Jesús García Herrero

Methods for Operations Planning in Airport Decision Support Systems

Simulation and decision support tools can help airport ground controllers to improve surface operations and safety, leading to enhancements in the process of traffic flow management. In this paper, two planning approaches for automatically finding the best routes and sequences for demanded operations are proposed and analyzed. These approaches are integrated into a general decision support system architecture. The problem addressed is the global management of departure operations, moving aircraft along airport taxiways between gate positions and runways. Two global optimization approaches have been developed together with a suitable problem representation: a modified time-space flow algorithm and a genetic algorithm, both aimed at minimizing the total ground delay. The capability and performance of these planning techniques have been illustrated on simulated samples of ground operations at Madrid Barajas International Airport.

Use of map information for tracking targets on airport surface

A generic and novel approach for integrating airport map information with sensor measurements in the track estimation process is proposed and evaluated. The surface restrictions imposed by the network of roads, taxiways, and runways, represented by a simplified geometric model, are included in both the target observation and the dynamic models, to derive the target state estimates. The performance of the methods proposed is illustrated in representative airport surface scenarios, taking as a reference for comparison other tracking alternatives such as VS-IMM (variable structure interacting multiple model estimator) ground target tracking, or standard ones that do not make use of ground information.

Cooperative Management of a Net of Intelligent Surveillance Agent Sensors

The use of distributed artificial intelligence (DAI) techniques, particularly the multiagent systems theory, in a decentralized architecture, is proposed to manage cooperatively, all sensor tasks in a network of (air) surveillance radars with capabilities for autonomous operation. At the multisensor data fusion (DF) center, the fusion agent will periodically deliver to sensor agents a list with the system‐level tasks that need to be fulfilled. For each system task, indications about its system‐level priority are included (inferred global necessity of fulfilling the task) as well as the performance objectives that are required, expressed in different terms depending on the type of task (sector surveillance, target tracking, target identification, etc.). Periodically, the local manager at each sensor (the sensor agent) will decide on the list of sensor‐level tasks to be executed by its sensor, providing also the sensor‐level priority and performance objectives for each task. The problem of sensor(s)‐to‐task(s) assignment (including decomposition of system‐level tasks into sensor‐level tasks and translation of system‐level performance requirements to sensor‐level performance objectives) is the result of a negotiation process performed among sensor agents, initiated with the information sent to them by the fusion agent. With types of agents, a symbolic bottom‐up fuzzy reasoning process is performed that considers the available fused or local target tracks, surveillance sectors data, and (external) intelligence information. As a result of these reasoning processes, performed at each agent planning level, the priorities of system‐level and sensor‐level tasks will be inferred and applied during the negation process.

Application of evolution strategies to the design of tracking filters with a large number of specifications

This paper describes the application of evolution strategies to the design of interacting multiple model (IMM) tracking filters in order to fulfill a large table of performance specifications. These specifications define the desired filter performance in a thorough set of selected test scenarios, for different figures of merit and input conditions, imposing hundreds of performance goals. The design problem is stated as a numeric search in the filter parameters space to attain all specifications or at least minimize, in a compromise, the excess over some specifications as much as possible, applying global optimization techniques coming from evolutionary computation field. Besides, a new methodology is proposed to integrate specifications in a fitness function able to effectively guide the search to suitable solutions. The method has been applied to the design of an IMM tracker for a real-world civil air traffic control application: the accomplishment of specifications defined for the future European ARTAS system.

A model to 4D descent trajectory guidance

Current standard arrival routes or STARs will require in next years more flexibility to handle the increase of aircraft arrivals and to minimize the related environmental impact applying continuous descent trajectories at idle thrust. To do that, a global optimized 4D trajectory must be followed from the given aircraft position to the terminal gate and tracked in real time. 4D trajectory guidance is not an easy task (mainly at arrivals trajectories) due to the variability of the atmospheric and meteorological conditions with the altitude, which introduces discrepancies from the given reference trajectory and the real one. In this paper we present a new 4D trajectory guidance model focussed on the descent and arrival flight phase. This model has been designed as a 4D FMS which is part of an entire dynamic control system using the well established point mass model for the aircraft aerodynamic behaviour. The proposed 4D FMS compares the aircraft state vector with the reference trajectory and calculates the necessary control entrances to fit the actual response to the previously estimated one.

FUZZY REASONING IN A MULTIAGENT SYSTEM OF SURVEILLANCE SENSORS TO MANAGE COOPERATIVELY THE SENSOR-TO-TASK ASSIGNMENT PROBLEM

In this work, a surveillance network composed of a set of sensors and a fusion center is designed as a multiagent system. Negotiation among sensors (agents) is proposed to solve the task-to-sensor assignment problem (the allocation of tasks to sensors), addressing several aspects. First, the fusion center determines the tasks (system tasks) to be performed by the network at each management cycle. To do that, a fuzzy reasoning system determines the priorities of these system tasks by means of a symbolic inference process using the fused data received from all sensors. In addition, a fuzzy reasoning process, similar to that performed in the fusion center, is proposed to evaluate the priority of local tasks (sensor tasks) now executed by each sensor. The network coordination procedure will be based on the system-task priorities, computed in the fusion center, and on the local priorities evaluated in each sensor. Priority values for system and sensor tasks will be the basis to guide a negotiation process among sensors in the multiagent system. The validity of the fuzzy reasoning approach is supported by the fact that it has been able to manage environmental situations in a similar way as experienced human operators do. Included results illustrate how the negotiation scheme, based on task priority and measured through their time-variant priority, allows the adaption of sensor operation to changing situations.

A 4D trajectory negotiation protocol for Arrival and Approach sequencing

Future 4D TBO will require effective air- ground data link communication and negotiation protocols. This issue is especially critical in Arrival and Approach flight phase due to the variability of conditions into a short space-time environment where multiple aircraft simultaneously converge. Besides, several subtasks are closely related with effective air-ground negotiation protocols for 4D TBO in Terminal Areas: predicting accurate arrivals 4D trajectories, performing well established 4D trajectory formats for an effective interoperability between airborne and ground systems, designing efficient real-time aircraft arrival sequencer and scheduler algorithms, etc. In this paper we propose a 4D Trajectory Air- Ground Negotiation Protocol for Arrival and Approach Sequencing. The Negotiation Protocol has been implemented in an ad hoc multi-agent platform. Based on this proposal we summarize other relevant information that should be incorporated into the 4D trajectory information.

Optimizing Statistical Character Recognition Using Evolutionary Strategies to Recognize Aircraft Tail Numbers

The design of statistical classification systems for optical character recognition (OCR) is a cumbersome task. This paper proposes a method using evolutionary strategies (ES) to evolve and upgrade the set of parameters in an OCR system. This OCR is applied to identify the tail number of aircrafts moving on the airport. The proposed approach is discussed and some results are obtained using a benchmark data set. This research demonstrates the successful application of ES to a difficult, noisy, and real-world problem.

Player: An open source tool to simulate complex maritime environments to evaluate data fusion performance

Abstract In this paper it is presented a new open source tool for evaluating data fusion systems, mainly related to the maritime surveillance domain. This tool provides specific utilities for designing and simulating synthetic maritime environments to assists in the fusion development process, like designing vessels trajectories, placing different types of sensors, simulating vessels dynamics, or simulating sensors detections. This synthetic information can be used to feed a data fusion system to evaluate its response in a reproducible way under different conditions. This tool can be used to optimize the data fusion evaluation process, since testing a fusion system is a quite complex task, as the fusion output depends on the combination of multiple algorithms, configurations, measures, timing, and so on. Then, having reproducible synthetic environments can be quite useful when evaluating fusion results, system performance in dense scenarios, vessel trajectories with different dynamics, sensor coverages, and so on. This tool has been used with success for evaluating different fusion systems, and now it is presented as an open source tool, so it can be easily adapted to different environments, be used by other researchers, or extended by the community. This paper presents how it is built, the underlying algorithms, and presents some example use cases.

On Approximation Properties of Smooth Fuzzy Models

This paper addresses the approximation properties of the smooth fuzzy models. It is widely recognized that the fuzzy models can approximate a nonlinear function to any degree of accuracy in a convex compact region. However, in many applications, it is desirable to go beyond that and acquire a model to approximate the nonlinear function on a smooth surface to gain better performance and stability properties. Especially in the region around the steady states, when both error and change in error are approaching zero, it is much desired to avoid abrupt changes and discontinuity in the approximation of the input–output mapping. This problem has been remedied in our approach by application of the smooth compositions in the fuzzy modeling scheme. In the fuzzy decomposition stage of fuzzy modeling, we have discretized the parameters and then calculated the result through partitioning them into a dense grid. This could enable us to present the formulations by convolution and Fourier transformation of the parameters and then obtain the approximation properties by studying the structural properties of the Fourier transformation and convolution of the parameters. We could show that, irrespective to the shape of the membership function, one can approximate the dynamics and derivative of the continuous systems together, using the smooth fuzzy structure. The results of the paper have been tested and evaluated on a discrete event system in the hybrid and switched systems framework.