Перегляд за Автор "Buhaiov, M. V."

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    Energy detector of stochastic signals in noise uncertainty
    (КПІ ім. Ігоря Сікорського, 2023) Buhaiov, M. V.
    Wide use of software-defined radio has led to a significant sophistication of electronic environment. This is mainly due to ability of generation signals of almost any shape. To detect signals with an unknown dynamic frequency-time structure, it is advisable to use advanced energy detector algorithms. The purpose of this article is to automate processes of stochastic signals detection and time parameters estimation under the conditions of unknown frequency-time structure of signals and noise power. The essence of proposed method is to detect and track temporal energy changes averaged over L samples of received signal in selected frequency channel. Threshold value for a given probability of false alarm is calculated using current estimates of signal power. This threshold is dynamic and is refined only in time intervals free from the signals. In those time windows where energy exceeds threshold, a decision is made about the presence of a signal. An algorithm for detecting stochastic signals is proposed. If a signal is present at the initial moment of time, proposed algorithm can detect only its end by a sharp decrease of signal energy. After that, new noise level is estimated and threshold value is refined. Detection curves of proposed algorithm are obtained. It is shown that when number of samples L is increased by an order, the gain in signal-to-noise ratio in signal detection is about 4 dB. The maximum value of correct detection probability of a pulse signal is achieved with the same pulse duration and the length of the integration interval. Compared to method of signal smoothing with moving average window, proposed method has less computational complexity, simplifies the search for signal time boundaries, and gives smaller errors in signal duration estimates. Recommendations for the implementation of developed algorithm are formulated.
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    Fast Spectrum Sensing Method for Cognitive Radio
    (КПІ ім. Ігоря Сікорського, 2020) Buhaiov, M. V.
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    Iterative Method for Noise Power Estimating at Unknown Spectrum Occupancy
    (КПІ ім. Ігоря Сікорського, 2022) Buhaiov, M. V.
    Noise power estimating is the core of modern radio monitoring systems for solving tasks of spectrum occupancy calculation, detecting and estimating signal parameters. The growth of electronic devices number leads to an increase in overall noise level and its fast fluctuations. These devices often emit pulses or separate carriers. Since radio monitoring equipment must operate under these conditions, it may not be possible to exclude these components from radio noise measurements. It was shown that in some cases an increase in the noise power by 20% of the expected value leads to an increase in the false alarm rate by an order. The aim of this work is to develop and explore an iterative method for estimating the noise power with an unknown occupancy of the analysis frequency band, which will have low computational complexity and estimates independent of spectrum occupancy. The essence of the proposed method consists in two-threshold division of frequency samples into signal and noise by a statistical criterion using the coefficient of variation of spectral estimates. Thresholds are selected for a given false alarm rate. When threshold value of the coefficient of variation is exceeded, it is considered that there are occupied frequency channels in the spectrum, and each frequency sample is compared with the second threshold. Those samples that have exceeded the threshold are considered signal, and the rest – noise. The described procedure is then repeated for noise samples until all signal samples have been discarded. Also was developed method for calculating the noise power in time domain using the obtained noise power in frequency domain. Algorithm evaluation has shown that it remains robust for spectrum occupancy up to 60%. In this case, the relative error in estimating the noise power does not exceed 5%, and the average number of iterations of the algorithm grows with increasing occupancy and does not exceed 10.
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    Method for Selecting Pulsed Signals by Their Duration in Fading Channels
    (КПІ ім. Ігоря Сікорського, 2024) Buhaiov, M. V.; Zakirov, S. V.
    The duration of pulsed signals is one of the main parameters to be estimated in radio monitoring systems. When signals propagate in channels with deep fading, even at high signal-to-noise ratios, the pulse shape will be distorted. In sophisticated electronic environment, it is also may be random interference in signal processing channel, which leads to the occurrence of false pulses with random durations. Therefore, the values of the signal pulses durations will be concentrated near their true value, and the rest of the detected pulses will have a significantly random duration. That’s why, the development and study of methods for selecting pulse signals by their durations in sophisticated signal environment is actual scientific problem. The aim of the work is improving pulsed signals processing methods in fading channels by selecting its’ durations. The study found that the estimates of signal pulse durations are normally distributed. Pulse durations that are not related to signals are subjected to an exponential distribution. The input data for the proposed method is only a sample of measured pulse durations. The values of the parameters of both the exponential and normal distributions are unknown. In this case, the problem of selecting pulses by their durations is formalized to the estimation of the mean values of normal distributions. To do this, it is proposed to search for the maxima of the smoothed estimate of the probability density function. The scientific novelty of the obtained results is that a method for estimating the mean value of a normal distribution at the background of exponentially distributed values was proposed. An example of this approach is the estimation of pulsed signal durations in channels with deep fading and impulse interference. Based on the developed method, algorithms for automatic pulse selection for radio monitoring systems can be implemented.
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    Method of Complex Envelope Processing for Signal Edges Detection
    (КПІ ім. Ігоря Сікорського, 2023) Buhaiov, M. V.
    Problem statement. The need of information processing automation in modern radio monitoring systems stimulates development of flexible methods for signal detection and its parameters estimation in time domain. A priori uncertainty of signal time-frequency structure complicates the automatic determination of signals edges. Purpose. The purpose of the article is subsequent automation of radio frequency spectrum analysis process by developing and implementing a method for determining signals time edges under conditions of a known noise power and signal-to-noise ratio. Method. To determine signal time edges in given frequency channel, square of signals’ complex envelope is first calculated, smoothed with moving average window and compared with threshold. Threshold is calculated as a quantile of gamma distribution using Wilson-Hilferty approximation of х 2 distribution quantiles for a given probability of false alarm. An analytical expression is obtained for calculation length of moving average window depending on signal-to-noise ratio. An algorithm has been developed for determining signals’ time parameters and filtering them by duration. Unknown noise power value in frequency channel can be replaced by its estimate under the assumption that frequency channel is not constantly occupied and noise level is estimated on signal-free time intervals. Conclusions. Proposed method makes it possible to automatically determine edges of signal with an arbitrary structure at signal-to-noise ratio values from -6 dB. Adjustable length of moving average window makes it possible to reduce the error in determining signal time parameters by 2-4 times with an increase in the signal-to-noise ratio compared to a fixed window length. Prospects for further research in this direction should be focused on development and implementation of methods for detection signal edges under conditions of an unknown noise level.
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    Spectrum Smoothing Method for OFDM Signals Detection in Frequency Selective Channel
    (КПІ ім. Ігоря Сікорського, 2021) Buhaiov, M. V.
    Orthogonal Frequency Division Multiplexing (OFDM) technology has become widespread in civil and military radio systems, especially in channels with frequency selective fading. Due to the large number of OFDM signal schemes, an urgent task for modern radio monitoring systems is development of methods and algorithms for detecting such signals that will be stable in the uncertainty of OFDM signal structure and electromagnetic environment. At the stage of detection, the characteristic feature of OFDM signal is presence of frequency channels in its spectrum envelope. In this research, an algorithm for detecting an OFDM signal in the frequency domain and for estimating the number of frequency channels and duration of the interval of orthogonality was developed. To make a decision whether signal is present in realization of the normalized to the energy spectrum, its variation was used. This approach avoids estimating noise power. In case of signal samples detecting spectrum is double-smoothed using moving average. This provides better smoothing than with a single long window. Thereafter, double thresholding is performed. The second threshold is calculated using samples that have not exceeded the first threshold. Samples that have exceeded the second threshold are considered signal. Next, a search is made for occupied frequencies with a given bandwidth. The samples located in this band are re-smoothed and give spectrum trend, which is used as a threshold to determine the boundaries of frequency channels. OFDM signal is considered detected if equidistant frequency channels were found. After that, duration of the interval of orthogonality is calculated. The proposed method requires a slight complication of the spectral analysis procedure based on the fast Fourier transform. Proposed method can be used for improving broadband radio monitoring systems and provide practically simultaneously implementation procedure of OFDM signal detection-recognition.
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    Аlgorithm for Spectrum Sensing and Signal Selection by External Parameters
    (КПІ ім. Ігоря Сікорського, 2024) Buhaiov, M. V.
    For modern radio monitoring, a panoramic view of a wide frequency band and signal selection is its most important part. The constant growth of the number of radio electronic devices and the expansion of the instantaneous bandwidth of analysis in modern radio receiving devices leads to the fact that a significant number of analog and digital signals can be observed at the same time. Automatic adaptation of radio monitoring system to further signal processing is possible due to preliminary signal selection. The goal of this research is to develop an algorithm for signals selection in panoramic radio monitoring systems by their external parameters. The essence of proposed algorithm is to detect occupied bands of radio frequency spectrum, estimate center frequency and bandwidth of each channel, noise level and signal-tonoise ratio. Creation of frequency channels allows for signal filtering and estimation of pulse durations, as well as occupancy of each channel. Estimates of parameter for each signal fragment and frequency channel are recorded in associative arrays, which simplifies further signal selection. Due to variability of noise and propagation channel, estimates of signal parameters for each signal fragment are random variables. To obtain reliable estimates of signal center frequency and bandwidth, they are further grouping. Array of data can be accessed both by frequency channel number (table rows) and by signal parameters (keys), which are table column headers. Associative relationships between data provide flexible signals filtering by any combination of parameters. To test developed algorithm, we analyzed frequency band of 933-953 MHz and used the DataFrame Multi Index data container of Pandas package of Python programming language. This structure provides multi-level indexing, flexible access to data, and a wide range of tools for their processing and modifying. Developed algorithm can be used in existing and future radio monitoring systems for radio electronic devices identification and databases creation.