With the rapid development of the technology industry, low, slow, and small civilian Unmanned aerial vehicle (UAV) have been popularized, bringing fun and convenience to life and work. However, with the popularization of UAV, all kinds of illegal flying behaviors have appeared successively, posing a threat to people's life and national security. To solve this problem, this article proposes a method and designs a system to detect and identify the UAV through the radio frequency (RF) signal emitted by the UAV itself. First, the RF front-end scans and intercepts the radio signals in the target airspace, then analyzes and identifies them by algorithms, and then implements the algorithm by field-programmable gate array (FPGA). Among them, algorithm recognition includes UAV flight control signal (FCS) detection and recognition, UAV image transmission signal (ITS) blind detection, and accurate recognition. Through the algorithm recognition result, it can be judged whether there are UAVs in the protected area. Finally, the hierarchical experimental verification method is used to demonstrate that the system can identify the number, type, and frequency band of UAVs in urban complex electromagnetic environment by using dual source detection and recognition algorithm, which provides a good foundation for UAV management and control.

For orthogonal frequency division multiplexing (OFDM) systems in high-mobility scenarios, the estimation of time-varying multipath channels not only has a large error, which affects system performance, but also requires plenty of pilots, resulting in low spectral efficiency. To address these issues, we propose a time-varying multipath channel estimation method based on distributed compressed sensing and a multi-symbol complex exponential basis expansion model (MS-CE-BEM) by exploiting the temporal correlation and the joint delay sparsity of wideband wireless channels within the duration of multiple OFDM symbols. Furthermore, in the proposed method, a sparse pilot pattern with the self-cancellation of pilot intercarrier interference (ICI) is adopted to reduce the input parameter error of the MS-CE-BEM, and a symmetrical extension technique is introduced to reduce the modeling error. Simulation results show that, compared with existing methods, this proposed method has superior performances in channel estimation and spectrum utilization for sparse time-varying channels.

To address the significant decline in the accuracy of Specific Emitter Identification(SEI) under wireless channel, we propose a novel method that combines a domain adversarial network with multi-feature fusion(MFF) to extract domain-invariant features of the signal and leverage the complementary nature of signal features extracted from different views. Initially, we employ the IQ Convolutional Neural Network (IQCNN), the Gate Recurrent Unit (GRU), and the stacked Fourier Analysis Networks (SFAN) to directly extract and fuse correlation, temporal, and periodic features from the raw I/Q data. Subsequently, we integrate a Domain-Adversarial Training of Neural Networks (DANN) to eliminate channel features, ultimately enabling SEI under channel interference. The experimental results on the WiFi dataset demonstrate that the MFF network designed in this study achieves an identification accuracy of 97% under Additive White Gaussian Noise(AWGN) channel interference with a signal-to-noise ratio(SNR) of 10dB. Furthermore, the proposed method achieves identification accuracy of 93.8%, 90.3%, and 78.2% under three complex real-world channel interference scenarios, respectively. These findings indicate that the proposed method effectively mitigates channel interference and significantly enhances the robustness of SEI.

Frequency‐time selective fading degrades the performance of communication systems, but it also provides an opportunity to collect multipath diversity and Doppler diversity. In this paper, an oversampled grouped‐linear‐constellation‐precoding (GLCP) scheme is proposed for orthogonal frequency division multiplexing (OFDM) systems to extract the diversity inherent in doubly selective fading channels. In the proposed scheme, GLCP is employed to precode the information symbols of OFDM at the transmitter, and the precoded OFDM symbols are oversampled in the time domain at the receiver. A full‐groups maximum likelihood (ML) decoder and a successive group decoder to decode the oversampled OFDM symbols with diversity gains are developed. The successive group decoder combines the group ML decoding algorithm with a block‐decision‐feedback‐equalization (BDFE) algorithm. The proposed scheme can take advantage of both the GLCP scheme and the oversampling scheme to achieve multipath diversity gains and Doppler diversity gains in doubly selective fading channels. Simulation results show that the proposed scheme can significantly improve the performance of OFDM systems in the frequency selective fading channel and the doubly selective fading channel in terms of bit error ratio, indicating that the diversity gains can be greatly enhanced by the proposed scheme in doubly selective fading channels.