The fast development of unmanned aerial vehicles (UAVs), commonly known as drones, has brought a unique set of opportunities and challenges to both the civilian and military sectors. While drones have proven useful in sectors such as delivery, agriculture, and surveillance, their potential for abuse in illegal airspace invasions, privacy breaches, and security risks has increased the demand for improved detection and classification systems. This state-of-the-art review presents a detailed overview of current improvements in drone detection and classification techniques: highlighting novel strategies used to address the rising concerns about UAV activities. We investigate the threats and challenges faced due to drones’ dynamic behavior, size and speed diversity, battery life, etc. Furthermore, we categorize the key detection modalities, including radar, radio frequency (RF), acoustic, and vision-based approaches, and examine their distinct advantages and limitations. The research also discusses the importance of sensor fusion methods and other detection approaches, including wireless fidelity (Wi-Fi), cellular, and Internet of Things (IoT) networks, for improving the accuracy and efficiency of UAV detection and identification.

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.

The pulsed elongation of fiber Bragg gratings is considered in order to be used to measure the displacement or deformation rate of objects. Optimal measurement modes were determined, numerical simulation of the output signal was performed during pulsed elongation or compression of the fiber grating, and the main patterns were analyzed. The results of the application of the Bragg gratings for the experimental determination of the deformation rate of materials under pulsed magnetic action are presented. Experimentally obtained and theoretical dependencies are compared. The dependencies of the change in the grating parameters—the coefficient and the half-width of the reflection spectrum with successive shortening of the grating—are given.

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.

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