Augmented state multiple model probability data association track fusion for air traffic control

With growing air traffic density, air-traffic controllers (ATCOs) are facing more challenges in interpreting and analyzing air traffic information. As one of the solutions to this problem, automation supports such as tactile human computer interface, interactive 3D radar displays, and conflict resolution aid (CRA) are proposed for the enhancement of the current air traffic control (ATC) systems. To evaluate the proposed ATC systems, questionnaires are commonly used to get the feedback from ATCOs. However, the questionnaires are usually administered upon completion of each ATC simulation task thus provide only overall ratings towards the new ATC systems. In this paper, we propose and implement a novel Electroencephalogram (EEG)-based neurocognitive tools for evaluation of ATC systems. The nature of EEG-based technique is that the brain states can be monitored in a high resolution time, fitting the nature of time-critical ATC tasks. Thus, such EEG-based human factors study allows for real-time monitoring of ATCOs' mental workload during the task performance in ATC systems. We designed and conducted an experiment to evaluate the costs and benefits of the CRA and tactile user interface in future ATC systems. Thirty six participants participated in the experiment and were assigned into three groups of different display modes: Non-Display, Display, and Trajectory Prediction. In each group, three CRA conditions were given: Manual, Reliable and Unreliable. The EEG data were recorded during the tasks, and the traditional workload evaluation method NASA Task Load Index (TLX) was administered at the end of each task. The result shows that the ratings obtained from NASA TLX and from the EEG labeling are highly correlated. Thus, the EEG-based system is reliable to recognize workload during the task performance. With the proposed EEG-based system, we found that 1) relatively high workload was observed at the beginning of the experiment in each group which could be due to participants' unfamiliarity with the interfaces; 2) participants in the trajectory prediction group had much higher workload as compared to vertical display and non-display groups, which could be attributed to its complexity. The changes of workload are due to interaction between the type of display modes and time (p<0.05); 3) workload varied slightly with different CRA setting. The results show that the proposed EEG-based system for human factors study can provide better understanding of real-time mental workload changes during the task performance in new ATC systems, therefore enables the evaluation of current and future ATC systems.

A path object is a set of instructions plus the values of associated parameters that would be used by an aircraft’s flight management system (FMS) or area navigation (RNAV) computer to construct a flight trajectory on the basis of the values of the parameters provided by the pilot or air traffic control (ATC) system. The concept of path objects and its applicability for development of FMS- or RNAV-based flight paths for aircraft routes are described. The use of path objects requires only a small number of parameters for specification of an RNAV route, and the computer then calculates the resulting flight path. Because most RNAV routes are just variations of the same basic shapes, many RNAV routes can be generated from a single path object by changing just one or two parameters. This offers the capability to reduce the size of the navigation database by a significant amount. This also allows the dynamic alteration of three-dimensional FMS-RNAV routes instead of radar vectoring, which represents a significant improvement in ATC procedures. Because of the compact expressions for the path objects, these procedures could be used in a voice communications environment as well as a data link environment. A path object can be thought of as a high-level language with which aircraft and ATC systems communicate flight path intentions. The concept of FMS- or RNAV-stored path objects is a change in thinking about the role of avionics technology. It offers a means of including the precision of the FMS-RNAV technology in the ATC system during the transition from today’s system to the future’s fully automated control system. This is an enabling technology that improves the ability to use and maintain FMS-RNAV in its role as a vital component of the ATC system. It has the potential to enhance future air traffic management-communication navigation surveillance concepts and applications.

The interacting multiple model (IMM) and probabilistic data association (PDA) filter (IMMPDAF), is developed to support the air surveillance of the Track While Scan (TWS) radar systems. Take advantage of both, IMM and PDA algorithms to increase the tracking efficiency of fast maneuvers targets. The known algorithm was implemented in a radar tracking application for the purpose of modernizing the ‘L’ band radar, as a replacement for the conventional NN (Nearest Neighbor) algorithm which proved to be ineffective in situations with airborne targets in the air. In extensive experiments with complex, target maneuver and non-maneuver scenarios has been shown that proposed algorithm to have robust tracking characteristics with small position and velocity error.

Aviation performance depends on education and training of pilots and air traffic controllers (ATCOs) on technical, procedural, crisis management, decision making, leadership and communication skills. Recent incidents and studies, however, show crew members may be caught unaware and errors in human judgement can still occur in air traffic control (ATC) systems even more so in the presence of cyberattacks. This paper focuses on raising situation awareness and decision making of ATCOs with cyberattacks impacting operations in next-generation ATC systems. Our goal is to investigate crew-based cyber security as a layer in making ATC systems resilient to cyber and cyber-physical attacks. Automatic Dependent Surveillance Broadcast (ADS-B) technology is used as the basis of our preliminary investigation due to its widely known security concerns and central role in ATC systems. We present experimental study considerations for assessing cyber readiness and improving training of ATCOs. We propose a Kalman filter based cyber alarm solution approach for a machine learning based aid for ATCOs towards early cyberattack detection and incident response in ATC systems.

Most of the existing algorithms for multi-sensor tracks fusion are based on sensors with the same sampling rate, ignoring communication delay and different initial time. In this paper, a method to fuse information of asynchronous sensor tracks is proposed. At the track fusion center, position and velocity information of two sensors with different sampling rates and communication delays are fused. The fused track is an asynchronous track, which has minimum error covariance. This proposed fusion method not only avoids the time correction of asynchronous data, but also improves the tracking performance of multiple sensors. The performance of the proposed algorithm is also studied.

Variable Structure Multiple Model (VSMM) estimation generalizes Multiple Model (MM) estimation by assuming that the set of models used for MM estimation is time varying. By using VSMM estimation, a large model set which may cover all possible target maneuvers can be used without significant increase of computational load, while maintaining a reasonable estimation accuracy. Various implementable VSMM algorithms, like Model Group Switching (MGS), Likely Model Set (LMS) and Minimal Sub-Model-Set Switching (MSMSS) using the Interacting Multiple Model (IMM) algorithm with a sub-model-set adaptation logic have appeared in the recent literature. However, the use of these algorithms for tracking maneuvering target in clutter has not been explored. In presence of clutter, one need to use data association technique to differentiate target originated measurement from clutter. The probabilistic data association (PDA) has been popularly adopted to many algorithms for tracking in clutter. In this paper, we integrate PDA technique with MSMSS and propose a VSIMM-PDA algorithm for maneuvering target tracking in clutter. A new grating technique to account for potential model errors is used. A numeric example via multiple Monte Carlo runs, which compares the performance of the new algorithm to a standard IMM-PDA in terms of Root Mean Squared error (RMS) and percentage of track loss, is presented.© (2003) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Interacting multiple model (IMM) filtering and multiple hypothesis tracking (MHT) represent the most accurate methods currently available for tracking multiple maneuvering targets in cluttered environments. Although these methods are complex, modern computational capabilities make their combined implementation feasible for modern tracking systems. This paper discusses alternative approaches for developing a combined IMM/MHT tracking system and describes specific implementations that have been developed for radar and IRST applications. Simulation results for both radar and IRST systems are presented. Track maintenance and accuracy performance for the IMM/MHT system is compared with that obtained from an MHT tracker using a conventional filtering approach. Results indicate that the improvements in data association derived from the use of IMM filtering with MHT may be comparable to the well known IMM improvements in track accuracy. The addition of multiple filter models to MHT data association has the potential to significantly increase computational requirements. Thus, several compromises, described in this paper, have been developed in order to assure computational feasibility. Preliminary estimates of computational requirements are given in order to demonstrate implementation feasibility.© (1997) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

A systematic approach to interactive multiple model (IMM) filter design is described, using evolution strategies (ESs), a global optimization technique inherited from the artificial intelligence field. The methodology defined is based on the definition of test scenarios and related performance goals, which are then assumed as design constraints. Then, ESs are used to search a set of filter parameters for the chosen IMM structure, trying to fulfill all design constraints, and if not possible, gracefully relaxing those constraints. The method has been successfully applied to the design of an IMM monoradar tracker for an air traffic control (ATC) application, comparing the performance of several IMM structures. The resulting filters are compared with previous designs for similar applications, with ad-hoc parameters, and with other classical optimization methods. The proposed method enables improved filter tuning in a comprehensive and robust way.

The identification of backup radio sites, to increase the availability of the future air/ground (A/G) air traffic control (ATC) systems, is investigated, When there is an A/G communications failure, due to a radio site or an ATC controller access failure, another site (or sites) provides the service. The backup site should be capable of providing full coverage of the desired airspace known as the service volume. Meeting the full coverage conditions by one site or a group of sites is analyzed. It is concluded that identification of one site providing full coverage can be determined easily. The identification of a group of sites, each with partial coverage and providing full coverage is analyzed in detail. An algorithm involving both analytical and numerical approaches is described. It determines if two radio sites with given geographical coordinates, and each with partial coverage of a given cylindrical service volume, can provide full coverage. This algorithm can be implemented as a computer program to process a database of radio sites.

This paper describes an application of the IMM (Interacting Multiple Model) technique in a multiple target tracking system for an IRST (Infrared Search and Track) system operating in the mid and in the long wave infrared bands. The use of the two IR bands allows better performances in terms of detection probability, lower false tracks and short time for track initiation. To properly merge data from the two sensors, an enhancement of the PDA (Probabilistic Data Association) technique is introduced in the process. The approach has shown to properly operate with a very high number of possible targets in the two IR bands. Good results have been obtained also in the case of clustered detections, as well as in uniformly space distributed detections.

Under the condition of low data-rate measurement, it tends to mistrack or lose the target of high maneuvering targets by using the traditional single model tracking algorithm. This paper proposes a tracking method using Interacting Multiple Model (IMM) algorithm combined with Probabilistic Data Association (PDA) algorithm to solve the problem. Several measurement filtering thresholds are tuned to improve the accuracy of track association. Experiments show that this method achieves good tracking results.

This paper presents sensor and data rate control algorithms for tracking maneuvering targets. The manuevering target is modeled as a jump Markov linear system. We present novel extensions of the Interacting Multiple Model (IMM), Particle filter tracker, and Probabilistic Data Association (PDA) algorithms to handle sensor and data rate control. Numerical studies illustrate the performance of these sensor and data rate control algorithms.

The human-machine problem in automation systems is generally limited to control tasks in self-contained (i.e., clearly definable) technical processes. Integrated air traffic management and control systems, however, are not definable processes, as the amount and the purpose of traffic vary. Persons charged by different organizations try to override the other claims by means of their automation equipment. In order to avoid dangerous competition, there are some requirements for the design of the automation system. Analyzing the general air traffic management problem, the influence of the system architecture on the verification and validation of the system function is to be shown.

The paper presents the results of research and development of a methodology for optimizing parameters of tracking estimators for object coordinates and motion parameters. The methodology is based on a comprehensive approach to training dataset formation considering various types of object motion and application of specialized optimization algorithms. The developed algorithms implement a complete optimization cycle, including training dataset formation, data preprocessing, parameter optimization, and verification of obtained results. The results of practical application of the methodology for optimizing parameters of non-adaptive Kalman filter and Interacting Multiple Model (IMM) filter under various observation conditions and object motion patterns are demonstrated. Based on simulation modeling, it is shown that the application of the developed methodology significantly improves the accuracy of estimating coordinates and motion parameters compared to traditional approaches to parameter selection. Special attention is paid to studying the stability of obtained solutions to changes in observation conditions and object motion patterns. The obtained results are advisable to use in development and modernization of radar data tracking systems, air traffic control systems, air and ground situation monitoring complexes, as well as in other applications requiring high-precision estimation of object motion parameters under a priori uncertainty.

Air traffic controllers (ATCos) monitor flight operations and resolve predicted aircraft conflicts to ensure safe flights, making them one of the essential human operators in air traffic control systems. Researchers have been studying ATCos with human subjective approaches to understand their tasks and air traffic managing processes. As a result, models were developed to predict ATCo actions. However, there is a gap between our knowledge and the real-world. The developed models have never been validated against the real-world, which creates uncertainties in our understanding of how ATCos detect and resolve predicted aircraft conflicts. Moreover, we do not know how information from air traffic control systems affects their actions. This Ph.D. dissertation work introduces methods to evaluate existing ATCo action prediction models. It develops a prediction model based on flight contextual information (information describing flight operations) to explain the relationship between ATCo actions and information. Unlike conventional approaches, this work takes data-driven approaches that collect large-scale flight tracking data. From the collected real-world data, ATCo actions and corresponding predicted aircraft conflicts were identified by developed algorithms. Comparison methods were developed to measure both qualitative and quantitative differences between solutions from the existing prediction models and ATCo actions on the same aircraft conflicts. The collected data is further utilized to develop an ATCo action prediction model. A hierarchical structure found from analyzing the collected ATCo actions was applied to build a structure for the model. The flight contextual information generated from the collected data was used to predict the actions. Results from this work found that the collected ATCo actions do not show any preferences on the methods to resolve aircraft conflicts. Results found that the evaluated existing prediction model does not reflect the real-world. Also, a large portion of the real conflicts was to be solved by the model both physically and operationally. Lastly, the developed prediction model showed a clear relationship between ATCo actions and applied flight contextual information. These results suggest the following takeaways. First, human actions can be identified from closed-loop data. It could be an alternative approach to collect human subjective data. Second, the importance of evaluating models before implications. Third, potentials to utilize the flight contextual information to conduct high-end prediction models.