Signal Processing Methods Research Articles

Enhancing multiple sclerosis diagnosis: A comparative study of electroencephalogram signal processing and entropy methods.

As one of the most common neurodegenerative diseases, Multiple sclerosis (MS) is a chronic immune-driven disorder that affects the central nervous system (CNS). Due to the variety of symptoms, accurately diagnosing MS demands rigorous attention to differential diagnosis, as various disorders can closely mimic its clinical and paraclinical features. Although MR imaging techniques are gold standards in diagnosing MS, the feasibility of advanced Electroencephalogram (EEG) signal processing methods is discussed in this study to detect patients with MS disorder. EEG signals from 50 individuals were evaluated through entropy-based methods. Sixteen distinct entropy methods were employed to extract features, which were used to train several machine-learning algorithms for classifying MS patients. Furthermore, each entropy method was individually evaluated to identify the most effective approach for MS diagnosis. A regional analysis of the EEG channels was conducted to determine the most informative regions for classification. The results indicated that the proposed method outperformed previous studies and achieved highly effective results in the classification of MS patients.

QUANTUM-INSPIRED CRYPTO SOLUTIONS:PIONEERING SUSTAINABLE FREQUENCIES

This project aims to develop a secure application that leverages quantum, computing and Google Quantum AI with a strong focus on sustainability .The application will employ advanced cryptographic techniques, including XOR gate signal processing and control engineering binomial z-transform methods, to ensure robust security and effectively prevent cybercrime. The application will be powered by renewable energy sources, specifically solar power, hydroelectric energy, and wind turbines. These renewable sources will be integrated with the quantum processor, utilizing AI predictive optimization control to efficiently manage energy consumption and maximize performance.This integration ensures that the application operates at peak efficiency while minimizing its carbon footprint. For example, the quantum processors operation can be optimized to align with the availability of renewable energy. During peak sunlight hours, solar panels can supply abundant power to the quantum processor, while the AI system predicts and adjusts the processors workload accordingly. Similarly, hydroelectric and wind turbine-generated power can be monitored and managed to provide consis- tent energy, ensuring that the quantum processor maintains high efficiency even when renewable energy availability fluctuates. Overall, this project represents a pioneering effort in the integration of quantum computing with sustainable practices, aiming to provide a secure and eco-friendly solution for modern cryptographic challenges.

Optimized convolutional neural network using African vulture optimization algorithm for the detection of exons

The detection of exons is an important area of research in genomic sequence analysis. Many signal-processing methods have been established successfully for detecting the exons based on their periodicity property. However, some improvement is still required to increase the identification accuracy of exons. So, an efficient computational model is needed. Therefore, for the first time, we are introducing an optimized convolutional neural network (optCNN) for classifying the exons and introns. The study aims to identify the best CNN model that provides improved accuracy for the classification of exons by utilizing the optimization algorithm. In this case, an African Vulture Optimization Algorithm (AVOA) is used for optimizing the layered architecture of the CNN model along with its hyperparameters. The CNN model generated with AVOA yielded a success rate of 97.95% for the GENSCAN training set and 95.39% for the HMR195 dataset. The proposed approach is compared with the state-of-the-art methods using AUC, F1-score, Recall, and Precision. The results reveal that the proposed model is reliable and denotes an inventive method due to the ability to automatically create the CNN model for the classification of exons and introns.

Improving Tracking of Selective Attention in Hearing Aid Users: The Role of Noise Reduction and Nonlinearity Compensation.

Hearing impairment (HI) disrupts social interaction by hindering the ability to follow conversations in noisy environments. While hearing aids (HAs) with noise reduction (NR) partially address this, the "cocktailparty problem" persists, where individuals struggle to attend to specific voices amidst background noise. This study investigated how NR and an advanced signal processing method for compensating for nonlinearities in EEG signals can improve neural speech processing in HI listeners. Participants wore hearing aids with NR, either activated or deactivated, while focusing on target speech amidst competing masker speech and background noise. Analysis focused on temporal response functions to assess neural tracking of relevant target and masker speech. Results revealed enhanced neural responses (N1 and P2) to target speech, particularly in frontal and central scalp regions, when NR was activated. Additionally, a novel method compensated for nonlinearities in EEG data, leading to improved signal-to-noise ratio (SNR) and potentially revealing more precise neural tracking of relevant speech. This effect was most prominent in the left-frontal scalp region. Importantly, NR activation significantly improved the effectiveness of this method, leading to stronger responses and reduced variance in EEG data and potentially revealing more precise neural tracking of relevant speech. This study provides valuable insights into the neural mechanisms underlying NR benefits and introduces a promising EEG analysis approach sensitive to NR effects, paving the way for potential improvements in HAs.Significance Statement Understanding how hearing aids (HAs) with noise reduction (NR) improve selective auditory attention in noisy environments is crucial for future advancements. This study investigated the neural effects of NR in hearing-impaired listeners using EEG. We observed significantly enhanced neural responses (N1 and P2 peaks) to target speech with NR activated, suggesting improved speech tracking in frontal and central scalp regions. The advanced signal processing method also compensated for nonlinearities in EEG data, improving the signal-to-noise ratio (SNR) and revealing more precise neural tracking, particularly in the left-frontal scalp region. This study sheds light on the neural mechanisms behind NR benefits and introduces a promising EEG analysis method sensitive to NR effects, paving the way for optimizing future HAs.

Real-time single-pixel video imaging based on deep learning

The emergence of compressed sensing (CS) theory has enabled the development of single-pixel cameras that achieve high-resolution imaging using a single photodetector. However, traditional CS reconstruction algorithms require significant computational time and face an inherent trade-off between imaging resolution and frame rate, limiting current single-pixel cameras to static scene imaging. A key challenge lies in achieving real-time single-pixel imaging with both high frame rate and high resolution. This paper proposes a real-time single-pixel imaging technology based on deep learning. We design a deep convolutional neural network architecture incorporating residual networks to simulate the measurement and reconstruction process of CS-based single-pixel imaging. The network is trained on an image dataset and subsequently deployed for single-pixel imaging. The trained network can complete image reconstruction at a low sampling rate with minimal latency, enabling real-time single-pixel video capture at 128×128 resolution with 33 frames per second (fps) at a 4\% sampling rate. Furthermore, we implement a four-channel parallel signal processing method to achieve real-time single-pixel imaging video at 256×256 resolution at 33 fps.

Radar Signal Processing and Its Impact on Deep Learning-Driven Human Activity Recognition

Human activity recognition (HAR) using radar technology is becoming increasingly valuable for applications in areas such as smart security systems, healthcare monitoring, and interactive computing. This study investigates the integration of convolutional neural networks (CNNs) with conventional radar signal processing methods to improve the accuracy and efficiency of HAR. Three distinct, two-dimensional radar processing techniques, specifically range-fast Fourier transform (FFT)-based time-range maps, time-Doppler-based short-time Fourier transform (STFT) maps, and smoothed pseudo-Wigner–Ville distribution (SPWVD) maps, are evaluated in combination with four state-of-the-art CNN architectures: VGG-16, VGG-19, ResNet-50, and MobileNetV2. This study positions radar-generated maps as a form of visual data, bridging radar signal processing and image representation domains while ensuring privacy in sensitive applications. In total, twelve CNN and preprocessing configurations are analyzed, focusing on the trade-offs between preprocessing complexity and recognition accuracy, all of which are essential for real-time applications. Among these results, MobileNetV2, combined with STFT preprocessing, showed an ideal balance, achieving high computational efficiency and an accuracy rate of 96.30%, with a spectrogram generation time of 220 ms and an inference time of 2.57 ms per sample. The comprehensive evaluation underscores the importance of interpretable visual features for resource-constrained environments, expanding the applicability of radar-based HAR systems to domains such as augmented reality, autonomous systems, and edge computing.

Demodulation of Fibre Bragg Grating Sensors by Using Cumulative Sum as a Preprocessing Method

Fibre Bragg gratings are one of the most popular sensors with a huge number of applications. Their most important advantage is signal modulation consisting in shifting the spectrum in the wavelength domain. Determining the wavelength shift is the most important issue in precise measurements of various quantities. New demodulation methods are constantly being developed. Many of them have good properties, but they do not gain much polarity. This is partly due to their high complexity and partly to a small improvement in the accuracy of determining the wavelength shift in relation to classical methods. Cumulative preprocessing is a very simple method of spectrum processing with the property of reducing the influence of noise on the result. The method can be used directly or with additional algorithms. In this article, we demonstrate the advantages of this method and the possibilities of combining it with other signal processing methods. We show that this method is much simpler than the spectrum denoising methods and additionally simplifies the next stage of the algorithm, i.e., determining the wavelength shift itself.

Species-specific Antimony Isotope Analysis by High Performance Liquid Chromatography Coupled with Multicollector ICPMS Using Hydride Generation as an Interface.

A novel method has been developed for the simultaneous online determination of the isotopic compositions of different antimony (Sb) species in a single analytical run using high-performance liquid chromatography (HPLC) coupled with multicollector inductively coupled plasma mass spectrometry (MC-ICPMS), with hydride generation (HG) serving as the interface. Various parameters affecting the precision of Sb isotope analysis including HG conditions, transient signal processing methods and peak integration windows, were optimized. The linear regression slope method and a 100% peak integration window provided the optimal precision. Under optimized conditions, our method achieved external 2SD precisions better than 0.05‰ for both Sb(III) and Sb(V), with minimal consumption of 0.5 ng for Sb(III) and 5 ng for Sb(V). Furthermore, flow injection (FI) coupled with HG-MC-ICPMS demonstrated precise Sb isotopic analysis with sample requirements as low as 0.25 ng. The proposed methods were validated by analyzing δ123Sb in synthetic solutions and reference materials. Additionally, it was applied to investigate isotopic fractionation during the reduction of Sb(V) by KI, revealing preferential reduction of the light Sb isotope(121Sb). The isotopic compositions of Sb(V) varied from -0.04- 1.18‰, fitting well with a Rayleigh fractionation model and yielding a fractionation factor (αSb(III)-Sb(V)) of 0.99831. In summary, this approach enables high precision isotopic analysis of Sb(III) and Sb(V) simultaneously with reduced sample consumption, providing a powerful tool for investigating Sb isotopic fractionation in various environmental processes and advancing our understanding of the Sb biogeochemical cycle.

Low-loss dispersion compensation of high-speed data with integrated, concatenated gratings.

Optical transceivers serve as the backbone of high-speed data transmission over optical fiber in communication systems and there is a constant challenge to keep cost and power consumption low while increasing data capacities. However, dispersion impairments introduced by the transmission of data over optical fiber, limit the baud rates and fiber reach. In this paper, we demonstrate a low loss, transmission-based grating device to compensate for dispersion up to 20 km by the concatenation of gratings where the operation occurs outside of the stopband in the region where transmissivity is high, and the normal dispersion magnitude is large. High-speed measurements with 28.05GBd/s PAM4 data show a significant improvement of >14 dB improvement in the transmitter dispersion eye closure quaternary after 10 km, and improvement in the dispersion penalty up to 20 km when dispersion compensation is applied without digital signal processing (DSP) methods. Further analysis by scanning the carrier wavelength across the grating stopband while simultaneously measuring the bit-error rate (BER) reveal that improvements in the BERs correspond with the nonlinear group delay curve and low BERs can be realized with vestigial filtering of the modulated data. The presented integrated optical system provides a low-loss, CMOS-compatible solution for the effective management of dispersion and may enable augmented fiber reaches and data rates in transceivers.

Acoustic-to-Hyper-Spectral: Real-Time Perimeter Intrusion Detection System Monitoring through Learnable Filters and Hyper-Spectral Image Generation from Distributed Acoustic Sensing Systems

This paper presents an integrated distributed acoustic sensing (DAS) system with artificial intelligence to provide real-time system monitoring for fence perimeter and buried system applications. The DAS system is a Rayleigh backscatter based fibre optic sensing system that has been deployed in two real-world, commercial applications to detect acoustic wave propagation and scattering along perimeter lines, and classify intrusions accurately. What we believe to be three novel signal processing methods are proposed to train filters for automatically selecting frequency bands from the power spectrum and generating hyper-spectral images from the data gathered by the DAS system without expert knowledge. The hyper-spectral images are analyzed by a neural network based object detection model. The system achieves 81.8% accuracy on a fence perimeter installation and 60.4% accuracy on a buried system application in detecting and classifying various intrusion events. The evaluation interval of the integrated DAS system framework between event sensing and detection does not exceed 5 s.

Ultrasonic probes and echo time algorithms in ultrasonic gas flow measurement systems: Progress and perspectives

Ultrasonic gas flowmeters employ non-intrusive measurement techniques, characterized by rapid responsiveness and exceptional anti-interference capabilities. These attributes not only minimize disruption to the gas during measurement but also facilitate dynamic process control while ensuring robust performance under complex operational conditions. This paper provides an overview of the key components of ultrasonic gas measurement systems, briefly summarizing the fundamental principles of commonly used measurement methods. After focusing on the evolution of transducer structures and materials within ultrasonic probes, it categorizes different types of transducers and outlines the latest designs of excitation circuits in both hardware and software. The review also critically assesses the determination of echo signal reception characteristics and the accuracy and effectiveness of time-of-flight calculations. Based on innovative analyses of the critical nodes within the measurement system's components, a framework system is established for corresponding measurement scenarios. The measurement results show that the repeatability error of the new transducer remains below 0.3%. The optimized signal processing method expands the measurable flow range to 30–1200 m3/h, and the zero drift is reduced to approximately half of the system's original zero drift. This paper aims to provide clear guidance for researchers and professionals in related industries, enabling them to conduct more in-depth studies based on their research interest and enhancing their understanding of ultrasonic measurements.

OPTIMIZATION OF RADIO SYSTEM STRUCTURES FOR DIRECTION FINDING OF SIGNAL SOURCES WITH COMPLETELY KNOWN PARAMETERS

Context. Direction finders are critical components of radar and radio navigation systems, particularly when installed onboard UAVs. The increasing use of unmanned systems has heightened the need for precise direction finding and wide-angle unambiguous measurements. The primary challenge is to strike a balance between achieving high accuracy and maintaining a broad range of unambiguous measurement angles. Objective. To simultaneously enhance direction finding accuracy and expand the range of unambiguous measurement angles through the statistical synthesis of functionally deterministic signal processing methods in multichannel direction finders. Method. The approach is grounded in the statistical theory of optimization for radio remote sensing and radar systems. For the specific type of signals considered in this study, represented by functional-deterministic models, the likelihood function is constructed, and its maxima are determined for various configurations of multi-antenna direction finders. The statistical synthesis results are validated through simulation and in-situ experiments. Results. Theoretical analysis and simulation modeling confirm that in dual-antenna direction finders, there is a trade-off between high resolution and the range of unambiguous direction finding angles. An improved signal processing method is developed for a four-antenna direction finder, utilizing a pair of high-gain and a pair of low-gain antennas. To achieve the maximum possible bearing accuracy within the range of unambiguous direction finder measurements, a new signal processing method is synthesized for a sixelement radio receiver, combining the processing of signals in two amplitude direction finders and one phase direction finder. Conclusions. The proposed approach achieves an optimal balance between resolution and angle range, making it particularly suitable for onboard systems of unmanned aerial vehicles. Simulation results confirm the effectiveness of the proposed method, highlighting its potential for implementation in modern radio systems.

CONSTRUCTING SENSOR SIGNAL PROCESSING CHANNEL FOR AUTONOMOUS ROBOTIC PLATFORMS

Context. The development of autonomous mobile robotic platforms has advanced rapidly, especially in cyber-physical systems where integrating physical components and computational resources is vital. A key challenge in such platforms is the efficient realtime processing of sensor signals under limited computational resources, enabling robots to operate independently of human intervention. Traditional signal processing methods demand significant power, which may limit mobile platforms constrained by energy and resources. This research focuses on restructuring sensor signal processing channels using digital bandpass filters while overcoming technical challenges posed by limited resources. Objective. The goal is to create an efficient method for processing sensor signals in autonomous mobile platforms with constrained resources. This involves using low-order bandpass filters, capable of adjusting their characteristics and improving quality through sequential connection of identical filters. Reducing the computational load allows for enhanced overall performance of cyber-physical systems, improving efficiency under changing conditions and enabling autonomous task completion. New computational formulas are also proposed to simplify the design and better utilize onboard resources. Method. The improved method for constructing sensor signal processing channels uses identical low-order frequency-dependent components, sequentially connected to solve challenges faced by higher-order components. This approach simplifies coefficient calculations for cutoff frequencies and improves filter performance by increasing the order and quality. The method achieves a quasilinear phase-frequency characteristic, ensuring minimal distortion in the processed signals, while significantly reducing computational requirements. Results. The proposed method effectively reduces computational costs while maintaining high performance in sensor signal processing. The new formulas allow for calculating filter coefficients with fewer resources, suitable for autonomous systems. Modelling and experimental verification confirm that this method lowers the computational load and enhances filter performance, enabling more efficient sensor data processing, extended battery life, and improved system reliability. Conclusions. This research presents an efficient approach to sensor signal processing for resource-constrained autonomous robotic platforms. Sequentially connecting identical frequency-dependent components reduces computational costs while maintaining high signal processing quality. These findings are recommended for real-time applications requiring efficient resource utilization, contributing to improved autonomy and adaptability in mobile robotic systems.

HARDWARE IMPLEMENTATION OF AN ANALOG SPIKING NEURON WITH DIGITAL CONTROL OF INPUT SIGNALS WEIGHING

Context. Significant challenges facing hardware developers of artificial intelligence systems force them to look for new nonstandard architectural solutions. One of the promising solutions is the transition from von Neumann’s classic architecture to neuromorphic architecture, which at the hardware level tries to imitate the work of the neural network of the human brain. A neuromorphic processor built as hardware implementation of a spiking neural network consists of a large number of elementary electronic circuits that structurally and functionally correspond to neurons. Thus, the design of hardware implementation of a spiking neuron as the basic building element of a neuromorphic processor is of great scientific interest. Objective. The goal of the work is to design an analog spiking neuron hardware implementation with digital control of input signals by binary synaptic weighting coefficients. Method. Designing is performed at the logical/schematic and topological levels of the design flow using modern tools of electronic design automation. All proposed schematic and layout solutions are verified and simulated using computer aided design tools to prove their functionality. Results. The schematic and layout solutions have been developed and investigated for the hardware implementation of the spiking analog neuron with digital control of input signals by binary synaptic weighting coefficients to be the basic building element of a spiking neural network of the neuromorphic processor. Conclusions. The proposed hybrid design of the spiking neuron hardware implementation benefits by combining the simplicity of analog signal processing methods in the neuron with digital control of the state of the neuron using binary weighting coefficients. The simulation results confirm the functionality of the obtained schematic/layout solutions and demonstrate the possibility of implementing logical functions inherent in the perceptron. The prospects for further research may include the design of hardware implementation for a spiking neural network core based on the developed schematic and layout solutions for the spiking neuron.

The Resampling Methods Direct Sequence Spread Spectrum Signal’s Demodulator Implementation

Relevance. The direct spread spectrum signals are widely used in navigation and communication systems recently. These signals prevail in modern satellite navigation systems and are used in various communication systems with code division multiplexing in particularly. In this regard, the tasks of building direct spread spectrum signals’ demodulators have the key importance. Mach importance in the construction of demodulators is the problem chip rate variability.The purpose of the study is to propose a demodulator structure focused on solving this problem.Methods. The research is based on computer modeling methods.Decision. The paper proposes an approach to the construction of the direct spread spectrum signal’s demodulators based on modern methods of digital signal processing. It is shown that the main advantage of the proposed approach is the possibility of rebuilding the variable chip rate demodulators. Based on the results obtained, a scheme for the direct spread spectrum signals demodulator using resampling methods is proposed. Resampling, in turn, is implemented on the basis of polynomial interpolation using Lagrange polynomials. The structure of the resampler is proposed, similar to the structure of an interpolating filter with a finite impulse response. The presented simulation results show the effectiveness of the proposed approach.Novelty. It seems that the currently common methods of implementing direct spread spectrum signal in terms of delay synchronization do not sufficiently meet modern requirements. The implementation of delay synchronization schemes based on resampling is practically not discussed in well-known works. At the same time, modern methods and devices of digital signal processing make it possible to ensure an effective hardware implementation of the scheme in question. In this context, the approach proposed in the paper to the construction of demodulators seems to be very relevant.Significance. The results of the work can be used in the construction with direct spread spectrum signals’ demodulators for a wide range of communication and navigation systems. The synchronous sampling structure proposed in this paper is very promising, especially for variable chip rate demodulators.

Improved singular spectrum decomposition method for resonance recognition of air-coupled ultrasonic signals in through-transmission steel plate detection

Abstract Air-coupled ultrasonic resonance detection is an emerging technique that eliminates the need for coupling agents in conventional ultrasonic testing and has a wide range of applications. However, this technique faces challenges, such as weak signal strength and susceptibility to noise, primarily due to the mismatch of acoustic impedance at the air–solid interface and sound attenuation in the air. This study proposed an improved signal processing method based on singular spectrum decomposition (SSD), optimizing the frequency components of each iteration using singular spectrum analysis. Additionally, a weighted Hurst exponent was introduced as a selection criterion to identify useful components and reconstruct the signal. Simulation results demonstrated the effectiveness of this method in denoising components and extracting weak resonance frequency features, especially in low signal-to-noise ratios (SNRs). Compared with conventional SSD and empirical mode decomposition, the proposed method improved the SNR by 39.4% and 301.33%. Furthermore, repeated experimental studies on steel plates with varying air lift-off distances and thicknesses confirmed that the proposed method could effectively reduce noise, thereby enabling accurate measurement of steel plate thickness.

Forecasting River Water Temperature Using Explainable Artificial Intelligence and Hybrid Machine Learning: Case Studies in Menindee Region in Australia

Water temperature (WT) is a crucial factor indicating the quality of water in the river system. Given the significant variability in water quality, it is vital to devise more precise methods to forecast temperature in river systems and assess the water quality. This study designs and evaluates a new explainable artificial intelligence and hybrid machine-learning framework tailored for hourly and daily surface WT predictions for case studies in the Menindee region, focusing on the Weir 32 site. The proposed hybrid framework was designed by coupling a nonstationary signal processing method of Multivariate Variational Mode Decomposition (MVMD) with a bidirectional long short-term memory network (BiLSTM). The study has also employed a combination of in situ measurements with gridded and simulation datasets in the testing phase to rigorously assess the predictive performance of the newly designed MVMD-BiLSTM alongside other benchmarked models. In accordance with the outcomes of the statistical score metrics and visual infographics of the predicted and observed WT, the objective model displayed superior predictive performance against other benchmarked models. For instance, the MVMD-BiLSTM model captured the lowest Root Mean Square Percentage Error (RMSPE) values of 9.70% and 6.34% for the hourly and daily forecasts, respectively, at Weir 32. Further application of this proposed model reproduced the overall dynamics of the daily WT in Burtundy (RMSPE = 7.88% and Mean Absolute Percentage Error (MAPE) = 5.78%) and Pooncarie (RMSPE = 8.39% and MAPE = 5.89%), confirming that the gridded data effectively capture the overall WT dynamics at these locations. The overall explainable artificial intelligence (xAI) results, based on Local Interpretable Model-Agnostic Explanations (LIME), indicate that air temperature (AT) was the most significant contributor towards predicting WT. The superior capabilities of the proposed MVMD-BiLSTM model through this case study consolidate its potential in forecasting WT.

Proteinoid-polyaniline neuromorphic composites for audio recognition

Abstract We present an innovative neuromorphic system using a proteinoid-polyaniline (PANI) composite for recognition of audio inputs of the English alphabet. Neuromorphic devices, which draw inspiration from the neural networks of the brain, have emerged as very promising potential solutions for efficient signal processing. The proteinoid-PANI composite was synthesized through a template-free method, resulting in a unique nanostructure consisting of both nanorods and nanospheres. Principal component analysis (PCA), spectrogram analysis, and temporal spiking response analysis were among the signal processing methods used to examine the composite's audio response to English alphabet stimuli. The system showed a moderate positive correlation between input and output signals, unique time-frequency response patterns, and convoluted spiking behaviour. In addition, the output amplitude showed less variation compared to the input, while maintaining the same temporal characteristics.
Microscopic analysis provided detailed information about the morphology of the composite. The nanorods displayed an optimal aspect ratio and had diameters of around 
100~nm, while the nanospheres varied in size, ranging from 200 to 500~nm in diameter. The nanostructure, morphological characteristics, and signal processing properties of the proteinoid-PANI composite demonstrate its potential for advanced applications in neuromorphic computing and signal processing, particularly in speech recognition and human-machine interaction.

Optimization of the structure of the security communication system of on-board and ground equipment of the integrated radio complex for recovery of ground targets

The basis of the offensive ground grouping of the armies of the militarily leading countries are highly maneuverable objects (launchers of tactical missiles, batteries, platoons, sections of self-propelled guns, rocket launchers and mortars, self-propelled installations of anti-aircraft guns and anti-aircraft guided missiles with an autonomous system guidance, helicopters on landing sites, defeating these objects is artillery. According to estimated data, artillery in the confrontation with Russia accounted for 70 to 80% of all losses suffered by the parties to the conflict. The effectiveness of artillery fire depends on the total error of the shot, the number of guns involved in hitting the target, the rate of fire of artillery systems, the striking effect of ammunition and their number, the area of the target, the timeliness of its detection and opening fire on it. At the same time, the speed of detecting a highly maneuverable target and the accuracy of determining its coordinates play a key role in attacking the enemy with fire. Therefore, in the process of preparing data for firing and fire control, artillery reconnaissance means are of essential importance. The key tactical characteristics of artillery reconnaissance are maximum range and informativeness. The combination of these tasks consists in the integration of air and ground means of surveillance within the framework of a single automated radar complex for reconnaissance of firing positions and unmanned aerial vehicles (UAVs). The exchange of information between the ground and air components of the complex, the transmission of data to the automated guidance and fire control system takes place through the interference-protected communication channels using signal repeaters placed on the UAV. For the practical implementation of such complexes, new and improved known methods of signal generation and processing are proposed. The developed phase, clock and frame synchronization systems are based on the modernized scheme of the phase auto-frequency adjustment system, which makes it possible to reduce the time of entering into synchronization and increase the tracking band, significantly improve the quality of signal demodulation, and significantly increase the immunity of communication channels in the conditions of a complex interference environment. The mathematical models developed in the work made it possible theoretically and experimentally, through simulation modeling on computer, without material costs to evaluate the effectiveness of using the proposed new and improved known methods of synthesis of hardware to ensure communication with the necessary reliability and reliability of information transmission. Based on the results of simulation modeling, the structure of the radio channel using the relay of information signals and control commands, the structural diagrams of the transmitting and receiving devices, and the main technical characteristics of their constituent parts are determined. This made it possible to implement an experimental sample of the proposed system of interference-protected communication, to evaluate the achieved technical characteristics and to determine ways to improve them.

Novel Digital Signal Processing Method for Data Acquired From Low Coherence Interferometry

Novel Digital Signal Processing Method for Data Acquired From Low Coherence Interferometry