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Introduction. In the past few years, the rapid development and widespread use of unmanned aerial vehicles (UAVs) for solving a variety of tasks has created new threats. The problem of ensuring the safety of protected objects, especially in the area of critically important objects or in places with difficult ornithological conditions (airports, wind power facilities), is of particular importance. In this regard, the issue of detecting small air targets and recognizing their type and degree of danger is acquiring greater importance. This paper presents an algorithm for recognizing air targets based onartificial intelligence technology. The results of a comparative analysis of decision-making methods for recognizing small UAVs and birds based on their trajectory features are presented. The results obtained can be used in the development of systems for recognizing classes of small airborne targets in existing and future radar stations. Aim. Development of an algorithm for recognizing small air targets by trajectory features based on machine learning. Implementation and evaluation of the quality of decision-making methods in a given recognition problem. Materials and methods. Experimental data on the trajectories of UAVs and birds obtained in a passive bistatic radar system are used. The trajectory parameters of the targets and their statistical characteristics are calculated; a set of features for recognition is formed. Using the MATLAB software package, a program for implementing the recognition algorithm and analyzing the quality of decision-making methods was developed. Results. An algorithm for recognizing air targets based on artificial intelligence technology is presented. A comparative analysis of the six most common recognition methods based on machine learning (Naïve Bayes, decision trees, k-nearest neighbors, neural network recognition algorithm, support vector machine, random forests) was carried out, which showed that, under the conditions of this problem, the most effective are k-nearest neighbor method and support vector machine. Conclusion. The presented methods can be used to directly determine the class of targets from a set of marks of their trajectories. Further research will be aimed at developing and implementing a real-time recognition algorithm.

Floods often cause changes in the hydro-geomorphology of riverbeds and banks. These changes need to be closely monitored to find a balance and exchange between lateral and vertical erosion and deposition, upstream local sediment supply, and a stream's transport capacity. Low-frequency cross-sectional field surveys cannot map hard-to-reach locations. Innovative techniques, such as small unmanned aerial vehicles (UAVs), must be employed to monitor these processes. This research compared historical data with a UAV survey and the Pix4DMapper structure-from-motion (SfM) program to assess the longitudinal, lateral, and vertical changes of Sidere Creek in the eastern Black Sea, Türkiye. Digitization was undertaken using 2011–2015–2017 Google Earth photographs, 1960s topographic maps, and 2023 orthomosaics. ArcGIS 10.6 was used to delineate the centerlines (thalweg), left/right banks, alluvial bars, active channel widths, and channel confinement layers. Channel Migration Toolbox and CloudCompare were utilized for analyzing lateral and vertical morphological changes, respectively. The active channel migrated 25.57 m during 1960–2011, 15.84 m during 2011–2015, 6.96 m during 2015–2017, and 5.79 m during 2017–2023. Left-bank channel confinement rose from 2.4% to 42% and right-bank channel confinement from 5.9% to 34.8% over 63 years. Neither stream meandering nor sinuosity index changed statistically. Active channel boundary widths varied from 149.79 m to 9.46 m, averaging 37.3 m. It can be concluded that UAV surveys can precisely measure and monitor the stream channel longitudinal, lateral, and vertical morphological changes at a lower cost and in less time than previous methods. [ABSTRACT FROM AUTHOR]

Aiming at the attitude solution accuracy and robustness for small UAVs in complex flight conditions, this paper proposes a dynamic adaptive attitude and heading systems(AHRS) estimator with singular value decomposition Cubature Kalman filter(SVDCKF). Considering the problem of random bias for the low-cost attitude sensor, this paper designs a method that the sensor random bias is used as the state vector to eliminate the effect of the sensor random bias. Due to the non-linearity of small UAVs AHRS model and the non-positive definite phenomenon of the covariance matrix, a nonlinear AHRS filter combined with the Cubature Kalman filter and singular value decomposition is designed to improve the attitude solution accuracy. In addition, when the UAV flies in the different flight conditions, the three-axis acceleration of the attitude sensor will affect the attitude solution. Thus, a dynamic adaptive factor based on adaptive filtering is used to adjust continuously the acceleration noise variance to improve the robustness of the AHRS. The experimental results show that the method and algorithm proposed not only improve the attitude solution accuracy, and satisfy the flight requirements of small UAVs, but also eliminate the influence of the attitude sensor random bias and three-axis acceleration for the attitude solution to improve the proposed algorithm robustness and anti-interference.

The inverse synthetic aperture Ladar (ISAL) is an important system for observation of space objects. Here, a 2-D ISAL imaging and tracking algorithm is proposed for low observable maneuvering extended objects tracking (M−EOT) such as small space UAVs. When imaging maneuvering objects with ISAL, dispersion and Doppler frequency time-variation exist in the range and cross-range echo signal, respectively. Additionally, the ISAL system can achieve high-resolution images for long-range moving objects, while its performance is affected by atmospheric turbulence. Atmospheric turbulence is a very complicated random state formed by the instability of regular laminar flow at a high maneuvering velocity, which is caused by the random motion of the atmosphere and lead to the random fluctuation of the atmospheric refractive index. Therefore, in real-time M−EOT scenario, the measurements distribution over the whole object is not symmetrical but distributed and skewed in some portions while a target maneuvers due to the unbalanced of reflection that caused by the atmospheric turbulence. In practice, the micro Doppler effect caused by micro moving components such as drone rotors will seriously affect the focused ISAL imaging of the M−EOT's rigid body. To solve these problems in real-time low observable M−EOTs imaging and tracking from 2-D ISAL system, a novel robustness measurement model and recursion multi-Bernoulli track-before-detect filter based on a multiple model skewed non-symmetrically normal distribution is represented with more than one ellipse and lower complexity. In this paper, the Radon-Laplace method is presented to reduce this micro-Doppler effect in ISAL imaging. Additionally, we will eliminate the effect of atmospheric turbulence in ISAL Imaging by using the network-based variance–covariance estimation (NVCE) weighting method. In particular, we describe the M−EOT's state through the random matrices model (RMM). In RMM, the extent is applied by one ellipse, which often lose information about size, heading, and shape, when the scatter centers are distributed symmetrically around the M−EOT's centroid. In this paper, a new observation model used to modify a sub-RMM by considering the characteristics of 2-D ISAL data is proposed. Simulation results demonstrates the proposed robustness algorithm with the influence of the atmospheric turbulence and micro-Doppler. [ABSTRACT FROM AUTHOR]

The urban environment is dynamic and is constantly changing. In Brazil, it has been recently published a decree for urban land regulation for the cadastre - Decree No 9.310 of 2018 15 of March which fixes 8,0 centimeters as the maximum spherical positional error for vertex of an urban property stock being georeferenced by Global Navigation Satellite System - GNSS equipment or aerial orthophotograph. Nowadays it has been noticed a growing use of Unmanned Aircraft Vehicle System - UAVS for remote sensing. This work aims to test if the accuracy of orthophoto-mosaic from a small UAVS mapping data of an urban area for a parcel scale georeferencing is answering the new decree of urban land regulation for the cadastre. It is intended to check the number of Ground Control Points (GCPs) to reach the best quality in terms of geometric resolution according to the necessity of positional quality for the urban parcel georeferenced for the cadastre. As a final consideration, the results implied that mapping a small surveying area with small UAVS is worth, being a potential tool for georeferencing city blocks, and using 20 pairs of GCPs is enough to reach the quality of positional geometry adequate to the Brazilian legislation. [ABSTRACT FROM AUTHOR]

The market of imaging and non-imaging sensors offers nowadays a wide range of products, which are constantly improving and growing. Given the recent advances in the miniaturization of devices for metric survey, and the relatively cheap COTS (Commercial Off The Shelf) solutions widely available, it is therefore crucial to optimally exploit, calibrate and evaluate the performance for 3D data acquisition of the new available devices. These issues are part of the latest research addresses of different Geomatics groups, with the aim to analyze and evaluate these new sensors, in order to discover their real potentialities which are not only connected to their interesting design, low price and small dimensions. According to the final objective of the research, which is the realization of 3D metric survey of a Cultural Heritage site using different techniques and methods, one small UAV (Unmanned Aerial Vehicle) and a portable TLS (Terrestrial Laser Scanner) have been employed. The acquisition strategies and the processing methods are discussed, and the quality of the achievable results is analysed, together with an evaluation of the sensors and the outline of the best practices and use cases scenarios. [ABSTRACT FROM AUTHOR]

Terraces are the major land-use type of agriculture and support the main agricultural production in southeast and southwest China. However, due to smallholder farming, complex terrains, natural disasters and illegal land occupations, a light-weight and low cost dynamic monitoring of agricultural terraces has become a serious concern for smallholder production systems in the above area. In this work, we propose a small unmanned aerial vehicle (UAV) based multi-temporal image registration method that plays an important role in transforming multi-temporal images into one coordinate system and determines the effectiveness of the subsequent change detection for dynamic agricultural terrace monitoring. The proposed method consists of four steps: (i) guided image filtering based agricultural terrace image preprocessing, (ii) texture and geometric structure features extraction and combination, (iii) multi-feature guided point set registration, and (iv) feature points based image registration. We evaluated the performance of the proposed method by 20 pairs of aerial images captured from Longji and Yunhe terraces, China using a small UAV (the DJI Phantom 4 Pro), and also compared against four state-of-the-art methods where our method shows the best alignments in most cases. [ABSTRACT FROM AUTHOR]

This paper presents a vision-aided inertial navigation system for small unmanned aerial vehicles (UAVs) in GPS-denied environments. During visual estimation, image features in consecutive frames are detected and matched to estimate the motion of the vehicle with a homography-based approach. Afterwards, the visual measurement is fused with the output of an inertial measurement unit (IMU) by an indirect extended Kalman filter (EKF). A delay-based approach for the measurement update is developed to introduce the visual measurement into the fusion without state augmentation. This method supposes that the estimated error state is stable and invariant during the second half of one visual calculation period. Simulation results indicate that delay-based navigation can reduce the computational complexity by about 20% compared with general augmented Vision/INS (inertial navigation system) navigation, with almost the same estimate accuracy. Real experiments were also carried out to test the performance of the proposed navigation system by comparison with the augmented filter method and a referential GPS/INS navigation. [ABSTRACT FROM AUTHOR]

Direct and inverse boundary value problems are solved and the solution of an optimization shape problem is obtained analytically in the case of some nonlinear integral functionals. The plain potential flow of an inviscid fluid is considered in the absence of mass force (Hyp). The flow - unlimited jet - encounters a symmetrical curvilinear obstacle. (the Helmholtz scheme). For myers problems there are derived singular integral equations and the movement is obtained in the auxiliary canonical half plane. Next, a new method of optimization problem is solved analytically. The design of the optimal airfoil is performed. The drag/lift coefficient, nonlinear integral functionals and there geometrical parameters are computed in the case of a given distribution of the velocity or angle on the profile/airfoil. The main applications of this contributions are related to the optimization of leading edges, modeling special airfoils for different categories of low speed small UAVs, but also in determining efficient systems for recovery of light UAVs (deflectors, special braking parachute, determal systems). [ABSTRACT FROM AUTHOR]