D KalmanNet

--- license: mit tags: - robotics - autonomous-vehicles - object-tracking - kalman-filter - fmcw-lidar - doppler-lidar - pytorch datasets: - AevaScenes metrics: - prediction-error pipeline_tag: object-detection --- # D-KalmanNet: Neural Kalman Filtering for Doppler LiDAR Tracking This repository contains the pre-trained weights for **D-KalmanNet**, the tracking component of the DPNet framework. D-KalmanNet integrates a structured Gaussian State Space (GSS) model with a recurrent neural network to accurately predict and track the future states of dynamic obstacles using measurements from (FMCW) **Doppler LiDAR**. The full framework can be found in the official GitHub repository. ## Model Details - **Developed by:** [UUwei-zuo](https://github.com/UUwei-zuo) - **Dataset Trained On:** [AevaScenes](https://github.com/aevainc/aevascenes) - **Framework:** PyTorch - **Associated Code:** [GitHub: UUwei-zuo/DPNet](https://github.com/UUwei-zuo/DPNet) - **Paper:** [RA-L '26][DPNet: Doppler LiDAR Motion Planning for Highly-Dynamic Environments](https://arxiv.org/abs/2512.00375) ## How to Use Intructions for loading the pretraining `model.pt` or training your custom model can be found in [GitHub: UUwei-zuo/DPNet](https://github.com/UUwei-zuo/DPNet). ## Citation ```bibtex @article{zuo2026dpnet, author={Zuo, Wei and Ren, Zeyi and Li, Chengyang and Wang, Yikun and Zhao, Mingle and Wang, Shuai and Sui, Wei and Gao, Fei and Wu, Yik-Chung and Xu, Chengzhong}, journal={IEEE Robotics and Automation Letters}, title={DPNet: Doppler LiDAR Motion Planning for Highly-Dynamic Environments}, year={2026}, volume={11}, number={6}, pages={7190-7197}, doi={10.1109/LRA.2026.3685933} } ```

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