This page has only limited features, please log in for full access.
A micro-Doppler signature (m-DS) based on the rotation of drone blades is an effective way to detect and identify small drones. Deep-learning-based recognition algorithms can achieve higher recognition performance, but they needs a large amount of sample data to train models. In addition to the hovering state, the signal samples of small unmanned aerial vehicles (UAVs) should also include flight dynamics, such as vertical, pitch, forward and backward, roll, lateral, and yaw. However, it is difficult to collect all dynamic UAV signal samples under actual flight conditions, and these dynamic flight characteristics will lead to the deviation of the original features, thus affecting the performance of the recognizer. In this paper, we propose a small UAV m-DS recognition algorithm based on dynamic feature enhancement. We extract the combined principal component analysis and discrete wavelet transform (PCA-DWT) time–frequency characteristics and texture features of the UAV’s micro-Doppler signal and use a dynamic attribute-guided augmentation (DAGA) algorithm to expand the feature domain for model training to achieve an adaptive, accurate, and efficient multiclass recognition model in complex environments. After the training model is stable, the average recognition accuracy rate can reach 98% during dynamic flight.
Caidan Zhao; Gege Luo; Yilin Wang; Caiyun Chen; Zhiqiang Wu. UAV Recognition Based on Micro-Doppler Dynamic Attribute-Guided Augmentation Algorithm. Remote Sensing 2021, 13, 1205 .
AMA StyleCaidan Zhao, Gege Luo, Yilin Wang, Caiyun Chen, Zhiqiang Wu. UAV Recognition Based on Micro-Doppler Dynamic Attribute-Guided Augmentation Algorithm. Remote Sensing. 2021; 13 (6):1205.
Chicago/Turabian StyleCaidan Zhao; Gege Luo; Yilin Wang; Caiyun Chen; Zhiqiang Wu. 2021. "UAV Recognition Based on Micro-Doppler Dynamic Attribute-Guided Augmentation Algorithm." Remote Sensing 13, no. 6: 1205.
With the increasing demand for intelligent traffic management and road network intelligent information services, the vehicular ad hoc networks (VANETs) combined with information of air, space and ground have outstanding advantages in coverage, reliable transmission, and resource richness. Due to the characteristics of heterogeneous, numerous nodes, and frequent cross-network flow, the space–air–ground integrated network (SAGIN) puts forward higher requirements for security. This paper proposes a cross-regional node identity management architecture based on the hash chain, combined with radio frequency (RF) fingerprint theory, to guarantee node identity security with a non-duplicated physical information identity authentication mechanism. At the same time, the blockchain consensus mechanism is simplified to achieve block recording and verification. OMNet ++, SUMO, and Veins co-simulation platforms are used to generate transactions for cross-regional traffic flow. Based on the Hyperledger–Fabric architecture, Kafka and PBFT consensus algorithms are simulated. The simulation results show that the average delay of a single transaction generated block is about 0.9 ms, which achieves efficient and low-latency authentication.
Gege Luo; Mingxian Shi; Caidan Zhao; Zhiyuan Shi. Hash-Chain-Based Cross-Regional Safety Authentication for Space-Air-Ground Integrated VANETs. Applied Sciences 2020, 10, 4206 .
AMA StyleGege Luo, Mingxian Shi, Caidan Zhao, Zhiyuan Shi. Hash-Chain-Based Cross-Regional Safety Authentication for Space-Air-Ground Integrated VANETs. Applied Sciences. 2020; 10 (12):4206.
Chicago/Turabian StyleGege Luo; Mingxian Shi; Caidan Zhao; Zhiyuan Shi. 2020. "Hash-Chain-Based Cross-Regional Safety Authentication for Space-Air-Ground Integrated VANETs." Applied Sciences 10, no. 12: 4206.