Safety-Aware Forward Detection in Networked ISAC for Low-Altitude UAV Flight

2026-07-15

AI Summary

This study addresses the insufficient reliability of forward non-cooperative target detection for low-altitude unmanned aerial vehicles, which poses a critical threat to flight safety. Within a networked integrated sensing and communication (ISAC) framework, the authors dynamically define the forward region of interest based on the safety braking distance and employ voxel-based modeling to jointly optimize the sensing pilot ratio, transmit power, and beam direction. This approach enhances both state estimation accuracy and forward detection reliability while maintaining required communication rates. The work further reveals, for the first time, that the Cramér–Rao lower bound (CRLB) of state estimation and the miss-detection probability decay logarithmically squared and exponentially, respectively, with the number of cooperative base stations. A resource allocation mechanism balancing communication, sensing, and safety is proposed, achieving a 17.05% reduction in average miss-detection probability and collision risk compared to a baseline without forward detection, at the cost of only a 14.82% increase in CRLB.

Abstract

Networked integrated sensing and communication (ISAC) exploits cooperation among multiple ground base stations (GBSs) to support safe uncrewed aerial vehicle (UAV) flight in low-altitude wireless networks (LAWNs). Existing studies mainly focus on communication enhancement or target parameter estimation, while the detection reliability of non-cooperative targets in the UAV forward region remains insufficiently investigated. To address this issue, this paper proposes a safety-aware forward detection design in networked ISAC, where multiple GBSs jointly support UAV downlink communication, state estimation, and non-cooperative target detection within the forward region of interest (ROI). First, the forward ROI is determined by the UAV position, velocity, and safe braking distance, and is voxelized to characterize target-existence states. Then, the Cramér-Rao lower bound (CRLB) for UAV state estimation and the forward-ROI miss-detection probability are derived, and their scaling laws are characterized: In detail, the UAV state-estimation CRLB approximately decreases as $\ln^{-2}J$ with the number of cooperative GBSs $J$, while the forward-ROI miss-detection probability follows an exponential-form scaling law as $λ_{t}D_{f}\ln^{-2}J$. Furthermore, a safety-aware resource optimization problem is formulated to jointly configure the sensing pilot ratio, transmit power, and beam direction, balancing UAV state-estimation performance and forward detection reliability under the communication-rate constraint. Simulation results show that, compared with the baseline scheme without forward detection, the proposed design reduces the average miss-detection probability and the corresponding sensing-induced collision risk by $17.05\%$, while introducing only limited state-estimation performance degradation, reflected by a $14.82\%$ increase in the average CRLB.

Problem

• forward detection
• non-cooperative targets
• UAV safety
• low-altitude UAV flight
• detection reliability

Innovation

• safety-aware detection
• networked ISAC
• forward region of interest
• Cramér-Rao lower bound
• miss-detection probability