As unmanned aerial vehicles (UAVs) are increasingly employed across various industries, the demand for robust and accurate detection has become crucial. Light detection and ranging (LiDAR) has developed as a vital sensor technology due to its ability to provide rich 3D spatial information, particularly in applications such as security and airspace monitoring. This review systematically explores recent innovations in LiDAR-based drone detection, deeply focusing on the principles and components of LiDAR sensors, their classifications based on different parameters and scanning mechanisms, and the approaches for processing LiDAR data. The review briefly compares recent research works in LiDAR-based only and its fusion with other sensor modalities, the real-world applications of LiDAR with deep learning, as well as the major challenges in sensor fusion-based UAV detection.

This study investigated a search-free digital method for radio direction-finding that utilizes spatial analytical signal reconstruction. The research focused on optimizing the method's accuracy for estimating the direction of a radio source. The analysis identified the separation distance and number of chosen antenna elements for signal reconstruction as the key parameters influencing accuracy. Analytical optimization and simulations were employed to determine the optimal values for these parameters. The research successfully modeled the impact of antenna element selection on direction-finding errors under specific signal-to-noise conditions. This model can be used to guide further development and optimization of this radio direction-finding method.

A promising direction for the development of passive radar monitoring stations is to improve their efficiency by increasing their speed of performance. For the digital spectral-correlation method for determining the delay of radio signals and direction finding, analytical expressions have been derived for a variance of the estimation of the delay in receiving a signal by radio channels and directions to the source of radio emission. A feature of the method reported in this study is the use of two-stage temporal and spatial spectral analysis of the mutual spectrum, a single-iteration correlation analysis. The duration of estimating the direction finding has been evaluated through the total number of multiplication operations with accumulation. The proposed method, while providing for a gain of 27 times in terms of performance speed, demonstrated a slight decrease in accuracy compared to the optimal one due to energy signal loss. The result of the simulation has established the dependences of the standard deviation in the direction finding and delay estimates on the signal-to-noise ratio, the type of spectral analysis window, and the size of the antenna base. The standard deviation of the direction-finding estimate depends on the signal-to-noise ratio and varies over the range of values [0.08; 0.034]° with a change in the signal/noise ratio [−10; 40] dB. As the signal/noise ratio increases, the error decreases in line with a hyperbolic dependence. The standard deviation of the delay estimate depends on the signal-to-noise ratio and varies similarly to the error of the directional estimate, and is in the range of values [18.176; 1.56] ns, which corresponds to an error of [0.637; 0.055] %. The error of direction-finding estimation, depending on the size of the antenna base, decreases in the exponent within [1.6; 0.03]° with an increase in the antenna base in the range from 200 to 7,500 m. The results reported here could be used for the parametric optimization of spectral-correlation radio direction finders at passive radar monitoring stations.

The object of the study is the process of radio signal delay and direction estimation using digital spectral-correlation analysis enhanced by machine learning. This process is essential for high-accuracy direction finding in electromagnetic monitoring systems. The problem addressed is the low adaptability and insufficient accuracy of traditional direction finding methods under variable signal conditions, especially due to manual parameter selection and the computational complexity of correlation processing. The essence of the obtained results is a machine learning-based method for predicting radio signal parameters (delay and angle), which reduced the standard deviation of direction finding estimates to 0.08–0.026° and delay estimation error to 1.5–14.8 μs across a signal-to-noise ratio range of 9 to 37 dB. These results are supported by averaging over 1000 realizations using Monte Carlo simulation, confirming their stability under noise. Due to its distinctive features, the proposed solution addressed the problem by enabling automated selection of processing parameters through a trained neural network that adapts to nonlinear signal characteristics, minimizing the need for manual adjustment or exhaustive search. These results are explained by the model’s ability to identify hidden dependencies between signal parameters and processing outcomes, enabling adaptive behavior and reduced deviations. Although no computational complexity assessment is provided, prediction-based parameter estimation is expected to improve processing speed in future implementations. The results can be applied in real-time electromagnetic monitoring, radio surveillance, and defense applications, especially under limited computing resources or varying noise conditions

A method of correlation-interferometric direction finding has been improved, which effectively solves the problem of radio direction finding of radio emission sources under conditions of exposure to one or two masking interference. The problem was solved using the selection of an unmasked fragment of the spatial spectrum of the signal and the reconstruction of the missing samples of its signal group. As a result of the synthesis of the proposed method, estimates of signal samples were obtained as exact solutions to the proposed energy balance equations. The resulting solutions provide a significant increase in the signal-to-interference ratio and, accordingly, direction-finding accuracy without increasing the number of reception channels of the antenna array. As a result of the simulation, the dependences of the standard deviation of the bearing estimate on the signal-to-noise ratio in the presence of interference were built. Under the influence of one or two masking interferences and a signal-to-interference ratio of 0 dB, the use of the known direction-finding method without interference selection produces an anomalously large direction-finding error of more than 0.42 degrees, which is practically independent of the signal-to-noise ratio. The direction-finding method with selection of spectral signal samples masked by interference reduces the direction-finding error to 0.22 degrees when exposed to one interference and to 0.3 degrees when exposed to two interferences. This is due to the presence of power losses of the usable signal during the selection of its samples masked by interference. The proposed method of direction finding with reconstruction of signal samples provides a significant gain in accuracy by 3–30 times compared to the method of selection of masked samples in the range of changes in the signal-to-noise ratio (–20.5) dB. The direction-finding error of the proposed method decreases with increasing signal/noise according to a hyperbolic dependence. It is advisable to use the proposed direction-finding method when masking no more than two samples of the signal group

In recent years, social networks have struggled to meet user protection and fraud prevention requirements under unpredictable risks. Anonymity features are widely used to help individuals maintain their privacy, but they can also be exploited for malicious purposes. In this study, we develop a machine learning-driven de-anonymization system for social networks, with a focus on feature selection, hyperparameter tuning, and dimensionality reduction. Using supervised learning techniques, the system achieves high accuracy in identifying user identities from anonymized datasets. In experiments conducted on real and synthetic data, the optimized models consistently outperform baseline methods on average. Even in cases where they do not, significant improvements in precision are observed. Ethical considerations surrounding de-anonymization are thoroughly discussed, including the responsibility of implementation to maintain a balance between privacy and security. By proposing a scalable and effective framework for analyzing anonymized data in social networks, this research contributes to improved fraud detection and strengthened Internet security.