Information-Optimal Formation Geometry Design for Multimodal UAV Cooperative Perception

TL;DR

The paper addresses how 3D UAV swarm formation geometry governs multimodal perception performance under communication and energy constraints. It introduces an information-theoretic framework based on maximizing the Fisher Information Matrix determinant to jointly optimize UAV-sensor allocation, formation geometry, and flight control, complemented by a formation transformation that preserves estimation accuracy while improving FOV and SINR. A Lyapunov-stable, energy-efficient flight controller with a logarithmic potential field ensures smooth, stable formation transitions. Empirical results show substantial gains in FOV coverage (25%), SINR (104.2%), and energy efficiency (47.2%), supporting the view that geometry should be a primary design variable in next-generation UAV swarms.

Abstract

The efficacy of UAV swarm cooperative perception fundamentally depends on three-dimensional (3D) formation geometry, which governs target observability and sensor complementarity. In the literature, the exploitation of formation geometry and its impact on UAV sensing have rarely been studied, which can significantly degrade multimodal cooperative perception at scenarios where heterogeneous payloads (vision cameras and LiDAR) should be geometrically arranged to exploit their complementary strengths while managing communication interference and hardware budgets. To bridge this critical gap, we propose an information-theoretic optimization framework that allocation of UAVs and multimodal sensors, configures formation geometries, and flight control. The UAV-sensor allocation is optimized by the Fisher Information Matrix (FIM) determinant maximization. Under this framework we introduce an equivalent formation transition strategy that enhances field-of-view (FOV) coverage without compromising perception accuracy and communication interference. Furthermore, we design a novel Lyapunov-stable flight control scheme with logarithmic potential fields to generate energy-efficient trajectories for formation transitions. Extensive simulations demonstrate our formation-aware design achieves 25.0\% improvement in FOV coverage, 104.2\% enhancement in communication signal strength, and 47.2\% reduction in energy consumption compared to conventional benchmarks. This work establishes that task-driven geometric configuration represents a foundational rather than incidental component in next-generation UAV swarm systems.

Figures (12)

• Figure 1: The illustration of the comprehensive framework for the cooperative perception of UAV swarms.
• Figure 2: UAV swarm-based SAR scenario.
• Figure 3: Demonstration of Field of View.
• Figure 4: Formation perception accuracy and marginal gain.
• Figure 5: 3D heterogeneous UAV formation configuration.