KEYWORDS: Detection and tracking algorithms, Defense and security, Data modeling, Systems modeling, System identification, Intelligence systems, New and emerging technologies, Network architectures, Defense systems
Robust defenses to the new threats require early determination of the adversary’s plan of attack. At the same time, automated path-planning systems often behave predictably and produce paths with recognizable characteristic features. The increasing adoption of autonomous systems changes the defense landscape, thwarting traditional defenses with rapid decision speeds, but opening up new weaknesses. In this work, we investigate several possibilities to exploit artifacts of path planning algorithms that might be used as tells, giving a defending commander early warning and advantage in mounting a defense. With the application of an integrated air-defense system in mind, we examine a use case of several high-value targets which are obstructed by known defending threats. We use incoming time-series track data to predict the most probable targets and future trajectories of an enemy platform. One such approach is to attempt to directly learn the mapping from threat track to target. However, such an approach is likely brittle, requiring large volumes of data and substantial retraining for each target laydown. By contrast, we attempt to exploit predictable features from the path-planning algorithm itself, by first classifying the path-planning algorithm being used, and including that knowledge in our target prediction algorithm. We demonstrate that it is possible to differentiate classes of path planning algorithms with high accuracy based on track data alone. Utilizing the underlying model, we can then predict likely track updates and likely targets. We discuss strengths and limitations of this approach with respect to the aim of adding a robust tool to the air-defense use case.
KEYWORDS: Mobile communications, Data communications, Telecommunications, Neural networks, Binary data, Stochastic processes, Receivers, Probability theory, Systems modeling
This paper determines optimal strategies for transmitting messages in a mobile ad-hoc network (MANET) in a communications-limited and lossy communications environment. When an agent generates or receives a message it must decide to which neighbors, and how many times, that message is to be passed. The opposing goals are to (1) propagate all messages throughout the MANET quickly and (2) to minimize the total number of messages sent. We compare two optimized decision strategies for the agents, reinforcement learning (RL) and game theory (GT) methods. For the RL framework, each node in the MANET acts as a reinforcement learning agent who must learn optimal decisions for when to send messages to whom. For the GT framework, we create a game tree where the nodes encompass message knowledge and connectivity information, and the decision branches represent sending messages to neighbors. We solve the game using a Monte-Carlo Tree Search (MCTS) variation to determine the probability that a message is sent to a neighbor. Performance is assessed in terms of the total number of messages sent, and the length of time for a given percentage of messages to reach a given percentage of nodes. Experiments with MANETs of varying size and connectivity are considered, and the RL and GT performance and training speed are compared. The decision strategies are domain agnostic and may be applied to ground, air, surface, sub-surface, or satellite networks.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.