This paper introduces a new system for mapping the coverage provided by antennas using autonomous drones and data fusion techniques. The primary objective of the system is to leverage a contextual layer that provides essential information regarding mobile antennas and their characteristics, such as type, operating frequency, and geodetic coordinates, among other relevant parameters. Based on this contextual layer, the system infers an initial coverage map, which relies entirely on analytical models to estimate free-space power loss as a function of signal propagation distance and frequency. This preliminary estimate is subsequently refined and adjusted to generate a coverage map that more accurately reflects real-world conditions. The refinement process is carried out through measurements acquired at various spatial locations by the drone, which undergo a spatial regression process that employs fully connected neural networks. The study demonstrates how small-scale autonomous drone missions can efficiently sample regions within the target mapping area. These sampled data points enable, through the fusion and filtering process, the spatial propagation of power loss corrections, thereby enhancing the contextual layer and yielding a significantly more precise coverage map.

This paper shows an innovative approach to road monitoring by integrating autonomous drones and sensor fusion within a computer vision-based system. By employing different sets of algorithms, drones equipped with cameras, GPS, and ultrasonic distance sensors can efficiently detect and geolocate road damages, providing crucial data for maintenance and infrastructure management. The system’s key components include automated planning, autonomous flight capabilities, object detection, and sensor fusion techniques, enhancing scalability and adaptability. The main objective is to use context information to compute a flying plan where the subsequent detection of defects allows us to expand and enhance GIS data. The implementation of such a system holds significant potential for improving road safety and optimizing maintenance costs, marking a notable advancement in the convergence of autonomous technologies, sensor fusion, and computer vision for effective road surveillance.

Within the EUROCONTROL Air Traffic Management (ATM) architecture, the Surveillance Analysis Support System for ATC Centres (SASS-C) is dedicated to the validation of Air Navigation Service Providers. One of its applications is the opportunity Traffic Reconstruction (OTR), which seeks to reconstruct all trajectories by combining noisy measurements from different sensors. It relies on association, tracking and fusion algorithms to determine the true motion of the aircraft on both the vertical and horizontal axes, alongside contextual information useful for identifying the aircraft’s flight mode at any given time. This paper focuses on the filtering of the vertical dimension and outlines certain problems present in the system: the effect of quantized measurements, the appearance of mode of flight transition overshoots and the low reactivity of the filter to abrupt transitions. These problems are analyzed and preliminary improvements according to the ATC context are implemented to overcome them. To demonstrate the improvement, a comparison between the proposed system and the original one is carried out through synthetic trajectory analysis.

UAVs are a cutting-edge technology whose use is currently highly restricted due to their potentially dangerous characteristics, and due to the lack of legislation adopting them and allowing a safe control of these vehicles. Unmanned Air System Traffic Management (UTM) initiatives seek to regularise their use by means of validation and monitoring techniques for the trajectories of these aircraft, both before flight and in real time. For this purpose, in the UTM framework, drones will be collaborative using similar systems to AIS and ADSB for ships or aircraft vehicles. Currently there are no UAV trajectory datasets that allow research in this field, so in this paper a dataset composed of the position and kinematics of the drones over time has been designed. By means of this dataset it is possible to develop and evaluate machine learning methods that help the verification entity to fulfil its functionalities. In this work we propose a first approach to extract useful information by classifying the type of drone based on its movement dynamics. This information would be useful in the identification of the validity of the proposed trajectory for the airframe indicated by the user. The code used for this research is available at https://github.com/DavidSanpedrochez/UAVTrackClassification

The role of the military has been continuously evolving and expanding beyond traditional warfare. In addition to an increasingly asynchronous battlefield, today's military is involved in supporting peacekeeping, crisis management, and disaster relief, to name just a few. Such activities require intense contact with diverse non-military organizations such as government, police, emergency services, relief agencies, and religious leaders. As a result, the information needed for commanders and other decision-makers must come from a wide variety of sources in a wide variety of formats. Thus, any attempt at fusion of this information requires an underlying system concept that is able to standardize information from a variety of input sources, including device-derived information (sensors) and (multiple) natural language(s), as well as to understand complex context, both static and dynamic, and to be able to deal with measures of uncertainty which may vary from source-type to source-type. This paper presents several lessons learned from the ongoing work of a series of NATO Research Task Groups (which have focused on the changing data and information incorporating structured and unstructured human generated information with device-generated data needs for fusion in increasingly complex scenarios requiring interaction between devices, human-generated information and complex contextual backgrounds. We discuss the differing challenges facing the synergic information processing as scenario complexity grows.

This paper describes two tracking filters based on the use of kinematic information (velocity, acceleration), in addition to usual position measurements. This kinematic information allows for more advanced filtering methods, reducing error especially on maneuvers. In the paper we will show two different Kalman filter exploiting this information, and compare them with regards to accuracy, computational load ... Mode S Enhanced Surveillance measures will be used as an example for the application of those filters.

A surveillance system that fuses data from several data sources is more robust than those which depends on a single source of input. Fusing the information acquired by a vision system is a difficult task since the system needs to use reliable models for errors and take into account bad performance when taking measurements. In this research, we use a bidimensional object correspondence and tracking method based on the ground plane projection of the blob centroid. We propose a robust method that employs a two phase algorithm which uses a heuristic value and context information to automatically combine each source of information. The fusion process is carried out by a fusion agent in a multi-agent surveillance system. The experimental results on real video sequences have showed the effectiveness and robustness of the system.

This work addresses off-line accurate trajectory reconstruction for air traffic control. We propose the use of specific dynamic models after identification of regular motion patterns. Datasets recorded from opportunity traffic are first segmented in motion segments, based on the mode probabilities of an IMM filter. Then, reconstruction is applied with an optimal smoothing filter operating forward and backward. The parameters describing the specific modes are estimated and then used as external input for smoothing filters. The performance of this approach is compared with a method based on interpolation B-splines. Comparative results on simulated and real data are discussed at the end.

Video target tracking is a complex task, specially when the tracking system is expected to work well in different scenarios. For this reason, this paper proposes an architecture based on a two layer image-processing modules: general tracking layer (GTL) and context layer (CL). GTL describes a generic multipurpose tracking process for video surveillance systems. CL is designed as a symbolic reasoning system that manages the symbolic interface data between GTL modules in order to assess a specific situation and take the appropriate decision. CL intervenes at three different stages of the tracking process, these are initialization, association and update. Our architecture has been tested in two different scenarios to show the advantages in improved performance and output continuity.

The growing interest in surveillance in public, military and commercial scenarios is increasing the need to create intelligent or automated distributed visual surveillance systems. Many applications based on distributed resources use the software agent paradigm. In this work, a multi-agent framework is applied to coordinate an indoor-surveillance system based on video cameras. The capacity of coordination will allow the improvement of the global image and the effectiveness of the task distribution. Software agents are embedded in each camera and control the capture parameters. The multi-agent framework allows the coordination of the acquisition procedure based on high level messages and the fusion of information among agents. The agent paradigm uses the internal interpretation of the situation from each agent to improve the global coordination

This work addresses the application of a machine-learning approach to classify ATC trajectory segments from recorded opportunity traffic. It is based on the mode probabilities estimated by an IMM tracking filter operating forward and backward over available data. A learning algorithm creates a rule base for classification from these data, once they have been properly prepared. Performance of this data-driven classification system is compared with a more conventional approach based on transition detection on simulated and real data of representative situations. The offline processing of real data allows an accurate classification of manoeuvring segments, with the possibility of synthesizing ground truth lines for performance evaluation

The evolution of airborne communication systems and ground stations make it possible to improve the performance of tracking systems, in order to increase capacity, safety and efficiency of air traffic environments. These new systems enable downlinking several aircraft parameters for use in ground air traffic management (ATM) systems. Some of these parameters can be used for ATM system function enhancements, such as aircraft tracking. Thus, in this paper, a robust architecture is proposed using downlink airborne parameters (DAPs). This architecture consists of two filters in parallel: one is in charge of dealing with DAPs and the other one cope with the absence of them. Results showing the improvements in performance of the whole system are presented, as well as results coming from the DAP based filter alone. Both results will be compared with the ones delivered by the conventional filter

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. © 2003 Wiley Periodicals, Inc.