Multi-Agent Inverse Learning for Sensor Networks: Identifying Coordination in UAV Networks

TL;DR

This paper introduces a method for radar systems to detect coordination and infer objectives of UAV networks by modeling their interactions as a multi-objective optimization problem and applying inverse microeconomic analysis.

Contribution

It presents a novel framework combining abstract interpretation and microeconomic theory for inverse multi-objective optimization in UAV network detection.

Findings

Successfully detects UAV coordination from radar signals

Reconstructs individual UAV objectives using inverse optimization

Links microeconomic models to radar waveform analysis

Abstract

Suppose there is an adversarial UAV network being tracked by a radar. How can the radar determine whether the UAVs are coordinating, in some well-defined sense? How can the radar infer the objectives of the individual UAVs and the network as a whole? We present an abstract interpretation of such a strategic interaction, allowing us to conceptualize coordination as a linearly constrained multi-objective optimization problem. Then, we present some tools from microeconomic theory that allow us to detect coordination and reconstruct individual UAV objective functions, from radar tracking signals. This corresponds to performing inverse multi-objective optimization. We present details for how the abstract microeconomic interpretation corresponds to, and naturally arises from, physical-layer radar waveform modulation and multi-target filtering. This article serves as a tutorial, bringing together concepts from several established research contributions in an expository style.