Exploiting Structural Signal Information in Passive Emitter Localization

The operational use of systems for passive geolocation of radio frequency emitters poses various challenges to single sensor systems or sensor networks depending on the measurement methods. Position estimation by means of direction finding systems often requires complex receiver and antenna technique. Time (Difference) of Arrival methods (TDOA, TOA) are based on measurements regarding the signal propagation duration and generally require broadband communication links to transmit raw signal data between spatially separated receivers of a sensor network. Such bandwidth requirements are particularly challenging for applications with moving sensor nodes. This issue is addressed in this thesis and techniques that use signal structure information of the considered signals are presented which allow a drastic reduction of the communication requirements. The advantages of using knowledge of the signal structure for TDOA based emitter localization are shown using two exemplary applications. The first case example deals with the passive surveillance of the civil airspace (Air Traffic Management, ATM) using a stationary sensor network. State of the art airspace surveillance is mainly based on active radar systems (Primary Surveillance Radar, PSR), cooperative secondary radar systems (Secondary Surveillance Radar, SSR) and automatic position reports from the aircraft itself (Automatic Dependent Surveillance-Broadcast, ADS-B). SSR as well as ADS-B relies on aircrafts sending transponder signals at a center frequency of 1090 MHz. The reliability and accuracy of the position reports sent by aircrafts using ADS-B are limited and not sufficient to ensure safe airspace separation for example of two aircrafts landing on parallel runways. In the worst case, the data may even be altered with malicious intent. Using passive emitter localization and tracking based on multilateration (TDOA/hyperbolic localization), a precise situational awareness can be given which is independent of the content of the emitted transponder signals. The high concentration of sending targets and the high number of signals require special signal processing and information fusion techniques to overcome the huge amount of data. It will be shown that a multilateration network that employs those techniques can be used to improve airspace security at reasonable costs. For the second case, a concept is introduced which allows TDOA based emitter localization with only one moving observer platform. Conventional TDOA measurements are obtained using spatially distributed sensor nodes which capture an emitted signal at the same time. From those signals, the time difference of arrival is estimated. Under certain conditions, the exploitation of signal structure information allows to transfer the otherwise only spatial into a spatial and temporal measurement problem. This way, it is possible to obtain TDOA estimates over multiple measurement time steps using a single moving observer and to thus localize the emitter of the signals. The concept of direct position determination is applied to the single sensor signal structure TDOA scheme and techniques for direct single sensor TDOA are introduced. The validity and performance of the presented methods is shown in theoretical analysis in terms of Cramér-Rao Lower Bounds, Monte-Carlo simulations and by evaluation of real data gained during field experiments

Coordinated Parallel Runway Approaches

The current air traffic environment in airport terminal areas experiences substantial delays when weather conditions deteriorate to Instrument Meteorological Conditions (IMC). Expected future increases in air traffic will put additional pressures on the National Airspace System (NAS) and will further compound the high costs associated with airport delays. To address this problem, NASA has embarked on a program to address Terminal Area Productivity (TAP). The goals of the TAP program are to provide increased efficiencies in air traffic during the approach, landing, and surface operations in low-visibility conditions. The ultimate goal is to achieve efficiencies of terminal area flight operations commensurate with Visual Meteorological Conditions (VMC) at current or improved levels of safety

Direct and indirect TDOA estimation based multilateration system position estimation accuracy comparison

Multilateration (MLAT) system estimate aircraft position from its electromagnetic emission using time difference of arrival (TDOA) estimated at ground receiving station (GRS)s with a lateration algorithm. The position estimation (PE) accuracy of the MLAT system depends on several factors one of which is the TDOA estimation approach. In this paper, the PE performance of a minimum configuration 3-dimensional (3-D) MLAT system based on the direct and indirect approaches to TDOA estimation is presented. The analysis is carried out using Monte Carlo simulation with the transmitter and receiver parameters based on an actual system used in the civil aviation. Simulation results show that within 150 km radius, the direct TDOA based MLAT system performs better than the indirect TDOA based MLAT system. Beyond 150 km radius, the indirect TDOA based MLAT system has the least PE error compared the direct TDOA based MLAT system. Further comparison of the MLAT system based on the two TDOA estimation approaches with other surveillance systems shows that the direct TDOA based MLAT system has the least PE error within 150 km radius while long-range aircraft PE beyond 150 km, automatic surveillance dependent broadcast (ADS-B) outperformed the MLAT system as it has the least PE error

C-Band Airport Surface Communications System Standards Development. Phase II Final Report. Volume 2: Test Bed Performance Evaluation and Final AeroMACS Recommendations

This report is provided as part of ITT s NASA Glenn Research Center Aerospace Communication Systems Technical Support (ACSTS) contract NNC05CA85C, Task 7: New ATM Requirements-Future Communications, C-Band and L-Band Communications Standard Development and was based on direction provided by FAA project-level agreements for New ATM Requirements-Future Communications. Task 7 included two subtasks. Subtask 7-1 addressed C-band (5091- to 5150-MHz) airport surface data communications standards development, systems engineering, test bed and prototype development, and tests and demonstrations to establish operational capability for the Aeronautical Mobile Airport Communications System (AeroMACS). Subtask 7-2 focused on systems engineering and development support of the L-band digital aeronautical communications system (L-DACS). Subtask 7-1 consisted of two phases. Phase I included development of AeroMACS concepts of use, requirements, architecture, and initial high-level safety risk assessment. Phase II builds on Phase I results and is presented in two volumes. Volume I is devoted to concepts of use, system requirements, and architecture, including AeroMACS design considerations. Volume II (this document) describes an AeroMACS prototype evaluation and presents final AeroMACS recommendations. This report also describes airport categorization and channelization methodologies. The purposes of the airport categorization task were (1) to facilitate initial AeroMACS architecture designs and enable budgetary projections by creating a set of airport categories based on common airport characteristics and design objectives, and (2) to offer high-level guidance to potential AeroMACS technology and policy development sponsors and service providers. A channelization plan methodology was developed because a common global methodology is needed to assure seamless interoperability among diverse AeroMACS services potentially supplied by multiple service providers

Machine-Learning-Aided Trajectory Prediction and Conflict Detection for Internet of Aerial Vehicles

As exploitation of low and medium airspace for air traffic management (ATM) is gaining more attention, aerial vehicles' security issues pose a major challenge to the air-ground-integrated vehicle networks (AGIVNs). Traditional surveillance technology lacks the capacity to support the intensive ATM of the future. Therefore, an advanced automatic-dependent surveillance-broadcast (ADS-B) technique is applied to track and monitor aerial vehicles in a more effective manner. In this article, we propose a grouping-based conflict detection algorithm based on the preprocessed ADS-B data set, and analyze the experimental results and visualize the detected conflicts. Then, in order to further improve flight safety and conflict detection, the trajectories of the aerial vehicles are predicted based on machine learning-based algorithms. The results are fed into the conflict detection algorithm to execute conflict prediction. It was shown that the trajectory prediction model using long short-term memory (LSTM) can achieve better prediction performance, especially when predicting the long-term trajectory of aerial vehicles. The conflict detection results based on the trajectory prediction methods show that the proposed scheme can make it possible to detect whether there would be conflicts within seconds