J.R. Casar Corredera

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.

Surface movement radar data processing methods for airport surveillance

An airport surveillance function operating on surface movement radar (SMR) images is proposed and evaluated. The main contributions presented are the statistical error models of the target centroid and attributes extracted from radar images, developed and applied to the design of its main data processing blocks. Besides a multihypothesis image-to-tracks assignment method, a tracking filter using the extracted orientation and a classification scheme based on target attributes is detailed. The error models confidence and processing methods performance are demonstrated through simulation in representative scenarios.

Fuzzy management of surveillance sensors

A fuzzy management system is developed to cope with the problem of evaluating radar tasks in order to obtain their priority of execution. Based on all the available track and sector data, a reasoning system determines a priority for each one of the possible tasks that needs to be executed in each decision cycle through an inference process similar to that performed by a human operator. This methodology allows one to integrate high-level information in the decision process. The formal method elected to represent the variables involved in this decision process are the possibility theory and fuzzy set theory, since it offers a unified framework to represent uncertainty knowledge. In this sense, to obtain the priority for each task the reasoning process relies on a decision tree whose nodes are linguistic variables that represent the intermediate concepts used by a human operator to define the task priority. The study demonstrates the validity of this approach to cope with the task management problem.A fuzzy management system is developed to cope with the problem of evaluating radar tasks in order to obtain their priority of execution. Based on all the available track and sector data, a reasoning system determines a priority for each one of the possible tasks that needs to be executed in each decision cycle through an inference process similar to that performed by a human operator. This methodology allows one to integrate high-level information in the decision process. The formal method elected to represent the variables involved in this decision process are the possibility theory and fuzzy set theory, since it offers a unified framework to represent uncertainty knowledge. In this sense, to obtain the priority for each task the reasoning process relies on a decision tree whose nodes are linguistic variables that represent the intermediate concepts used by a human operator to define the task priority. The study demonstrates the validity of this approach to cope with the task management problem.

Double-parameter CFAR detector for Weibull clutter

Presents a double-parameter CFAR with very reasonable losses and low computational complexity. Its basic architecture has been conceived from tail extrapolation theory. The detector uses a detection threshold, set from the measured PFA which is obtained with an auxiliary threshold (pseudothreshold), lower than the final detection threshold. Starting from the basic scheme, a CFAR detector for Weibull clutter has been designed; both the pseudothreshold control mechanism and the correction of the basic extrapolation equation are described. Since, with less computation, the detection losses are much lower that those of classical double-parameter CFAR systems, the proposed detectors are qualified candidates for real system implementation.Presents a double-parameter CFAR with very reasonable losses and low computational complexity. Its basic architecture has been conceived from tail extrapolation theory. The detector uses a detection threshold, set from the measured PFA which is obtained with an auxiliary threshold (pseudothreshold), lower than the final detection threshold. Starting from the basic scheme, a CFAR detector for Weibull clutter has been designed; both the pseudothreshold control mechanism and the correction of the basic extrapolation equation are described. Since, with less computation, the detection losses are much lower that those of classical double-parameter CFAR systems, the proposed detectors are qualified candidates for real system implementation.

Robust adaptive detection of optical patterns in low resolution airborne radar images

Deals with the conception and design methodology of detectors for the location of prominent objects in airborne radar imagery. In its first half, several detection configurations are compared (linear, squared, logarithmic and binary detectors) working on impulsive clutter environments (Weibull model). The election of one detector over the others is based on its average behaviour over the whole range of clutter impulsiveness characteristics expected for the type of environment. The main conclusion is that logarithmic and binary detectors outperform the others. An adaptive threshold mechanism is incorporated into the detectors. Its aim is to maintain their CFAR property against variations of clutter impulsiveness. This scheme is based on the determination of the CFAR detection threshold factor as a function of the PFA estimated at a lower threshold (pseudothreshold).<<ETX>>

Data processing methods for autonomous on-board surveillance and collision avoidance

In this contribution, an ADS-B based airborne co-operative surveillance function, being the basis for an autonomous collision prediction function, is proposed. The main capabilities of the integrated data processing architecture provided will be detailed. This includes track formation, message-to-track association, coordinate transformation, position/velocity filtering, and conflict detection based on the concept of miss distance. Results illustrating the effectiveness of the system, based on rigorous modelling of navigation errors and communication channels, are presented.In this contribution, an ADS-B based airborne co-operative surveillance function, being the basis for an autonomous collision prediction function, is proposed. The main capabilities of the integrated data processing architecture provided will be detailed. This includes track formation, message-to-track association, coordinate transformation, position/velocity filtering, and conflict detection based on the concept of miss distance. Results illustrating the effectiveness of the system, based on rigorous modelling of navigation errors and communication channels, are presented.

High resolution imaging techniques in step-frequency subsurface radars

Imaging of buried objects with subsurface radars is affected by the resolution limits imposed by the constraints in bandwidth and antenna beamwidth. In this contribution, the step-frequency radar capabilities are exploited through the application of various signal processing methods that alleviate the resolution problem. Depth resolution is improved with a high-resolution technique based on signal extrapolation. Horizontal resolution is achieved via migration techniques similar to those used in seismic sounding, that exploit the peculiar characteristics of a step-frequency system. Selected examples are included to illustrate the improvements.