Although federated learning is gaining prevalence in smart sensor networks, substantial risks to data privacy and security persist. An improper application of federated learning techniques can lead to critical privacy breaches. Practical and effective privacy-enhanced federated learning (PEPFL) is a widely used federated learning framework characterized by low communication overhead and efficient encryption and decryption processes. Initially, our analysis scrutinized security vulnerabilities within the PEPFL framework and identified an effective attack strategy. This strategy enables the server to derive private keys from content uploaded by participants, achieving a 100% success rate in extracting participants’ private information. Moreover, when the number of participants does not exceed 300, the attack time does not surpass 3.72 s. Secondly, this paper proposes a federated learning model that integrates homomorphic encryption and secret sharing. By using secret sharing among participants instead of secure multi-party computation, the amount of effective information available to servers is reduced, thereby effectively preventing servers from inferring participants’ private gradients. Finally, the scheme was validated through experiments, and it was found to significantly reduce the inherent collusion risks unique to the federated learning scenario. Moreover, even if some participants are unavailable, the reconstructable nature of secret sharing ensures that the decryption process can continue uninterrupted, allowing the remaining users to proceed with further training. Importantly, our proposed scheme exerts a negligible impact on the accuracy of model training.

Vortex electromagnetic (EM) wave radar can obtain more accurate rotation parameters for target classification and recognition. However, the existing rotation parameter extraction methods are difficult to obtain the acceleration and initial phase of composite moving target in the case of multiple scattering points. In this paper, the estimation error of accelerated motion target features in a situation with numerous scattering points is examined for the first time, along with a method for extracting accelerated motion target parameters based on vortex electromagnetic wave radar. Firstly, the relationship between the characteristics of non derivable points (NDPs) in the rotating Doppler frequency shift and the characteristics of the target’s spin motion is analyzed, the method of estimating the number of scattering points is studied, and the formula for extracting the spin parameters is derived. After that, the time-frequency analysis of the echo signal is combined to extract the target translational motion characteristics. Simulation results show that the proposed method is effective.

With the increasing prevalence of unmanned aerial vehicles (UAVs) in daily applications, the accurate and timely detection of UAV targets is of paramount importance. As artificial intelligence continues to advance, deep learning technology has emerged as a key solution to this challenge. In light of this, we have developed a recognition model for UAV using a dual-channel multi-scale convolutional neural network (CNN). This model initially enhances UAV recognition accuracy by integrating the range-Doppler (RD) diagrams and time-frequency distribution (TFD) diagrams of radar echo signals, and then further refining it by fusing features of different scales. The accuracy of this model reaches 98.67% in the actual datasets, confirming its effectiveness in UAV surveillance applications.

Utilizing the micro-Doppler effect caused by the rotation of rotor blades to invert parameters such as spatial position and rotational speed of targets is of great significance for the detection and identification of unmanned aerial vehicles. The vortex electro-magnetic wave radar can directly obtain target azimuth information. However, most existing techniques for vortex electromagnetic wave radar are based on uniform motion models and can only extract partial parameters of the target, making it difficult to achieve precise characterization of the target. This article proposes a 3D parameter extraction algorithm for a rotor variable speed moving target. Firstly, the echo model of a rotor target is presented. Secondly, the separation of different rotor information was achieved based on the nearest neighbor criterion. Lastly, a set of equations containing the relationships between target structural, motion, and position parameters were established. By solving these equations, the extraction of target rotor length, wheelbase, rotational speed, acceleration, spatial position, attitude angle, and other parameters was achieved. The effectiveness and robustness of the proposed method were verified through simulation.

Vortex electromagnetic wave radar carrying orbital angular momentum can compensate for the deficiency of planar electromagnetic wave radar in detecting motion parameters perpendicular to the direction of electromagnetic wave propagation, thus providing more information for target recognition, which has become a hot research field in recent years. However, existing research makes it difficult to obtain the acceleration and rotation centers of targets under non-quasi-axial incidence conditions of vortex electromagnetic waves. Based on this, this article proposes a variable speed motion target parameter extraction method that combines single element and total element echoes. This method can achieve three-dimensional information extraction of radar targets based on a uniform circular array (UCA). Firstly, we establish a non-quasi-axis detection echo model for variable-speed moving targets and extract echoes from different array elements. Then, a single element echo is used to extract the target’s range slow time profile and obtain the target’s rotation center z coordinate. We further utilize the target linear, angular Doppler frequency shift extremum, and median information to extract parameters such as target acceleration, tilt angle, rotation radius, and rotation center x and y coordinates. We analyzed the impact of different signal-to-noise ratios and motion states on parameter extraction. The simulation results have verified the effectiveness of the proposed algorithm.