Nowadays, many researchers pay much attention to Multilateration localization process in air traffic control. This thesis deals with system geometry effect on target localization process. Modifications are proposed in both the sensors deployment network and the algorithm sequence used.
The work will be on a 3 steps: First, working on a small cell network taking minimum number of sensors. Different optimal sensor deployment methods will be examined and a pilot area will be chosen from a Multilateration network at Cairo International Airport, 05L Runway (RW). Simulation for a single aircraft is performed and the results are compared. Second, the deployment method that gives the best results in a small cell network will be considered and a new sensors deployment covering an area larger than the existing one is proposed, trying to have the same performance of the existing system with the advantage of decreasing the number of sensors. Finally, the proposed Multilateration network is divided into 9 main clusters and 4 backup clusters instead of using all sensors in localization process, and the cluster that has the least PDOP (position dilution of precision) is selected. To enhance the localization process only the cluster that has least PDOP < 21 is selected. Otherwise, are rejected. Space capacity and safety of Airspace Surveillance Systems are regularly increasing to meet the demands of air traffic control.
A 2-D Multilateration algorithm is proposed to accurately identify the aircraft position. It is based on the classical Two Ray propagation model. Important parameters that affect the final aircraft estimation position are presented like the path gain factor (interference factor). The proposed algorithm uses the geographic coordinates (Latitude and Longitude) which are considered more practically used in navigation than Cartesian coordinates that are used in previous algorithms in literature. In order to simulate the algorithm, the Multilateration network at Cairo international Airport is considered to be a pilot area and the results are presented.
Table of Contents
1. Introduction
1.1 Overview
1.2 Motivation
1.3 Thesis Contributions
1.4 Thesis Organization
2. Review Of Multilateration Air Traffic Surveillance System And Localization Problem
2.1 Introduction
2.1.1 Classifications of Localization algorithms
2.1.2 Classifications of Localization Processes
2.2 A Multilateration System Background
2.3 Localization Problem in Multilateration
2.4 Related Work
3. Geometry Effect On A Multilateration Air Traffic Surveillance System Performance
3.1 Introduction
3.2 Optimal Sensors deployment methods used for comparison
3.3 Proposed modification in a MLAT network
3.4 Proposed modification in a MLAT algorithm
3.4.1 First Modification
3.4.2 Second Modification
3.5 Simulated Scenarios
3.6 Conclusions
4. A 2D Multilateration Algorithm Used For Air Traffic Localization And Tracking
4.1 Hypotheses and Proposed Algorithm
4.1.1. Classical Two Ray Propagation Model
4.1.2. Proposed Algorithm
4.1.3. Kalman Filter Estimator
4.2 Simulation Results
4.3 Conclusions
5. Multiple Aircrafts Tracking In Clutter For Multilateration Air Traffic Surveillance System
5.1. Introduction
5.2. Proposed Algorithm for Single Aircraft Tracking in Clutter
5.3. Proposed Algorithm for Multiple Aircrafts Tracking in Clutter
5.3.1. A Proposed algorithm modification for the proposed MLAT network
5.4. Simulated Results
5.5. Conclusions
6. Conclusions And Recomendations For Future Work
6.1. Conclusions
6.2. Recomendations For Future Work
Research Objective and Topics
The primary research objective of this thesis is to improve the reliability and accuracy of Multilateration (MLAT) air traffic surveillance systems by optimizing sensor deployment and developing more robust tracking algorithms that function effectively even in the presence of sensor failures and environmental clutter.
- Enhancement of MLAT network geometry to mitigate site-specific performance limitations.
- Development of cluster-based sensor distribution to prevent system shutdown during sensor outages.
- Derivation of new 2D localization algorithms using geographic coordinates instead of Cartesian systems.
- Integration of Kalman filter estimators to ensure continuous aircraft tracking.
- Implementation of validation gates and data association techniques to track targets reliably amidst clutter.
Excerpt from the Book
3.4.1 First Modification
The existing algorithm of multilateration system at Cairo International Airport faces a big problem. It uses all the 32 sensors' measurements in localization process and in case of any 2 sensors have failure; the system becomes out of service and makes shutdown which is not practical in navigation usage. To overcome this problem the multilateration network is divided into clusters and each cluster consists of 4 sensors as shown in figure 8. If at least one sensor has a failure, the cluster will be isolated till the sensor return back to the normal case and the system is still running. The algorithm will run on each cluster in parallel and get the estimated position. PDOP coefficient is proposed to be calculated and the cluster that has the least PDOP will be selected as shown in Fig. 3.8.
Summary of Chapters
1. Introduction: Outlines the necessity of improving air traffic surveillance systems for safety and efficiency, detailing the specific challenges faced by existing installations at Cairo International Airport.
2. Review Of Multilateration Air Traffic Surveillance System And Localization Problem: Discusses existing localization algorithms and processes while establishing the theoretical background of Multilateration systems.
3. Geometry Effect On A Multilateration Air Traffic Surveillance System Performance: Analyzes the impact of network geometry on localization accuracy and proposes cluster-based modifications to ensure system robustness.
4. A 2D Multilateration Algorithm Used For Air Traffic Localization And Tracking: Presents a 2D localization approach utilizing geographic coordinates and Kalman filtering to achieve accurate, continuous aircraft tracking.
5. Multiple Aircrafts Tracking In Clutter For Multilateration Air Traffic Surveillance System: Describes a non-Bayesian algorithm designed to manage data association and track multiple targets effectively when clutter or false alarms are present.
6. Conclusions And Recomendations For Future Work: Summarizes the thesis findings regarding the proposed network and algorithm improvements and suggests future research directions.
Key Words
Multilateration, Air traffic surveillance systems, Air traffic control, Dilution of Precision, Data association, Tracking algorithm, Kalman filter, Network deployment, Sensor clustering, Geographic coordinates, Aircraft tracking, Cluster management, Performance analysis, Cairo International Airport.
Frequently Asked Questions
What is the core focus of this doctoral thesis?
The research focuses on enhancing the resilience and accuracy of Multilateration (MLAT) air traffic surveillance systems, specifically addressing issues like sensor failure-induced system shutdowns and performance degradation due to poor geometry or environmental clutter.
What are the primary thematic areas covered?
The work covers sensor network deployment optimization, mathematical localization algorithms, Kalman filter-based tracking, and data association techniques for multi-target tracking in cluttered environments.
What is the central research question?
The research aims to determine how modifying sensor deployment configurations and algorithm sequences can mitigate system geometry effects and prevent total system failure when individual sensors become unavailable.
Which scientific methodology does the author employ?
The author uses mathematical modeling, specifically utilizing the Two Ray propagation model, along with simulation-based testing to compare proposed network designs and tracking algorithms against existing operational systems at Cairo International Airport.
What does the main body of the work address?
The main chapters analyze the effects of geometry on performance, introduce cluster-based redundancy to maintain operation despite sensor failures, and present new 2D localization algorithms using latitude and longitude coordinates.
Which keywords best characterize this work?
Key terms include Multilateration, Air traffic surveillance, Dilution of Precision (PDOP), Kalman filter, Data association, and Sensor network optimization.
How does the proposed cluster-based approach resolve the system shutdown problem?
By dividing the total network into independent clusters, the system can isolate a failing sensor within a specific cluster while the rest of the network remains fully operational and continues to provide localization data.
How does the proposed 2D algorithm differ from previous methods?
The proposed algorithm uses real-world geographic coordinates (latitude and longitude) for localization instead of Cartesian coordinates, which offers a more practical approach for actual air traffic navigation systems.
What role does the Kalman filter play in the tracking process?
The Kalman filter is applied to the estimated positions to perform recursive prediction and correction, allowing the system to maintain continuous tracking even when measurements contain noise or are briefly interrupted.
- Citar trabajo
- Mohamed El-Ghoboushi (Autor), 2018, Improved Strategies applied in Air Traffic Surveillance Systems in Egypt, Múnich, GRIN Verlag, https://www.grin.com/document/542924
