Direct-sequence spread spectrum (DSSS) communication, as a key technology in code-division multiple access networks, demonstrates excellent performance in multiple-access capability and interference suppression. However, its effectiveness significantly degrades in mobile underwater acoustic (UWA) networks, where the platform mobility induces severe Doppler effect and exacerbates the instability of multipath propagation. These impairments are further exacerbated by the inherently low data rates of UWA DSSS systems, where the prolonged signal duration leads to time-varying Doppler effect and fast-fading multipath structure within a single frame, collectively degrading the UWA DSSS receiver performance. To address these limitations, this paper proposes an improved DSSS receiver to achieve time-varying UWA multipath tracking, which employs Kalman filtering for channel parameter estimation. A multipath management technique is implemented to determine the appearance and disappearance of paths, followed by a coherent combination of all valid paths. Comprehensive simulations demonstrate the superior performance of the proposed receiver under various time-varying channel scenarios. The effectiveness of the proposed receiver is further validated by experimental data collected from Danjiangkou Lake and Zhang River experiments.

Current sparsely-supervised object detection methods largely depend on high threshold settings to derive high-quality pseudo labels from detector predictions. However, hard instances within point clouds frequently display incomplete structures, causing decreased confidence scores in their assigned pseudo-labels. Previous methods inevitably result in inadequate positive supervision for these instances. To address this problem, we propose a novel Hard INsTance Enhanced Detector (HINTED), for sparsely-supervised 3D object detection. Firstly, we design a self-boosting teacher (SBT) model to generate more potential pseudo-labels, enhancing the effectiveness of information transfer. Then, we introduce a mixed-density student (MDS) model to concentrate on hard instances during the training phase, thereby improving detection accuracy. Our extensive experiments on the KITTI dataset validate our method's superior performance. Compared with leading sparsely-supervised methods, HINTED significantly improves the detection performance on hard instances, no-tably outperforming fully-supervised methods in detecting challenging categories like cyclists. HINTED also significantly outperforms the state-of-the-art semi-supervised method on challenging categories. The code is available at https://github.com/xmuqimingxia/HINTED.

Recently, 3D object detection with sparse annotations has received great attention. However, current detectors usually perform poorly under very limited annotations. To address this problem, we propose a novel Contrastive Instance feature mining method, named CoIn. To better identify indistinguishable features learned through limited supervision, we design a Multi-Class contrastive learning module (MCcont) to enhance feature discrimination. Meanwhile, we propose a feature-level pseudo-label mining framework consisting of an instance feature mining module (InF-Mining) and a Labeled-to-Pseudo contrastive learning module (LPcont). These two modules exploit latent instances in feature space to supervise the training of detectors with limited annotations. Extensive experiments with KITTI dataset, Waymo open dataset, and nuScenes dataset show that under limited annotations, our method greatly improves the performance of baseline detectors: CenterPoint, Voxel-RCNN, and CasA. Combining CoIn with an iterative training strategy, we propose a CoIn++ pipeline, which requires only 2% annotations in the KITTI dataset to achieve performance comparable to the fully supervised methods. The code is available at https://github.com/xmuqimingxia/CoIn.

3D object detection from LiDAR point clouds has gained great attention in recent years due to its wide applications in smart cities and autonomous driving. Cascade framework shows its advancement in 2D object detection but is less investigated in 3D space. Conventional cascade structures use multipleseparatesub-networks to sequentially refine region proposals. Such methods, however, have limited ability to measure proposal quality in all stages, and hard to achieve a desirable performance improvement in 3D space. This paper proposes a new cascade framework, termed CasA, for 3D object detection from LiDAR point clouds. CasA consists of a Region Proposal Network (RPN) and a Cascade Refinement Network (CRN). In CRN, we designed a new Cascade Attention Module that uses multiple sub-networks and attention modules to aggregate the object features from different stages and progressively refine region proposals. CasA can be integrated into various two-stage 3D detectors and improve their performance. Extensive experiments on KITTI and Waymo datasets with various baseline detectors demonstrate the universality and superiority of our CasA. In particular, based on one variant of Voxel-RCNN, we achieve state-of-the-art results on the KITTI dataset. On the KITTI online 3D object detection leaderboard, we achieve a high detection performance of 83.06%, 47.09%, and 73.47% Average Precision (AP) in the moderate Car, Pedestrian, and Cyclist classes, respectively. Code is available at https://github.com/hailanyi/CasA.