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

UAS Surveillance Criticality

The integration of unmanned aircraft systems (UAS) into the national airspace system (NAS) poses considerable challenges. Maintaining human safety is perhaps chief among these challenges as UAS remote pilots will need to interact with other UAS, piloted aircraft, and other conditions associated with flight. A research team of 6 leading UAS research universities was formed to respond to a set of surveillance criticality research questions. Five analysis tools were selected following a literature review to evaluate airborne surveillance technology performance. The analysis tools included: Fault Trees, Monte Carlo Simulations, Hazard Analysis, Design of Experiments (DOE), and Human-in-the-Loop Simulations. The Surveillance Criticality research team used results from these analyses to address three primary research questions and provide recommendations for UAS detect-and-avoid mitigation and areas for further research

Development of Aerial-Ground Sensing Network: Architecture, Sensor Activation, and Spatial Path-Energy Optimization

Title from PDF of title page viewed May 13, 2019Dissertation advisor: ZhiQiang ChenVitaIncludes bibliographical references (pages 101-110)Thesis (Ph.D.)--School of Computing and Engineering. University of Missouri--Kansas City, 2019The advent of autonomous navigation, positioning, and in general robotics technologies has enabled the maturity of small to miniature-sized unmanned aerial vehicles (UAVs; or colloquially called drones) and their wide use in engineering practice as a low-cost and effective geospatial remote sensing solution. Meanwhile, wireless sensing network technology (WSN) has also matured in recent years with many applications found in engineering practice. In this dissertation, a novel aerial ground wireless sensing network (AG-WSN) is developed, which is expected to transform a number of critical geospatial sensing and monitoring practices, such as precision agriculture, civil infrastructure protection, and disaster response. Towards the maximal energy efficiency, three research problems are concerned in this dissertation. First, a radio-frequency (RF) wake-up mechanism is investigated for aerial activation of ground sensors using a UAV platform. Second, the data transmission under wireless interference between the UAV and ground WSN is experimentally investigated, which suggests practical relations and parameters for aerial-ground communication configuration. Last, this dissertation theoretically explores and develops an optimization framework for UAV's aerial path planning when collecting ground-sensor data. An improved mixed-integer non-linear programming approach is proposed for solving the optimal spatial path-energy using the framework of the traveling-salesman problem with neighborhoods.Introduction -- Development of radio-frequency sensor wake-up through UAV as an aerial gateway -- Experimental investigation of aerial-ground network communication towards geospatially large-scale structural health monitoring -- Spatial path-energy optimization for tactic unmanned aerial vehicles operation in aerial-ground networking -- Conclusion and future wor

Supporting Validation of UAV Sense-and-Avoid Algorithms with Agent-Based Simulation and Evolutionary Search

A Sense-and-Avoid (SAA) capability is required for the safe integration of Unmanned Aerial Vehicles (UAVs) into civilian airspace. Given their safety-critical nature, SAA algorithms must undergo rigorous verification and validation before deployment. The validation of UAV SAA algorithms requires identifying challenging situations that the algorithms have difficulties in handling. By building on ideas from Search-Based Software Testing, this thesis proposes an evolutionary-search-based approach that automatically identifies such situations to support the validation of SAA algorithms. Specifically, in the proposed approach, the behaviours of UAVs under the control of selected SAA algorithms are examined with agent-based simulations. Evolutionary search is used to guide the simulations to focus on increasingly challenging situations in a large search space defined by (the variations of) parameters that configure the simulations. An open-source tool has been developed to support the proposed approach so that the process can be partially automated. Positive results were achieved in a preliminary evaluation of the proposed approach using a simple two-dimensional SAA algorithm. The proposed approach was then further demonstrated and evaluated using two case studies, applying it to a prototype of an industry-level UAV collision avoidance algorithm (specifically, ACAS XU) and a multi-UAV conflict resolution algorithm (specifically, ORCA-3D). In the case studies, the proposed evolutionary-search-based approach was empirically compared with some plausible rivals (specifically, random-search-based approaches and a deterministic-global-search-based approach). The results show that the proposed approach can identify the required challenging situations more effectively and efficiently than the random-search-based approaches. The results also show that even though the proposed approach is a little less competitive than the deterministic-global-search-based approach in terms of effectiveness in relatively easy cases, it is more effective and efficient in more difficult cases, especially when the objective function becomes highly discontinuous. Thus, the proposed evolutionary-search-based approach has the potential to be used for supporting the validation of UAV SAA algorithms although it is not possible to show that it is the best approach