Research on Conflict Detection in Terminal Area based on Monte Carlo Method

Flight conflict detection is fundamental to flight dispatch, trajectory planning, and flight safety control. An ever-increasing aircraft population and higher speeds, particularly the emergence of hypersonic/supersonic aircrafts, are challenging the timeliness and accuracy of flight conflict detection. Traditional trajectory conflict detection algorithms rely on traversing multivariate equations of every two trajectories, in order to yield the conflict result and involve extensive computation and high algorithmic complexity; these algorithms are often unable to provide the flight conflict solutions required quickly enough. In this paper, we present a novel, low-altitude flight conflict detection algorithm, based on the multi-level grid spatiotemporal index, that transforms the traditional trajectory-traversing multivariate conflict computation into a grid conflict state query of distributed grid databases. Essentially, this is a method of exchanging "storage space" for "computational time". First, we build the spatiotemporal subdivision and encoding model based on the airspace. The model describes the geometries of the trajectories, low-altitude obstacles, or dangerous fields and identifies the grid with grid codes. Next, we design a database table structure of the grid and create a grid database. Finally, we establish a multilevel grid spatiotemporal index, design a query optimization scheme, and examine the flight conflict detection results from the grid database. Experimental verification confirms that the computation efficiency of our algorithm is one order of magnitude higher than those of traditional methods. Our algorithm can perform real-time (dynamic/static) conflict detection on both individual aircraft and aircraft flying in formation with more efficient trajectory planning and airspace utilization.

This paper introduces a real-time means to detect and avoid conflicts for a conceptual Free Flight environment. The assumption of this paper is approximately the same as the model given by Lee C.Yang of MIT. The concept assumes that the datalinks (e.g. ADS-B IN) between different aircraft are available and that conflicts are primarily resolved on the flight deck. This paper provides the flight crew multiple ways to avoid the conflicts, including turn, acceleration, deceleration, climb and descent. The alerting logic is based on probabilistic analysis through modeling of aircraft sensors and trajectory uncertainties. Monte Carlo simulations are used to determine conflict probability for every possible encounter situation. The construction of conflict probability database (CPDB) before the aircraft takes off can reduce the computation time of the flight computer. The CPDB records the conflict probability corresponding to every possible air traffic situation in which there are only two aircraft. Due to the integrity of the constructed CPDB, CPDB is suitable to be used to study the characters of the air traffic conflict. The Avoidance Maneuver Database (AMDB) and the Redundant Maneuver Database (RMDB) have been constructed based on the CPDB which is established in advance. AMDB and RMDB are used in resolving conflicts in multiple aircraft scenario.

Efficient Estimation of Interrupted Energy with Time-Varying Load Models for Distribution Systems Planning Studies

On the Joint Probability Density Function of Non-Linear Rolling Motion

To predict the potential conflict during the flight routes, a conflict detection approach based on multi-waypoints is presented. Aimed at the encounter geometry of two aircrafts with multi-waypoints, in order to simply the computational process, the aircrafts routes are divided into several straight conflict paths. Model along track kinematics equation of the aircraft by referencing to a classical conclusion that the along track error of the aircraft is normal distributed, consider an encounter between two aircrafts at the same flight level , construct an ellipse conflict region, by establishing a rectangular coordinate system represented aircraft position appropriately . First, propose a probability estimation algorithm of medium term conflict detection for cross routes at the same level, then analyze the relationship between the conflict probability and several factors based on various graphs. MATLAB result shows that the proposed algorithm is advanced in its simple calculation method, fast calculation speed and precise detection calculations.

Monte Carlo (MC) simulation is the most frequently used method to numerically model the light propagation in biological tissues because of its high flexibility and precision. Although MC simulation is assumed to be capable of achieving any desired precision, larger number of photons are always necessary for more precise simulation, leading to its major limitation of intensive computation. In this work, the authors present a way to adapt generative adversarial networks (GAN) to accelerate MC simulation. The pix2pix network, a variant of GAN, was investigated to reconstruct precise MC simulation results from the results roughly modeled by small amount of photons, thus the computation time was expected to be significantly saved. The proposed method was tested on single-layer embedded tumor models to derive the absorption distribution maps. The results demonstrate that the absorption distribution maps reconstructed from the simulation of only 10000 photons were very similar to those modeled by using 1000000 photons, based on the criterion of peak signal to noise ratio (PSNR) and percentage difference of power coupling efficiencies, and the simulation process was proved to be accelerated by approximately 102 times. For the first time, GAN was adapted to save computation time of MC simulation of light propagation. By achieving MC simulation with acceptable quality, the proposed method can speed up the computation by hundreds of times.

How to prevent collision problems on the aircraft need frequent detection for flight conflicts, therefore, it is necessary to design a good aircraft anti-collision algorithm. This paper analyzed the existing flight conflict detection algorithm paielli algorithm and carried out error analysis. On the basis of the original algorithm, this paper eliminated non-conflicting and coordinate processing, the error rate calculated by the improved algorithm was smaller. At the same time, the algorithm was verified by simulation and comparison with the conflict warning tools used at present, which proved that the new algorithm is more effective in detecting flight conflicts when the aircraft is turning.

This work focuses on modeling of film porosity in thin film deposition using stochastic differential equations. A deposition process is modeled via kinetic Monte Carlo (kMC) simulation on a triangular lattice. The microscopic process events involve atom adsorption and migration and the film growth allows for vacancies and overhangs to develop inside the film. Appropriate definitions of film site occupancy ratio (SOR), i.e., fraction of film sites occupied by particles over total number of film sites, and its fluctuation are introduced to describe film porosity. Deterministic and stochastic ordinary differential equation (ODE) models are also derived to describe the time evolution of film SOR and its fluctuation. The coefficients of the ODE models are estimated on the basis of data obtained from the kMC simulator of the deposition process using least-square methods. Simulation results demonstrate the applicability and effectiveness of the proposed film porosity modeling methods in the context of the deposition process under consideration.

The increasing demand for air travel is stressing the current Air Traffic Control (ATC). This is likely to cause both safety and performance degradation in the near future. In order to solve this problem, increasing the automation level of ATC is an important development direction. So flight behavior recognizing is becoming a key technique for ATC, for it is the basis of other function of ATC, such as landing scheduling, conflict detection, and so on. This paper introduced support vector machine (SVM) to solve flight behavior recognizing in terminal area, and designed multi-classification algorithm flow. The simulation results show that SVM is equal to this task.

Despite the recent economic recession and its adverse impact on air travel, the Federal Aviation Administration (FAA) continues to forecast an increase in air traffic demand that may see traffic double or triple by the year 2025. Increases in air traffic will burden the air traffic management system, and higher levels of safety and efficiency will be required. The air traffic controllers primary task is to ensure separation between aircraft in their airspace and keep the skies safe. As air traffic is forecasted to increase in volume and complexity [1], there is an increased likelihood of conflicts between aircraft, which adds risk and inefficiency to air traffic management and increases controller workload. To attenuate these factors, recent ATM research has shown that air and ground-based automation tools could reduce controller workload, especially if the automation is focused on conflict detection and resolution. Conflict Alert is a short time horizon conflict detection tool deployed in the Terminal Radar Approach Control (TRACON), which has limited utility due to the high number of false alerts generated and its use of dead reckoning to predict loss of separation between aircraft. Terminal Tactical Separation Assurance Flight Environment (T-TSAFE) is a short time horizon conflict detection tool that uses both flight intent and dead reckoning to detect conflicts. Results of a fast time simulation experiment indicated that TTSAFE provided a more effective alert lead-time and generated less false alerts than Conflict Alert [2]. TSAFE was previously tested in a Human-In-The-Loop (HITL) simulation study that focused on the en route phase of flight [3]. The current study tested the T-TSAFE tool in an HITL simulation study, focusing on the terminal environment with current day operations. The study identified procedures, roles, responsibilities, information requirements and usability, with the help of TRACON controllers who participated in the experiment. Metrics such as lead alert time, alert response time, workload, situation awareness and other measures were statistically analyzed. These metrics were examined from an overall perspective and comparisons between conditions (altitude resolutions via keyboard entry vs. ADS-B entry) and controller positions (two final approach sectors and two feeder sectors) were also examined. Results of these analyses and controller feedback provided evidence of T-TSAFE s potential promise as a useful air traffic controller tool. Heuristic analysis also provided information on ways in which the T-TSAFE tool can be improved. Details of analyses results will be presented in the full paper.

AII aims to infer the most likely future intent based on current aircraft motion states, therefore, it has become an essential method to enhance air traffic situational awareness [1]. Generally, aircraft motion states consist of aircraft IDs, latitude/longitude/altitude coordinates, ground speeds, accelerations and heading angles, which could be directly gained from the surveillance infrastructures like Radars and Automatic Dependent Surveillance-Broadcast (ADS-B) systems. Given current aircraft motion states, one important issue in gaining future air traffic situation prediction is to infer aircraft intent. This is significant because AII plays a fundamental role in conflict detection and avoidance, which hence determines the operational safety of air transportation system.

A method for conflict detection and resolution based on the parameterization of the aircraft intents is presented. The method is also based on the use of predefined trajectory patterns, which are in fact flight intents; these patterns include standard airline procedures and air traffic control regulations. The conflict resolution problem is formulated as a parametric optimization problem subject to constraints; the optimality criterium is defined so that the deviation from the intended (preferred) trajectories in the lateral profile is minimized. The resolution trajectory patterns take into account changes of the nominal waypoints (vectoring) and changes of the aircraft speeds. The method is applied to the case of multiple conflicts among commercial transport aircraft in converging traffic in the terminal area; different scenarios are considered, which include locked aircraft, that is, aircraft whose trajectories are known and fixed. The conflict detection and resolution algorithm relies on a kinetic trajectory predictor (nonlinear point-mass model with variable mass), which is accurate, flexible, and transparent, and provides the high-fidelity prediction required in the analysis of the demanding terminal area traffic. The cost of the global conflict resolution process is assessed, in terms of extra distance traveled, extra flight time, and extra fuel consumed for each aircraft.

Continuous-time random disturbances (also called stochastic excitations) due to increasing renewable generation have an increasing impact on power system dynamics; However, except from the Monte Carlo simulation, most existing methods for quantifying this impact are intrusive, meaning they are not based on commercial simulation software and hence are difficult to use for power utility companies. To fill this gap, this paper proposes an efficient and nonintrusive method for quantifying uncertainty in dynamic power systems subject to stochastic excitations. First, the Gaussian or non-Gaussian stochastic excitations are modeled with an It\^{o} process as stochastic differential equations. Then, the It\^{o} process is spectrally represented by independent Gaussian random parameters, which enables the polynomial chaos expansion (PCE) of the system dynamic response to be calculated via an adaptive sparse probabilistic collocation method. Finally, the probability distribution and the high-order moments of the system dynamic response and performance index are accurately and efficiently quantified. The proposed nonintrusive method is based on commercial simulation software such as PSS/E with carefully designed input signals, which ensures ease of use for power utility companies. The proposed method is validated via case studies of IEEE 39-bus and 118-bus test systems.

Multilateration(MLAT) is commonly used in civil and military surveillance applications to accurately locate an aircraft, vehicle or stationary emitter. MLAT calculates the TDOA of signals by transmitted aircraft and determines the aircraft’s location. With more than four receivers it is possible to estimate the 3D position of the aircraft by calculating the intersection of the resulting hyperbolas and the system integrity. In this study, our objectives are to apply MLAT technique to Jeju terminal control area and to propose a MLAT receiver network to properly estimate the positions of aircraft approaching this area. Based on computer simulations, we determine locations of ground receivers in Jeju terminal control area, calculate estimated position errors of the aircraft with respect to the selected receiver networks, and find the best receiver network with the least position error.

Cooperation of combinatorial solvers for en-route conflict resolution