GPS Receiver Position Augmentation Using Correntropy Kalman Filter in Low Latitude Terrain

If any Global Positioning System (GPS) receiver is operated in low latitude regions or urban canyons, the visibility further reduces. These system constraints lead to many challenges in providing precise GPS position accuracy over the Indian subcontinent. As a result, the standalone GPS accuracy does not meet the aircraft landing requirements, such as Category I (CAT-I) Precision Approaches. However, the required accuracy can be achieved by augmenting the GPS. Among all these issues, the predominant factors that significantly influence the receiver position accuracy are selecting a user/receiver position estimation algorithm. In this article, a novel method is proposed based on correntropy and designated as Correntropy Kalman Filter (CKF) for precise GPS applications and GPS Aided Geosynchronous equatorial orbit Augmented Navigation (GAGAN) based aircraft landings over the low latitude Indian subcontinent. The real-world GPS data collected from a dual-frequency GPS receiver located in the southern region of the Indian subcontinent (IISc), Bangalore with Lat/Long: 13.021°N/ 77.5°E) is used for the performance evaluation of the proposed algorithm. Results prove that the proposed CKF algorithm exhibits significant improvement (up to 34%) in position estimation compared to the traditional Kalman Filter.

A Supervised Neural Network Control for Magnetorheological Damper in an Aircraft Landing Gear

This paper adopts an intelligent controller based on supervised neural network control for a magnetorheological (MR) damper in an aircraft landing gear. An MR damper is a device capable of adjusting the damping force by changing the magnetic field generated in electric coils. Applying an MR damper to the landing gears of an aircraft could minimize the impact at landing and increase the impact absorption efficiency. Various techniques proposed for controlling the MR damper in aircraft landing gears require information on the damper force or the mass of the aircraft to determine optimal parameters and control commands. This information is obtained by estimation with a model in a practical operating environment, and the accompanying inaccuracies cause performance degradation. Machine learning-based controllers have also been proposed to address model dependency but require a large number of drop test data. Unlike simulations, which can conduct a large number of virtual drop tests, the cost and time are limited in the actual experimental environment. Therefore, a neural network controller with supervised learning is proposed in this paper to simulate the behavior of a proven controller only with system states. The experimental data generated by applying the hybrid controller with the exact mass and force information, which has demonstrated high performance among the existing techniques, are set as the target for supervised learning. To verify the effectiveness of the proposed controller, drop test experiments using the intelligent controller and the hybrid controller with and without exact information about aircraft mass and force are executed. The experimental results from the drop tests of a landing gear show that the proposed controller maintains superior performance to the hybrid controller without using explicit damper models or any information on the aircraft mass or strut force.

Trajectory Track for the Landing of Carrier Aircraft with the Forecast on the Aircraft Carrier Deck Motion

The paper presents a prediction method of deck lateral-directional motion for the control of landing trajectory of aircraft. Firstly, through the analysis of the process of aircraft returning to the ship, the modeling of the motion has been built. Secondly, in view of the delay of trajectory tracking captured in the actual process of aircraft landing on the ship, the error caused by the carrier motion signal has been analyzed. Based on the simulation results, the recommended prediction time of carrier motion has been proposed.

A Multi-Step CNN-Based Estimation of Aircraft Landing Gear Angles

This paper presents a method for measuring aircraft landing gear angles based on a monocular camera and the CAD aircraft model. Condition monitoring of the aircraft landing gear is a prerequisite for the safe landing of the aircraft. Traditional manual observation has an intense subjectivity. In recent years, target detection models dependent on deep learning and pose estimation methods relying on a single RGB image have made significant progress. Based on these advanced algorithms, this paper proposes a method for measuring the actual angles of landing gears in two-dimensional images. A single RGB image of an aircraft is inputted to the target detection module to obtain the key points of landing gears. The vector field network votes the key points of the fuselage after extraction and scale normalization of the pixels inside the aircraft prediction box. Knowing the pixel position of the key points and the constraints on the aircraft, the angle between the landing gear and fuselage plane can be calculated even without depth information. The vector field loss function is improved based on the distance between pixels and key points, and synthetic datasets of aircraft with different angle landing gears are created to verify the validity of the proposed algorithm. The experimental results show that the mean error of the proposed algorithm for the landing gears is less than 5 degrees on the light-varying dataset.

Analysis and optimisation design on damping orifice of oleo-pneumatic landing gear

This paper investigates the feasibility of improving the aircraft landing performance by design the damping orifice parameters of the landing gear using lattice Boltzmann method coupled with the response surface method. The LBM is utilised to simulate characteristics of the damping orifice after model validation. The numerical model of the landing gear using simulated damping force is validated by single landing gear drop test. Based on the numerical model and the response surface functions, the sensitivity analysis and the optimisation design are performed. The maximum error of mean velocity simulated using LBM with experimental data is 7.07% for sharp-edged orifices. Moreover, the numerical model predicts the landing responses adequately, the maximum error with drop test data is 2.51%. The max overloading of the aircraft decreases by 5.44% after optimisation, which proves that this method is feasible to design the damping orifice for good landing performance.

Pilot-Induced Oscillation Prevention During the Aircraft Landing

The paper focuses on a manned aircraft landing control system. It is known that actuator level and rate limitations can cause pilot-induced oscillations. This phenomenon occurs during intensive pilot control in a closed-loop system under certain initiating conditions associated with both the influence of the external environment and changes in the system dynamics. Oscillations appear unintentionally and unexpectedly for the pilot, which jeopardizes flight safety. The study shows the possibility of preventing aircraft oscillations using the method of nonlinear correction of systems by sequential introduction of a pseudo-linear correcting device into the control loop, the phase-frequency characteristic of the device not depending on the amplitude of the input signal. The airplane-pilot closed-loop system for various parameters of the input signal is analyzed by calculating the generalized function of sensitivity and the excitation index. The results of the study are presented in the form of angle and the pitch rate time processes, and landing trajectories.