Phase-Time Array Enabled Multistatic Sensing with Multi-Level Fusion for UAV Localization

Abstract

In multistatic collaborative sensing, existing parameter-level fusion methods suffer from severe error amplification in geometrically unfavorable regions. Conventional receiver architectures present an additional challenge: analog phased arrays incur substantial beam sweeping overhead, while fully digital arrays demand prohibitive hardware costs. This paper proposes a multistatic sensing framework based on a phase-time array (PTA), whose rainbow beamforming maps spatial directions to OFDM subcarriers, achieving wide-angle coverage with a single RF chain and a favorable accuracy-complexity trade-off relative to both phased array and fully digital receivers. To suppress geometry-induced error hotspots, two parameter-level fusion schemes are developed: a GDOP-weighted analytical estimator (GDOP-WLS) and a lightweight multilayer perceptron (PF-MLP). An end-to-end signal-level convolutional neural network (SF-CNN) is further designed to directly regress target coordinates from raw received signals, bypassing cascaded parameter estimation. Simulation results show that the parameter-level schemes effectively mitigate localized error amplification with minimal computational overhead, while SF-CNN attains sub-meter positioning accuracy, at the cost of higher model complexity that can be offset by GPU acceleration. Power-matched training further extends both schemes' accuracy across a wide power range.

arXiv comment6 pages, 5 figures, 3 tables. Accepted to IEEE GLOBECOM 2026 Workshops. v2: accepted version; adds a receiver-architecture comparison, a processing-framework figure, and funding information, and updates the simulation results. Code available at: https://github.com/SJTU-WirelessAI-Lab/PTA-Multistatic-UAV-Localization