Jianhua Wang

A novel microstrip filtering antenna with slot coupling feed is presented in this work. An asymmetric interdigital coupling structure is used for the feed to excite the patch antenna through gap-fed coupling. Introducing a U-shaped slot on the patch surface modifies the current path to attain different resonant modes. The asymmetric coupled fingers in the low-frequency band generate a radiation null of −64 dBi, while additional resonances introduced in the high band broaden the bandwidth from 4.9 to 5.3 GHz. A horizontally shorted microstrip branch produces another null point of −28 dBi between the bands, enabling steeper roll-off and further improvement in frequency selectivity. The proposed filtering antenna provides a flexible filter response without requiring extra filtering circuits, with appreciable peak gains (6.1 dBi and 7.1 dBi) and stable radiation characteristics. This makes it suitable for WLAN (Wireless Local Area Network) and ISM (Industrial Scientific Medical) systems applications.

With the advancement of environmental perception technology, millimeter-wave (mmWave) radar is emerging as a predominant sensor. While deep learning has facilitated the development of mmWave radar object detection (ROD) techniques, mmWave ROD suffers from datasets. Because the annotation of mmWave datasets is inherently more complex. Motivated by masked image modeling (MIM), this paper proposes a novel pretraining method for ROD to address the limitations posed by datasets. The study conducts masking operations on mmWave radar images from both spatial and temporal perspectives, followed by a straightforward image reconstruction proxy task. To the best of our knowledge, our method represents the inaugural application of the MIM self-supervision method to ROD tasks. Additionally, we designed a lightweight self-supervised ROD network (SS-RODNet). Numerous ablation experiments have demonstrated the effectiveness of the proposed method. The pretrained SS-RODNet attains comparable results to the state-of-the-art (SOTA) on CRUW and CARRADA datasets with fewer parameters and floating-point operations per second (FLOPs).

Environmental perception technology is key to self-driving. Nowadays, this is mostly conducted using cameras and Lidar, despite their poor immunity to interference and high price. The millimeter-wave radar can solve these problems, but the current radar-based models suffer from over-complexity and poor global modeling capability. Moreover, the anti-interference capability of millimeter-wave radar object detection (ROD) techniques in complex environments is also a major challenge. Considering this, this paper proposes a novel model called Transformer ROD network (T-RODNet), which consists of a convolutional neural network (CNN) and Transformer, aiming to simultaneously utilize the ability of both to acquire local and global features. In order to improve the modeling capability of the encoder and decoder, the DAM and T-W/SW-MSA modules are proposed, which can greatly improve the performance of the model. Experiments show that T-RODNet achieves state-of-the-art (SOTA) performance on both CRUW and CARRADA datasets. The GFLOPs of T-RODNet are only 8.5% of RODNet-HG, but the Average Precision (AP) is 3.84 higher. Besides, T-RODNet also achieves a strong resistance to interference on the CRUW dataset with noise added. Our code is available at: https://github.com/Zhuanglong2/T-RODNet.