[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-78199":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":15,"subscribersCount":15,"size":15,"stars1d":14,"stars7d":16,"stars30d":17,"stars90d":15,"forks30d":15,"starsTrendScore":18,"compositeScore":19,"rankGlobal":10,"rankLanguage":10,"license":10,"archived":20,"fork":20,"defaultBranch":21,"hasWiki":20,"hasPages":20,"topics":22,"createdAt":10,"pushedAt":10,"updatedAt":35,"readmeContent":36,"aiSummary":37,"trendingCount":15,"starSnapshotCount":15,"syncStatus":14,"lastSyncTime":38,"discoverSource":39},78199,"SimART","guchuanv-alt\u002FSimART","guchuanv-alt","An open-source software platform for all-scenario wireless communication and sensing research","",null,"C++",252,13,2,0,104,141,6,3.44,false,"main",[23,24,25,26,27,28,29,30,31,32,33,34],"airsim","beamforming","ckm","communication","drone","raytracing","robotics","ros","simulation","sionna","uav","unreal-engine","2026-06-12 02:03:46","\u003Cdiv align=\"center\">\n\n\u003Cp>\n  \u003Cimg src=\"Tutorials\u002Fimages\u002Fsimart_wordmark.svg\" alt=\"SimART\" width=\"420\">\n\u003C\u002Fp>\n\n\u003Cp>\u003Cstrong>An open-source software platform for all-scenario wireless communication and sensing research.\u003C\u002Fstrong>\u003C\u002Fp>\n\n\u003Cp>\n  \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2605.13309\">\n    \u003Cimg alt=\"Paper\" src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FPaper-arXiv%202605.13309-9F1239?style=for-the-badge&logo=arxiv&logoColor=white&labelColor=0F172A\">\n  \u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Fkangyan.lat\u002Fsimart\u002F\">\n    \u003Cimg alt=\"Homepage\" src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FHomepage-SimART-0E7490?style=for-the-badge&logo=googlechrome&logoColor=white&labelColor=0F172A\">\n  \u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Fwww.bilibili.com\u002Fvideo\u002FBV12e5R6BEqV\u002F\">\n    \u003Cimg alt=\"Demo Video\" src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FDemo-Bilibili-00A1D6?style=for-the-badge&logo=bilibili&logoColor=white&labelColor=0F172A\">\n  \u003C\u002Fa>\n\u003C\u002Fp>\n\n\u003C\u002Fdiv>\n\n---\n\nSimART is an open-source software platform for all-scenario wireless communication and sensing research. Built around ROS1, it integrates a C++\u002FQt\u002FVTK graphical interface, Sionna-based ray tracing and link simulation, AirSim\u002FUnreal Engine live visualization, and rosbag recording and replay tools. It helps users place base stations, visualize trajectories, inspect wireless channel observations, and evaluate beam selection workflows in 3D scenes.\n\nIt supports digital-twin scene construction, ROS-based trajectory replay, Sionna data collection, visual network planning, and communication-sensing experiments with simulators such as AirSim and Unreal Engine.\n\n## Contents\n\n- [System Architecture](#system-architecture)\n- [Scene Construction and Map Adaptation](#scene-construction-and-map-adaptation)\n- [Multimodal Data and CKM](#multimodal-data-and-ckm)\n- [Key Features](#key-features)\n- [Preview](#preview)\n- [Tutorials](#tutorials)\n- [Quick Start](#quick-start)\n- [Try SimART](#try-simart)\n- [Use SimART with UE4 and AirSim](#use-simart-with-ue4-and-airsim)\n- [Further Exploration](#8-further-exploration)\n\n## System Architecture\n\n\u003Cp align=\"center\">\n  \u003Cimg src=\"Tutorials\u002Fimages\u002Fsystem_architecture.png\" alt=\"SimART system architecture\" width=\"100%\">\n\u003C\u002Fp>\n\nSimART consists of four functional modules coordinated by ROS:\n\n| Module | Role |\n| --- | --- |\n| Physics and Sensing Module | Provides platform motion, RGB\u002Fdepth\u002Fsemantic cameras, LiDAR, IMU, GPS, and ground-truth poses through ROS-compatible simulators such as AirSim, Gazebo, Isaac Sim, or CARLA. |\n| Ray Tracing Module | Uses Sionna RT to compute site-specific propagation paths, delays, angles, Doppler shifts, interaction points, and channel impulse responses. |\n| Link and System Module | Uses Sionna SYS to evaluate OFDM, PHY\u002FMAC behavior, multi-antenna links, beamforming codebooks, SINR, BLER, achievable rate, and optimal beam index. |\n| CKM Generator | Scans dense receiver grids to generate multi-layer channel knowledge maps for path loss, delay\u002Fangular spread, SINR, rate, and beam-selection priors. |\n\nThe ray tracing module supports online simulation, offline rosbag replay, and dense grid scan modes. This allows users to visualize propagation during a live session, reproduce experiments from recorded trajectories, or generate CKM layers over a region of interest.\n\n## Scene Construction and Map Adaptation\n\nSimART provides two complementary scene construction pipelines. Both produce a high-fidelity visual scene for physics\u002Fsensing and a simplified, material-aware scene for ray tracing, while preserving a shared coordinate frame.\n\n\u003Ctable>\n  \u003Ctr>\n    \u003Ctd align=\"center\">\n      \u003Cimg src=\"Tutorials\u002Fimages\u002Flouvre_map_pipeline.png\" alt=\"Real-world OpenStreetMap based scene construction pipeline\" width=\"100%\">\n    \u003C\u002Ftd>\n  \u003C\u002Ftr>\n  \u003Ctr>\n    \u003Ctd align=\"center\">\u003Csub>Real-world map adaptation: OpenStreetMap data are converted into aligned visual and ray-tracing assets.\u003C\u002Fsub>\u003C\u002Ftd>\n  \u003C\u002Ftr>\n  \u003Ctr>\n    \u003Ctd align=\"center\">\n      \u003Cimg src=\"Tutorials\u002Fimages\u002Froadrunner_map_pipeline.png\" alt=\"User-defined RoadRunner scene construction pipeline\" width=\"100%\">\n    \u003C\u002Ftd>\n  \u003C\u002Ftr>\n  \u003Ctr>\n    \u003Ctd align=\"center\">\u003Csub>User-defined scene interface: RoadRunner or Unreal Engine scenes are simplified into propagation-ready meshes.\u003C\u002Fsub>\u003C\u002Ftd>\n  \u003C\u002Ftr>\n\u003C\u002Ftable>\n\n| Pipeline | Workflow |\n| --- | --- |\n| Real-world map adaptation | OpenStreetMap extracts provide building footprints, heights, roads, and land-use polygons. OSM2World creates the visual asset for Unreal Engine\u002FAirSim, while Blender exports a Mitsuba\u002FSionna RT scene with electromagnetic material annotations. |\n| User-defined scene interface | RoadRunner, Unreal Engine, or other custom scene assets are imported for physics and sensing. A Blender conversion script removes fine visual details, applies mesh decimation, preserves dominant facades and ground planes, and assigns Sionna RT materials. |\n\n## Multimodal Data and CKM\n\nDuring a simulation session, every module publishes data under the shared ROS clock. A single rosbag can preserve the complete synchronized session for replay, inspection, dataset export, and downstream learning tasks.\n\n\u003Ctable>\n  \u003Ctr>\n    \u003Ctd align=\"center\" width=\"50%\">\n      \u003Cimg src=\"Tutorials\u002Fimages\u002Fmultimodal_uav_sensor_streams.png\" alt=\"Multimodal UAV sensor streams\" width=\"100%\">\n    \u003C\u002Ftd>\n    \u003Ctd align=\"center\" width=\"50%\">\n      \u003Cimg src=\"Tutorials\u002Fimages\u002Fmultimodal_ckm_layers.png\" alt=\"Dense CKM layers generated by SimART\" width=\"100%\">\n    \u003C\u002Ftd>\n  \u003C\u002Ftr>\n  \u003Ctr>\n    \u003Ctd align=\"center\">\u003Csub>Multimodal sensor streams: RGB, depth, LiDAR, and semantic segmentation.\u003C\u002Fsub>\u003C\u002Ftd>\n    \u003Ctd align=\"center\">\u003Csub>Dense CKM layers: path loss, received power, best BS rate, and effective SINR.\u003C\u002Fsub>\u003C\u002Ftd>\n  \u003C\u002Ftr>\n\u003C\u002Ftable>\n\n| Data Stream | Examples |\n| --- | --- |\n| Sensing and mobility | RGB images, depth images, semantic images, LiDAR point clouds, IMU, GPS, ground-truth poses, and navigation messages. |\n| Wireless channel | Per-link CIRs, propagation paths, path loss, delays, angles of arrival\u002Fdeparture, Doppler, and interaction points. |\n| Link\u002Fsystem KPIs | SINR, BLER, achievable rate, PHY\u002FMAC outputs, and optimal beam index from a configured codebook. |\n| CKM layers | Path loss, RMS delay spread, angular spread, average SINR, achievable rate, and beam-selection priors over dense spatial grids. |\n\n## Key Features\n\n| Capability | Description |\n| --- | --- |\n| 3D scene loading and preview | Load local scene meshes in the GUI to inspect maps, UAV trajectories, base stations, and ray paths. |\n| Sionna wireless simulation | Run ray tracing, OFDM\u002FSYS link adaptation, and beam codebook selection from exported Mitsuba\u002FXML scenes. |\n| ROS1 data-stream integration | Subscribe to UAV pose topics and publish RF, SYS, beam, codebook, and related simulation observations. |\n| AirSim\u002FUE live integration | Display UAVs, trajectories, base stations, and rays directly in Unreal Engine scenes, with support for base-station camera previews. |\n| Base-station editing and configuration | Add, select, edit, save, and load base-station JSON configurations from the GUI. |\n| Rosbag tools | Record, replay, and re-simulate wireless data for offline analysis and dataset generation. |\n| Coordinate-frame configuration | Configure transforms among the ROS frame, 3D scene frame, and AirSim frame. |\n\n## Preview\n\n\u003Ctable>\n  \u003Ctr>\n    \u003Ctd align=\"center\" width=\"33%\">\n      \u003Cimg src=\"Tutorials\u002Fimages\u002Fplanform.gif\" alt=\"SimART main simulation interface\" width=\"100%\">\n    \u003C\u002Ftd>\n    \u003Ctd align=\"center\" width=\"33%\">\n      \u003Cimg src=\"Tutorials\u002Fimages\u002Fdrone_view.gif\" alt=\"UAV view and ray tracing visualization\" width=\"100%\">\n    \u003C\u002Ftd>\n    \u003Ctd align=\"center\" width=\"33%\">\n      \u003Cimg src=\"Tutorials\u002Fimages\u002Fbase_station.gif\" alt=\"Base-station layout and preview\" width=\"100%\">\n    \u003C\u002Ftd>\n  \u003C\u002Ftr>\n  \u003Ctr>\n    \u003Ctd align=\"center\">\u003Csub>Main Simulation Interface\u003C\u002Fsub>\u003C\u002Ftd>\n    \u003Ctd align=\"center\">\u003Csub>UAV Perspective\u003C\u002Fsub>\u003C\u002Ftd>\n    \u003Ctd align=\"center\">\u003Csub>Base-Station Perspective\u003C\u002Fsub>\u003C\u002Ftd>\n  \u003C\u002Ftr>\n\u003C\u002Ftable>\n\n## Tutorials\n\nThe following brings you a quick start. For other tutorials, click here:\n\n| Tutorial | Link |\n| --- | --- |\n| Create Your Own Maps | [Tutorials\u002FCreateMap\u002FCreateMap.md](Tutorials\u002FCreateMap\u002FCreateMap.md) |\n| Use SimART with Your Own Maps | [Tutorials\u002FUsage\u002FUsage.md](Tutorials\u002FUsage\u002FUsage.md) |\n\n## Quick Start\n\n### Building and Compiling\n\nThis project is a ROS 1 catkin workspace package set and was tested under\n`Ubuntu 20.04 + ROS 1 Noetic`. We recommend working with the same environment.\nThe repository should be placed under a catkin workspace, for example:\n\n```bash\nmkdir -p ~\u002Fcatkin_ws\u002Fsrc\ncd ~\u002Fcatkin_ws\u002Fsrc\ngit clone https:\u002F\u002Fgithub.com\u002Fguchuanv-alt\u002FSimART.git\n```\n\nThe repository contains these catkin packages:\n\n| Package | Description |\n| --- | --- |\n| `airsim_gui_UErealtime` | the C++\u002FQt\u002FVTK GUI package |\n| `rf_msgs` | RF observation message definitions |\n| `sionna_sys_msgs` | Sionna SYS message definitions |\n| `sionna_beam_msgs` | Sionna beam\u002Fcodebook message definitions |\n\n### 1. System and ROS Dependencies\n\nInstall ROS 1 Noetic for Ubuntu 20.04 first.\n\nWe recommend following the official ROS Noetic installation instructions for\nUbuntu 20.04:\n\u003Chttp:\u002F\u002Fwiki.ros.org\u002Fnoetic\u002FInstallation\u002FUbuntu>\n\nIf you prefer to use the third-party FishROS convenience installer, treat it\nas an optional alternative and use the HTTPS endpoint:\n\u003Chttps:\u002F\u002Fgithub.com\u002Ffishros\u002Finstall>\n\n```bash\nwget https:\u002F\u002Ffishros.com\u002Finstall -O fishros && bash fishros\n```\n\nAfter ROS 1 Noetic is installed, source the ROS environment and install the\nremaining system dependencies:\n\n```bash\nsource \u002Fopt\u002Fros\u002Fnoetic\u002Fsetup.bash\nsudo apt update\nsudo apt install -y \\\n  build-essential \\\n  cmake \\\n  python3-pip \\\n  python3-venv \\\n  python3-rosdep \\\n  python3-catkin-tools \\\n  qtbase5-dev \\\n  qtdeclarative5-dev \\\n  libqt5opengl5-dev \\\n  qml-module-qtquick2 \\\n  libassimp-dev \\\n  libvtk7-dev\n```\n\nIf your Ubuntu version does not provide `libvtk7-dev`, install the available\nVTK development package instead, for example:\n\n```bash\nsudo apt install -y libvtk9-dev\n```\n\nThen install ROS package dependencies declared by the catkin packages:\n\n```bash\ncd ~\u002Fcatkin_ws\nsudo rosdep init 2>\u002Fdev\u002Fnull || true\nrosdep update\nrosdep install --from-paths src\u002FSimART --ignore-src -r -y\n```\n\n`rosdep` can now install the Qt, VTK, and Assimp dependencies declared by\n`SimART_GUI\u002Fpackage.xml`. The explicit `apt install` command above is still\nlisted so a new machine has the core tools before running `rosdep`.\n\n### 2. Python Dependencies\n\nWe recommend installing the Python dependencies with conda. The tested conda\nenvironment is named `SimART` and uses Python 3.12:\n\n```bash\ncd ~\u002Fcatkin_ws\u002Fsrc\u002FSimART\nconda create -n SimART python=3.12\nconda activate SimART\npython -m pip install --upgrade pip\npython -m pip install -r requirements.txt\n```\n\n`requirements.txt` includes sionna. You can also refer to [Sionna](https:\u002F\u002Fgithub.com\u002FNVlabs\u002Fsionna.git) to install it.\n\n### 3. Build the Default GUI\n\nBuild the default GUI with `catkin build`:\n\n```bash\ncd ~\u002Fcatkin_ws\nsource \u002Fopt\u002Fros\u002Fnoetic\u002Fsetup.bash\ncatkin init\ncatkin config --extend \u002Fopt\u002Fros\u002Fnoetic --cmake-args -DCMAKE_BUILD_TYPE=RelWithDebInfo\ncatkin build airsim_gui_UErealtime\nsource devel\u002Fsetup.bash\n```\n\nRun SimART:\n\n```bash\nrosrun airsim_gui_UErealtime airsim_gui_UErealtime\n```\n\n## Try SimART\n\n4 sample maps and 1 sample rosbag for one of them are provided. The quick-start\ndemo uses a UAV trajectory in BigCitySample. The sample rosbag already contains\nthe UAV pose data recorded from AirSim, so it can be used directly for\nsimulation without installing AirSim first. The rest of the maps can be used\nfor further exploration.\n\n### 4. Download the Sample Maps and Rosbags\n\nIn the root directory of the repository you just cloned, run:\n\n```bash\ncd ~\u002Fcatkin_ws\u002Fsrc\u002FSimART\nchmod +x download_sample_maps.sh\n.\u002Fdownload_sample_maps.sh\n```\n\nand:\n\n```bash\ncd ~\u002Fcatkin_ws\u002Fsrc\u002FSimART\nchmod +x download_sample_rosbags.sh\n.\u002Fdownload_sample_rosbags.sh\n```\n\nThe folder SimART_sample_maps contains 4 sample maps.\u003Cbr>\nThe folder SimART_sample_rosbags contains 1 rosbag for the map BigCitySample.\n\n### 5. Load the Config File in SimART and Start the Simulation\n\nRun SimART by using the command:\n\n```bash\nrosrun airsim_gui_UErealtime airsim_gui_UErealtime\n```\n\n- In SimART, click \"Open Existing Config\" and select `~\u002Fcatkin_ws\u002Fsrc\u002FSimART\u002FSimART_sample_maps\u002FBigCitySample\u002FBigCitySample.agcfg`.\n- Click \"Simulation Settings\", select the `SimART` conda environment in the Python environment field, and click \"Test Environment\".\n\n\u003Cp align=\"center\">\n  \u003Cimg src=\"Tutorials\u002Fimages\u002FSwitchPythonEnv.png\" alt=\"Select the SimART Python environment in Simulation Settings\" width=\"100%\">\n\u003C\u002Fp>\n\n- Click \"Open Rosbag Tools\", select the downloaded sample rosbag in Replay panel. Click \"Start Playback\".\n\n\u003Cp align=\"center\">\n  \u003Cimg src=\"Tutorials\u002Fimages\u002FOpenRosbag.png\" alt=\"Open the sample rosbag in Rosbag Tools\" width=\"100%\">\n\u003C\u002Fp>\n\n- Click \"Start Simulation\" to start the Sionna simulation. RF, SYS, and beam simulation will all be started. The data can be viewed in the Wireless Data and Sionna SYS panels. The raw data is available on ROS topics and can be inspected with the `rostopic` CLI.\n\n\u003Cp align=\"center\">\n  \u003Cimg src=\"Tutorials\u002Fimages\u002FStartSim.png\" alt=\"Start the Sionna simulation in SimART\" width=\"100%\">\n\u003C\u002Fp>\n\n\u003Cp align=\"center\">\n  \u003Cimg src=\"Tutorials\u002Fimages\u002FWirelessData.png\" alt=\"Wireless Data panel in SimART\" width=\"100%\">\n\u003C\u002Fp>\n\n\u003Cp align=\"center\">\n  \u003Cimg src=\"Tutorials\u002Fimages\u002FSionnaSYSData.png\" alt=\"Sionna SYS panel in SimART\" width=\"100%\">\n\u003C\u002Fp>\n\n- You can also record the generated data after clicking \"Start Recording\".\n\n\u003Cp align=\"center\">\n  \u003Cimg src=\"Tutorials\u002Fimages\u002FRecordingData.png\" alt=\"Recording data in SimART\" width=\"100%\">\n\u003C\u002Fp>\n\n## Use SimART with UE4 and AirSim\n\nIf you need to view the scene in UE4 or customize UAV flight, follow the steps\nbelow to set up Unreal Engine and AirSim.\n\n### 6. Download and Compile Unreal Engine\n\nClone and build Unreal Engine 4 and AirSim. We recommend you to use Unreal Engine 4.27 version. You can refer to [Unreal Engine Documentation](https:\u002F\u002Fdev.epicgames.com\u002Fdocumentation\u002Funreal-engine\u002Fdownloading-source-code-in-unreal-engine) and [AirSim](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FAirSim.git). \n\nMake sure that you can use AirSim to perform UAV simulations first. Then move the AirSim ros wrapper package to your AirSim path. In the root directory of the repository, run (Remember to replace the path\u002Fto\u002Fyour\u002FAirSim with your actual AirSim path):\n\n```bash\ncp -r ~\u002Fcatkin_ws\u002Fsrc\u002FSimART\u002Fthird_party\u002Fairsim_ros_pkgs_sa path\u002Fto\u002Fyour\u002FAirSim\u002Fros\u002Fsrc\n```\n\nThis will copy the modified AirSim ros wrapper package to the AirSim ros workspace. Then build the new AirSim ros wrapper in the root directory of the AirSim ros workspace:\n\n```bash\ncd path\u002Fto\u002Fyour\u002FAirSim\u002Fros\ncatkin build\n```\n\nWhen you use the new ros wrapper to publish the rostopics in AirSim, you can run:\n```bash\ncd path\u002Fto\u002Fyour\u002FAirSim\u002Fros\nsource devel\u002Fsetup.bash\nroslaunch airsim_ros_pkgs_sa airsim_node.launch is_vulkan:=\"false\"\n```\n\n### 7. Enable UE Live View\n\nThe normal build can load local meshes and use ROS topics without the AirSim\nC++ SDK. Enable AirSim C++ support only when you need UE live-view integration(The UE Live View panel).\n\nFirst make sure that Unreal Engine 4 and AirSim have been cloned and built, including AirSim's RPC library. \nThen rebuild the catkin workspace with(Remember to replace \u002Fpath\u002Fto\u002Fyour\u002FAirSim with your actual AirSim path)\n\n```bash\ncd ~\u002Fcatkin_ws\nsource \u002Fopt\u002Fros\u002Fnoetic\u002Fsetup.bash\ncatkin build airsim_gui_UErealtime --cmake-args \\\n  -DAIRSIM_GUI_ENABLE_AIRSIM=ON \\\n  -DAIRSIM_CLIENT_ROOT=\u002Fpath\u002Fto\u002Fyour\u002FAirSim\nsource devel\u002Fsetup.bash\n```\n\n\u003C!-- With `catkin_make`, use:\n\n```bash\ncd ~\u002Fcatkin_ws\nsource \u002Fopt\u002Fros\u002Fnoetic\u002Fsetup.bash\ncatkin_make \\\n  -DAIRSIM_GUI_ENABLE_AIRSIM=ON \\\n  -DAIRSIM_CLIENT_ROOT=\u002Fpath\u002Fto\u002Fyour\u002FAirSim\nsource devel\u002Fsetup.bash\n```\n\nYou can also export the AirSim path before building:\n\n```bash\nexport AIRSIM_CLIENT_ROOT=\u002Fpath\u002Fto\u002Fyour\u002FAirSim\ncatkin build airsim_gui_UErealtime --cmake-args -DAIRSIM_GUI_ENABLE_AIRSIM=ON\n``` -->\n\n### 8. Further Exploration\n\nThe rest of the maps should work with a UAV simulation software, e.g., AirSim (Recommended), Gazebo, etc, and a matched map (Take AirSim for instance, a corresponding Unreal Engine project is required). The required output of the UAV simulation software is a rostopic containing the pose of the UAV (data type is nav_msgs\u002FOdometry or geometry_msgs\u002FPoseStamped). If you decide to use AirSim, please follow [Create your own maps](Tutorials\u002FCreateMap\u002FCreateMap.md).\n\n---\n\n## Contact\n\nFor questions, feedback, or collaboration, please contact us at kangyan@std.uestc.edu.cn, yuqicao@std.uestc.edu.cn.\n\n---\n\n## Citation\n\nIf you use SimART in your research, please cite our paper:\n\n```bibtex\n@article{yan2026simart,\n  title={SimART: A Unified and Open Real-world Multimodal Simulation Platform for 6G Integrated Sensing and Communication},\n  author={Yan, Kang and Cao, Yuqi and Li, Jiaqi and Xiang, Luping and Yang, Kun},\n  journal={arXiv preprint arXiv:2605.13309},\n  year={2026}\n}\n```\n","SimART是一个面向全场景无线通信与感知研究的开源软件平台。该项目基于ROS1构建，集成了C++\u002FQt\u002FVTK图形界面、Sionna框架下的射线追踪和链路仿真功能、AirSim\u002FUnreal Engine实时可视化工具以及rosbag记录和回放工具。其核心功能包括基站布局、轨迹可视化、无线信道观测检查及三维场景中的波束选择评估等。此外，SimART还支持数字孪生场景建设、基于ROS的轨迹重放、Sionna数据采集、视觉网络规划等功能，并能够配合AirSim和Unreal Engine进行通信-感知实验。适用于需要对复杂环境下的无线通信系统性能进行模拟分析的研究场景，如无人机通信、机器人导航等领域。","2026-06-11 03:56:36","CREATED_QUERY"]