| Rapidly increasing growth and demand in various unmanned aerial vehicles (UAV) have pushed governmental regulation development and numerous technology research advances toward integrating unmanned and manned aircraft into the same civil airspace. Safety of other airspace users is the primary concern; thus, with the introduction of UAV into the National Airspace System (NAS), a key issue to overcome is the risk of a collision with manned aircraft. The challenge of UAV integration is global. As automatic dependent surveillance-broadcast (ADS-B) system has gained wide acceptance, additional exploitations of the radioed satellite-based information are topics of current interest. One such opportunity includes the augmentation of the communication ADS-B signal with a random bi-phase modulation for concurrent use as a radar signal for detecting other aircraft in the vicinity. This dissertation provides detailed discussion about the ADS-B radar system, as well as the formulation and analysis of a suitable non-cooperative multi-target tracking method for the ADS-B radar system using radar ranging techniques and particle filter algorithms.;In order to deal with specific challenges faced by the ADS-B radar system, several estimation algorithms are studied. Trilateration-based localization algorithms are proposed due to their easy implementation and their ability to work with coherent signal sources. The centroid of three most closely spaced intersections of constant-range loci is conventionally used as trilateration estimate without rigorous justification. In this dissertation, we address the quality of trilateration intersections through range scaling factors. A number of well-known triangle centers, including centroid, incenter, Lemoine point (LP), and Fermat point (FP), are discussed in detail. To the author’s best knowledge, LP was never associated with trilateration techniques. According our study, LP is proposed as the best trilateration estimator thanks to the desirable property that the total distance to three triangle edges is minimized. It is demonstrated through simulation that LP outperforms centroid localization without additional computational load. In addition, severe trilateration scenarios such as two-intersection cases are considered in this dissertation, and enhanced trilateration algorithms are proposed.;Particle filter (PF) is also discussed in this dissertation, and a simplified resampling mechanism is proposed. In addition, the low-update-rate measurement due to the ADS-B system specification is addressed in order to provide acceptable estimation results. Supplementary particle filter (SPF) is proposed to takes advantage of the waiting time before the next measurement is available and improves the estimation convergence rate and estimation accuracy. While PF suffers from sample impoverishment, especially when the number of particles is not sufficiently large, SPF allows the particles to redistribute to high likelihood areas over iterations using the same measurement information, thereby improving the estimation performance. |
