Теоретичні основи побудови багаточастотних радіометричних комплексів для виявлення БпЛА на тлі атмосферного випромінювання

Due to the impossibility of hiding the unmanned aerial vehicles (UAV) own radiothermal radiation or reducing its contrast against the background of atmospheric radiation, it is advisable to use highly sensitive radiometric receivers to solve the detection problem. The optimal method for complexing the results of measurements in multichannel radiometric receivers and identifying different types and classes of UAV against the sky in X, Ka, and W wave ranges under different meteorological conditions has been developed. end-to-end optimization of methods and algorithms will reveal the theoretical foundations of the construction of radiometric systems, ranging from the field of registration of electromagnetic fields to the final stages. In cloudless and clear weather, radiometric measurements in the W range will allow to obtain high-precision estimates of the spatial position of UAVs, in the X range of reliable observations in rain, snow, fog. The use of the Ka-band receiver in the radiometric complex will allow to realize the best sensitivity due to the technical achievements of domestic production in the creation of broadband radiometric receivers in this waveband. Studies of the main parameters of UAV detection have been conducted, namely, the probability of erroneous detection alarm and the probability of correct detection. The obtained theoretical results allow to determine signal processing algorithms and optimal structures of radiometric receivers, to analyze the maximum measurement error and to develop recommendations for experiments. Having received a database of radiometric contrasts, it is possible to further implement technical solutions to increase the capabilities of airspace monitoring for UAV detection. Recommendations are given for the practical choice of the UAV detection threshold to ensure the probability of correct detection is not worse than 0.9 for different angles of observation, atmospheric state, size and material of manufacture.

Analysing detection bands of two-spectral reflection method to identify forest species composition

The optical reflection method is considered for detection of the forest areas where coniferous or broadleaved trees are dominant. Statistical modelling of correct detection and false alarm probabilities for identifying dominant (coniferous or broadleaved) tree species by the two-spectral reflection method has been conducted. It has been shown that monitoring enables us to identify dominant (coniferous or broadleaved) tree species with correct detection probability close to 1 and false alarms probability ~ second decimal places for the temperate climate zone at the wavelengths of 532 and 1540 nm or 532 and 1480 nm. As to the subtropical climate zone, due to a great variety of reflection spectra of vegetation, a selection of the spectral detection bands for reliable identification of dominant coniferous or broadleaved tree species is possible only for specific forestlands where the number of evergreen broadleaved and coniferous tree species is relatively small.

SE-Mask R-CNN: An improved Mask R-CNN for apple detection and segmentation

Fruit detection and segmentation is an essential operation of orchard yield estimation, the result of yield estimation directly depends on the speed and accuracy of detection and segmentation. In this work, we propose an effective method based on Mask R-CNN to detect and segment apples under complex environment of orchard. Firstly, the squeeze-and-excitation block is introduced into the ResNet-50 backbone, which can distribute the available computational resources to the most informative feature map in channel-wise. Secondly, the aspect ratio is introduced into the bounding box regression loss, which can promote the regression of bounding boxes by deforming the shape of bounding boxes to the apple boxes. Finally, we replace the NMS operation in Mask R-CNN by Soft-NMS, which can remove the redundant bounding boxes and obtain the correct detection results reasonably. The experimental result on the Minneapple dataset demonstrates that our method overperform several state-of-the-art on apple detection and segmentation.

DOUBLE THRESHOLD SPECTRUM SENSING WITH OPTIMIZATION OF SAMPLES IN COGNITIVE RADIO NETWORKS

In cognitive radio networks spectrum sensing plays a vital role to identify the presence or absence of the primary user. In conventional spectrum sensing one secondary user will make a final decision regarding the availability of licensed spectrum. But Secondary user fail to make a correct detection about the presence of the primary user if he is in fading environment and it causes interference to the licensed users. Therefore to avoid interference to the licensed users and to make correct detection, number of samples is increased, Which increases the probability of detection. In this paper the optimization of samples is proposed to reduce the system overhead and also to increase the propagation time. Simulation results show the optimized value of samples for a given probability of false alarm and also the variation of probability of detection with optimized samples and false alarm is shown in the results. ABSTRAK: Dalam rangkaian radio kognitif, penginderaan spektrum memainkan peranan penting untuk mengenal pasti kehadiran atau ketiadaan pengguna utama. Dalam penginderaan spektrum konvensional, seorang pengguna sekunder akan membuat keputusan akhir mengenai ketersediaan spektrum berlesen. Tetapi pengguna Sekunder gagal membuat pengesanan yang betul mengenai kehadiran pengguna utama jika dia berada dalam persekitaran yang pudar dan menyebabkan gangguan kepada pengguna yang berlesen. Oleh itu untuk mengelakkan gangguan kepada pengguna berlesen dan membuat pengesanan yang betul, jumlah sampel meningkat, yang meningkatkan kemungkinan pengesanan. Dalam makalah ini pengoptimuman sampel dicadangkan untuk mengurangi overhead sistem dan juga untuk meningkatkan waktu penyebaran. Hasil simulasi menunjukkan nilai sampel yang dioptimumkan untuk kebarangkalian penggera palsu dan juga variasi kebarangkalian pengesanan dengan sampel yang dioptimumkan dan penggera palsu ditunjukkan dalam hasil.

The Effects of Rewards on Autonomous Unmanned Aerial Vehicle (UAV) Operations Using Reinforcement Learning

The effects of rewards on the ability of an autonomous UAV controlled by a Reinforcement Learning agent to accomplish a target localization task were investigated. It was shown that with an increase in the reward obtained by a learning agent upon correct detection, systems would become more risk-tolerant, efficient and have a tendency to locate targets faster with an increase in the sensor sensitivity after systems achieve steady-state performance.

Probabilistic characteristics of a two-channel detector with two inertial single-photon photoemission devices and a logical adder

Analytical expressions are obtained for calculating the probabilities of correct detection of a two-channel inertial detector of pulsed optical radiation in the photon counting mode, containing a receiving optical complex of two lens antennas, two single-photon photoemission devices and a logic adder.

Characteristics of detecting the coherent system of selection of moving targets during the matching of the parameters of the square channels.

ntroduction. Operation of adaptive system of moving targets selection is considered, which is represented by single intermittent subtractor with coherent accumulator, which is tuned in frequency. System of moving targets selection is made in the form of two quadrature channels, in which high-frequency passive interference and signal from moving object are converted to video frequency with subsequent rejection, accumulation and comparison with threshold level. Setting the task. Investigation of the effect of the inevitable in practice amplitude and phase misalignments of quadrature channel parameters on the detection characteristics of the adaptive selection system of moving targets. Method. Method of complex variable is used, in which passive interference and signal from moving object at input and output of adaptive selection system of moving targets are presented in the form of pair of real components shifted in phase by ninety degrees. Results. Models of passive interference and signal from moving object are presented taking into account possible in practice amplitude and phase mismatches of quadrature channels parameters. The structure of the processing device is shown, which includes a series-connected generator of quadrature components of passive interference and a signal from a moving object, an adaptive cutting filter in the form of a single intermittent subtraction circuit with a frequency-tuned rejection zone, an adaptive coherent accumulator and a threshold device. Expressions are obtained for complex correlation functions of passive interference and signal from moving object at output of coherent accumulator taking into account inevitable amplitude and phase mismatches of quadrature channels parameters. Dependencies of probability of correct detection of signal from moving object against the background of passive interference from value of deviation of transmission coefficients of quadrature channels and phase mismatch of reference voltages for different values of average frequency of spectral density of passive interference are calculated and constructed. Conclusion. Significant dependence of probability of correct detection of signal from moving object on average frequency of spectral density of passive interference, as well as amplitude and phase mismatches of quadrature channels parameters is shown.

Algorithm for partially decentralized processing of a posteriori information in an integrated detection system

The integration of detectors can lead to an improvement in their performance. In this case, heterogeneous (radar, optoelectronic, radiotechnical) detectors can be combined. The task of integrating radar detectors is relevant for multi-positional radar stations. The construction of integrated identification systems can also be viewed as the task of combining detectors of their objects. There are three main options for combining detectors: centralized, partially decentralized, and fully decentralized. Centralized detection implements aggregation at the primary processing level, which provides potential characteristics, but it is difficult to implement such a combination in practice. Partially decentralized detection is implemented more simply, when preliminary detection decisions are made in the integrated detectors, which are then jointly processed and a final decision is formed. In joint processing of preliminary decisions, the probabilities of correct detection and false alarm of complexed detectors are usually used. The article presents an algorithm for partially decentralized combining of detectors using posterior probabilities of correct detection and false alarm. According to the results of computer simulation of the algorithm, characteristics were obtained that indicate an increase in the quality of detection relative to the best of the combined detectors. The use of the usual probabilities of correct detection and false alarm when combining does not guarantee such an increase – for certain characteristics of the detectors being combined, the effect of combining can be negative: the detection quality after combining will be lower relative to the best of the combined detectors.

Analysis of the Possibility of Remote Laser Detection of Propane Leaks

Propane is one of the main components of the wide fraction of light hydrocarbons (WLHF). A large volume of WLHF is transported to petrochemical plants via pipelines. Control of pipelines is carried out by means of in-line pressure sensors. However, they are ineffective for detecting low-intensity leaks.To detect low-intensity propane leaks from pipelines, it is promising to use a remote laser gas analyzer installed on an aircraft.The article is devoted to the analysis of the possibilities of remote laser detection of propane leaks.Based on the data on the absorption of propane and atmospheric gases, the wavelengths of 3370 nm (in the maximum absorption of propane) and 3550 nm (in the spectral region where there is no absorption of propane) were chosen as the sounding wavelengths.It was believed that the monitoring of propane leaks is carried out by a lidar installed on the aircraft in a monostatic sensing scheme. The method of differential absorption with scattering from the earth's surface is used.To detect propane leaks, an information parameter was used, which is equal to the ratio of the power recorded by the receiver at wavelengths of 3370 nm and 3550 nm. The value of the information parameter was calculated for different heights of the propane layer on the earth's surface and different concentrations of propane in the layer.Statistical modeling was performed to quantify the effectiveness of remote detection of propane leaks.In the work, the probability of correct detection of a propane leak (detection of a leak when it is in reality) and the probability of false alarms (detection of a leak when it is not in reality) were calculated.The decision to detect propane leaks was made when the value of the information parameter was less than the threshold.The results of mathematical modeling show that for a propane content in the leak of at least 0.17 % (an order of magnitude less than the concentration limit of flame propagation), the problem of remote detection of propane leaks from the pipeline can be solved with a probability of correct detection of more than 0.999 and a probability of false alarms of less than 0.001 with a thickness of the propane layer on the earth's surface of at least 20 cm.