With the rapid advancements in deep learning technology, various deep learning-based object detection algorithms have found extensive applications in UAV-related tasks. However, motivated by the fact that current object detection algorithms for unimodal aerial remote sensing images (ARSI) fail to achieve around-the-clock object detection. To tackle this, we propose an around-the-clock object detection algorithm YOLOFIV that fuses infrared and visible features. First, we design a dual-stream backbone network based on the attention mechanism to adequately extract the features of both modalities. Moreover, the ECA attention mechanism is integrated into the feature enhancement network to amplify attention toward challenging detection scenarios. Finally, we improve the horizontal detection head to a rotating one to preserve object orientation. We evaluate the proposed method YOLOFIV on the widely used Drone Vehicle dataset, YOLOFIV achieves an accuracy of 64.71% (in terms of $mAP_{0.5}$ ), accuracy improvement of 8.32% over baseline bimodal model, similar performance to UACMD designed for ARSI object detection but with 92.35% reduction in parameter count, and 17.87 times speedup. The results show that our approach achieves round-the-clock object detection while maintaining a favorable accuracy-speed trade-off.

This article studies the transmit beamforming security in the dual-functional radar-communication (DFRC) system, where the radar beam also serves as the artificial jamming to suppress potential eavesdropper channels. Unlike previous researches, which assumed that the lone target might be a potential eavesdropper on the adversary side, this paper analyses a more generic scenario in which the eavesdroppers’ channel state information (CSI) is unknown. The eavesdropping rate should be limited to guarantee transmission confidentiality, which is intractable owing to the unknown CSI. To address this issue, we present the long-term eavesdropping signal-to-interference-plus-noise ratio (SINR) as a more tractable security metric. Based on the long-term eavesdropping SINR, the sum rate maximization (SRM) and jamming power maximization (JPM) transmission scheme are investigated. For the SRM scheme, we propose a zero-forcing (ZF) suboptimal algorithm to solve the non-convex fractional programming (FP) problem, and for the JPM scheme, the resultant problem is solved via the semidefinite relaxation (SDR) technique. Finally, numerical results validate the effectiveness of the proposed beamforming approaches.

Identifying challenging samples in real-time aerial images captured by UAVs is currently a difficult task. Lightweight network designs often utilize lightweight modules or employ methods such as network pruning, quantization, and distillation for real-time detection on embedded devices. However, these approaches are inadequate for complex background aerial images. In this paper, we propose a real-time object detection algorithm specifically designed for complex aerial images, named MINIAOD. First, we use Ghost convolution to build a lightweight backbone network, which improves the detection speed without compromising the feature extraction capability. Additionally, we utilize the GSConv module to construct a feature enhancement network and develop the GSC3ECA module to improve the network’s learning capability for challenging targets, while also decreasing computational complexity. To boost the network’s focus on small and medium-sized targets and mitigate interference from complex backgrounds, we incorporate the ECA attention mechanism in the small and medium-sized target detection branch to elevate the focus on difficult-to-detect samples. Finally, we design the rotation detection head to characterize the target directionality of aerial images. We perform extensive experiments on multiple publicly available datasets and the proposed method demonstrates a favorable balance between accuracy and speed when compared to the current leading models.

We focus on the target detection problem of the multiple‐input multiple‐output (MIMO) radar in clutter. Most of the existing MIMO radar detection works rely on the clutter models, which may limit their scopes of application. To address this issue, a deep learning based MIMO radar target detection framework is proposed in this paper, which utilises the powerful representation and discrimination capabilities of deep neural networks (DNNs) to improve the detection performance in a data‐driven manner and does not require any prior information about the clutter distribution. In most classification problems, DNNs are trained with the cross entropy loss, which promotes DNNs to achieve higher accuracy. However, the main concern in the radar target detection problem is the probability of detection (PD) under a given probability of false alarm. In this framework, a novel loss is proposed to directly maximise the average PD of the DNN, or equivalently maximise the area under curve of the DNN. Under the DL‐based MIMO radar target detection framework, a deep convolutional neural network (CNN) architecture is introduced, and an input feature of the received signal for the CNN based on the conventional generalised likelihood ratio test statistics is designed to further improve the detection performance. Finally, extensive simulation results are presented to validate the detection performance and the robustness of the proposed methods.

In this paper, a novel multiphase reconfigurable and fault-tolerant drive converter topology is proposed for driver with fail-work capability, and fault-tolerant strategies are proposed for open-circuit and short-circuit faults in the single-phase windings, aiming to improve the motor drive system's fail-work ability in the high reliability applications. The drive converter topology can isolate open-circuit and short-circuit faults in power supplies, power devices or switches. Then, it can reconfigure the drive circuit topology from independent H-bridge to star-type by reconfigurable switches and power devices, according to the fault type and the fault tolerant truth tables. After reconfiguration, remedial operation will be utilized for open- and short-circuit faults of windings, so that the motor system has two-fold fail-work capabilities in driver and windings. At last, based on a series of experiments on a transit co-simulation model and test bench, the effectiveness and validity of fault-tolerant capability and the strategies are verified, and two-fold fail-work remedy so on.