Efficient and low-cost localization of radio sources with an autonomous drone
Keywords: Aeronautics and Astronautics.

A radio source is anything that emits radio signals. It might be a signal jammer, a cellphone, a wildlife radio-tag, or the telemetry radio of an unauthorized drone. It is often critical to find these radio sources as quickly as possible. For example, if the radio source is a GPS jammer, it must be found and stopped so nearby users can continue to use GPS signals for navigation. Traditional methods for localizing radio sources are expensive and often labor-intensive. This thesis explores the use of an autonomous drone (a small aircraft) to efficiently localize a single radio source. This thesis takes a holistic approach to the problem, making contributions to both the hardware and algorithms needed to solve it. Because drones offer a low-cost platform to quickly localize radio sources, there has been much research into drone-based radio localization. However, previous work has limitations that this thesis attempts to address. In terms of hardware, previous approaches use sensors that are either inefficient or expensive and complex. In terms of algorithms, most work uses greedy (also called myopic or one-step) optimizations to guide the drone. While these methods can work well, they are generally suboptimal. The first contributions of this thesis relate to hardware. Two sensing modalities are presented and evaluated for drone-based radio source localization. These modalities are simple, easily constructed, inexpensive, and leverage commercial-off-the-shelf components. Despite their simplicity, these modalities outperform sensors commonly used in prior work and are robust to radio sources with unknown or time-varying transmit power. These modalities are validated in simulation and in flight tests localizing a cellphone, a wildlife radio-tag, and another drone by its telemetry radio. Secondly, this thesis makes contributions to the field of principled, multi-step belief-space planning. When performing localization, the drone maintains a belief, or distribution over possible radio source locations. Its goal is to select control inputs that lead to informative sensor measurements and a highly concentrated belief, implying high confidence in its estimate of the radio source's location. This multi-step problem is cast as a partially observable Markov decision process (POMDP). This thesis expands on recent work to incorporate belief-dependent rewards in offline POMDP solvers. In this respect, the chief contribution of this thesis is an improved lower bound that greatly reduces computation. Despite this improvement, it was found that offline solvers could not scale to handle realistic scenarios. To solve the problem in real-time, an online POMDP solver based on Monte Carlo tree search is used. In simulations, this method outperforms a greedy method in a multi-objective localization problem where the seeker drone must avoid near-collisions with a moving radio source. This method was implemented in a flight test localizing another drone by its telemetry radio. The third set of contributions made by this thesis relate to ergodic control for information gathering, in which a sensing agent selects trajectories that are ergodic with respect to an information distribution. This thesis briefly explores the conditions under which ergodic control might be optimal. Ergodic control is shown to be the optimal information gathering strategy for a class of problems which unfortunately does not include drone-based radio localization. In another contribution, it is shown how neural networks can quickly generate information maps, a key step to generating ergodic trajectories. The resulting approximations are accurate and yield orders of magnitude reduction in computation, allowing information maps to be generated in real-time. Finally, simulations are used to evaluate ergodic control in drone-based radio source localization. While the resulting performance depends on the method used to generate ergodic trajectories, ergodic control can offer modest improvements over greedy methods in nominal conditions and greater improvements in the presence of significant unmodeled noise.