Testing autonomous navigation algorithms of Unmanned Aerial Vehicles (UAVs) in real-world scenarios often entails significant safety risks. In this paper, we aim to build a flexible yet user-friendly UAV autonomous navigation simulator. Ideally, it should closely emulate real-world environments, support diverse UAV models and algorithms, and provide a flexible evaluation framework. Existing frameworks fail to satisfy all three requirements simultaneously. To this end, we present AirNavigation, an integrated simulation platform designed to support the end-to-end workflow of UAV navigation research. Specifically, our system leverages Unreal Engine to simulate highly realistic environments and diverse UAV models. It further facilitates semi-automated scene generation and multi-modal synthetic training data production. To lower the barrier of adoption, we develop a suite of user-friendly interfaces to enable seamless integration of diverse navigation algorithms. Moreover, we introduce a novel evaluation system powered by large language models to deliver personalized and fine-grained performance analysis.

Six-dimensional movable antenna (6DMA) technology has been proposed to enhance the performance of integrated sensing and communication (ISAC) systems, but its increased implementation complexity poses significant challenges due to the extensive modifications required to existing base station infrastructures. In recent years, rotatable antenna has emerged as a promising technology, offering great potential for improving both wireless communication and sensing performance by flexibly adjusting the directional antenna’s pointing. However, current research on rotatable antenna is still in its infancy, with the focus primarily on wireless communication systems, and its application has not yet been extended to ISAC systems. As a representative application of rotatable antenna, the rotatable array (RA) is more practically meaningful and implementable in engineering practice. Additionally, the sub-connected structure in hybrid beamforming, where each radio-frequency chain is connected to only a subset of antenna elements, offers higher energy efficiency and is easier to realize in practice. Given this, this paper focuses on a channel model that accounts for the efficiency of the antenna radiation pattern and studies the sub-connected hybrid beamforming design for multi-user RA-aided ISAC system, aiming to reduce hardware cost and complexity while improving communication rate and sensing accuracy. Aiming at the non-convex nature with coupled variables in this problem, this paper transforms the complex fractional objective function using the fractional programming (FP) method, and then proposes an algorithm based on the alternating optimization (AO) framework, which achieves optimization by alternately solving five subproblems. Extensive simulation results demonstrate the effectiveness of the proposed RA-aided hybrid beamforming design method. It not only significantly improves the overall system performance while reducing hardware costs, but also achieves performance comparable to that of the fully-digital beamforming design with fixed-position antennas (FPA) under specific parameter configurations.

The development of multi-modal Unmanned Aerial Vehicles (UAVs) environment perception systems is hindered by three critical gaps in existing datasets: (1) insufficient modalities and pixel misalignment, (2) noisy labels, and (3) limited task types. To address these gaps, we propose an automatic data construction approach and construct a multi-modal UAV-based environment perception dataset, UEMM-Air. Its synthetic nature ensures scalability, reproducibility, and rare-event coverage, making it suitable for large-scale model pre-training. Benefiting from our automated data collection and annotation pipeline, UEMM-Air encompasses 120k data pairs across 6 aligned modalities and supports 4 perception tasks, significantly exceeding existing datasets (max 60k data, 3 modalities, 2 tasks). Compared to existing synthetic datasets like SynDrone, UEMM-Air provides more accurate annotations by avoiding noisy labels from direct coordinate computation. Notably, models pre-trained on UEMM-Air achieve a 5.8% accuracy improvement compared to those utilizing other synthetic datasets, while requiring less than half the data. This benchmark establishes performance evaluation of UAV multi-modal environmental perception models, and hopefully encourages more research efforts towards enabling UAVs to undertake more multi-modal tasks. The dataset and its generation engine are openly accessible under a permissive license at https://github.com/1e12Leon/UEMM-Air.