during its airspace flight. It can provide experimental references for the widespread application of the UAV in the later period. [ABSTRACT FROM AUTHOR]

The noise and drift requirements for a navigation-grade gyroscope are widely known, yet there is no simple analytic model of how the noise and drift of a gyroscope influence the fix displacement error (FDE) of an inertial navigation system (INS). This work derives simple analytical expressions for the cross-track and along-track errors of an aircraft whose INS consists solely of a three-axis gyroscope system with perfect knowledge of the vertical direction. The error signal of each gyroscope is Gaussian white noise and drift modeled as a first-order Markov random walk. These expressions provide a straightforward mean of calculating the FDE of an aircraft as a function of the flight duration, velocity, noise amplitude, drift amplitude, and drift’s time constant. These expressions are validated with Monte-Carlo simulations of long flights. This model quantifies the noise-versus-drift trade-off for a gyroscope in an inertial navigation system. It can save time when estimating the noise and drift that a gyroscope must exhibit to satisfy a given position-error requirement, or vice versa. They are used in particular to confirm the values, often cited without demonstration, of the noise and drift required to meet the maximum position error of an aircraft imposed by the Federal Aviation Administration’s required navigation performance 10 specification. Finally, it demonstrates that using the minimum in the measured Allan deviation of a gyroscope as a metric of the drift is incorrect, because it fails to capture the drift’s time constant. The proper metric is the maximum in the Allan deviation. [ABSTRACT FROM AUTHOR]

Magnetic anomaly navigation is gaining popularity as an alternative-navigation system that addresses the challenges of GPS denial. Magnetic anomaly navigation has recently been flight test proven on an F-16 aircraft. The corrupting magnetic environment on this operational platform is 2–3 orders of magnitude larger than previous flight-tests on geo-survey aircraft, while also displaying stochastic drift. The difficult magnetic environment of the platform necessitated a combination of batch and online calibration to obtain accurate navigation solutions. Superiority of the new method is shown in comparison to prior published methods. Real navigation results on the F-16 are shown to obtain accuracies 50% better than these competing methods, with low-altitude results obtaining 59-m distance root-mean-squared errors on 1.5 h flights versus 111 m using batch-only calibrations. [ABSTRACT FROM AUTHOR]