AirGuard: UAV and Bird Recognition Scheme for Integrated Sensing and Communications System

TL;DR

AirGuard is a novel system combining signal processing and deep learning to accurately distinguish UAVs from birds in low-altitude monitoring, enhancing integrated sensing and communications capabilities.

Contribution

The paper introduces a dual feature fusion CNN for UAV and bird recognition using cmD spectrum and HRRP, with extensive sample generation and simulation validation.

Findings

High recognition accuracy demonstrated in simulations

Effective dual feature fusion approach

Large dataset of 237,600 samples used for training and validation

Abstract

In this paper, we propose an unmanned aerial vehicle (UAV) and bird recognition scheme with signal processing and deep learning for integrated sensing and communications (ISAC) system. We first provide the basic scene of low-altitude targets monitoring, and formulate the motion equations and echo signals for UAVs and birds. Next, we extract the centralized micro-Doppler (cmD) spectrum and the high resolution range profile (HRRP) of the low-altitude target from the echo signals. Then we design a dual feature fusion enabled low-altitude target recognition network with convolutional neural network (CNN), which employs both the images of cmD spectrum and HRRP as inputs to jointly distinguish between UAV and bird. Meanwhile, we generate 237600 cmD and HRRP image samples to train, validate, and evaluate the designed low-altitude target recognition network. The proposed scheme is termed as AirGuard, whose effectiveness has been demonstrated by simulation results.