A W-band slot array antenna based on a substrate integrated waveguide (SIW) for microwave power transmission (MPT) is proposed in this paper. By size optimization, the transition from the rectifier element to the antenna is limited to a small size. It realizes a compact arrangement of the radiating slots, which not only improves the aperture efficiency of the antenna but also makes it easy to integrate into a large-scale array. For antenna testing, a coplanar waveguide–SIW–rectangular waveguide transition structure is added at the end of the antenna, and an antenna with this transition structure is processed by PCB printing technology and measured. The measured reflection coefficient is less than −10 dB at 90–96 GHz, the aperture efficiency is greater than 60% at 93.5–94.5 GHz, the maximum gain is 13.2 dB at 93.5 GHz, and the aperture efficiency is 79%. The test results of the antenna show that the antenna has a good performance and can be applied to the MPT system as a rectenna.

In this article, a nanosecond pulse generation circuit is designed and fabricated based on a commercial Si-p-i-n rectifier diode using the DSRD principle. The circuit is composed of four parallel stacks with a 3-kV pulse output and a rise time of less than 2 ns. Each stack contains four diodes in a series. The working principle and main parameters of the circuit are analyzed by Pspice, and the simulation results are consistent with the experimental results. Switching cutoff speed, reverse voltage, offset voltage, and the topological circuit structure of connecting the rectifier diodes affect the amplitude and rise time of the output pulse.

Single-object tracking algorithms based on Siamese full convolutional networks have attracted much attention from researchers owing to their improvement in precision and speed. Since this tracking model only learns a similarity model offline, it is not able to obtain more useful feature discrimination information to adapt to the various variations of targets in complex scenes. To improve the performance of this tracking model, we propose a Siamese network tracking algorithm that incorporates multiple attention mechanisms and an adaptive updating strategy for background features. First, a backbone feature extraction network is proposed that utilizes a small convolutional kernel to fuse jump-layer connectivity features, thereby improving the feature representation capability of the network. Second, an adaptive update strategy for background features is proposed to improve the model’s ability to discriminate between the object and background features. Third, the fusion of multiple attention mechanisms is proposed so that the model learns to focus on the channel, spatial, and coordinate features. Fourth, the response fusion operation is proposed after the inter-correlation operation to enrich the output response of the model. Finally, our algorithm is trained using the GOT-10K dataset and evaluated by testing on the object tracking benchmark datasets OTB100 and VOT2018. The test results show that compared with other algorithms, our algorithm can effectively cope with the problem of degradation of the tracking performance in complex environments, and it can further improve the tracking precision and precision under the premise of ensuring the tracking speed.