Application of harmonic wavelets to processing oscillating hydroacoustic signals

The paper is devoted to the application of specific functions called harmonic wavelets, which are aimed at processing a wide range of oscillating hydroacoustic signals including multiharmonic and transient signals. We provide basics of the harmonic wavelet transform and a two-stage algorithm for computing wavelet coefficients based on the discrete Fourier transform. We introduce a special efficiency factor of applying these wavelets to oscillating hydroacoustic signals. Application of harmonic wavelets is efficient for processing oscillating hydroacoustic signals since harmonic wavelets have similarities with these types of signals. In many cases the best basis is the basis that has high correlation with the investigated signals since signal representation in such a basis will require a small number of components. We devote special attention to a very important practical task - denoising of oscillating signals using special statistical criteria and wavelet-based thresholding

Разработка и исследование методов демодуляции частотно-манипулированных сигналов

The goal of our research is development and study of different demodulation techniques for a frequency shift keyed signals. The signal under study is a frequency-shift keyed (FSK) signal when the information signal regulates the carrier frequency. We consider a model imitating data transmission channels and allowing us to perform error counting. The data transmission channels have ideal synchronization. Several techniques of demodulator design are introduced for demodulating frequency-shift keyed (FSK) signal and we compare the techniques using the criterion of error number of a demodulated signal relative to a signal prior to modulation for different signal-to-noise ratio. Signal-to-noise ratio is calculated as a ratio of the energy of the information symbol to the noise power spectral density. We have tested different demodulation techniques with different signal-to-noise ratio and produced a table containing information on demodulation accuracy for different techniques. Overall, we have simulated 100 sec. of continuous bit packages. We indicate that the best results for signal-to-noise ratio exceeding 5 dB are provided with the technique based on double correlation, and for signal-to-noise ratio less than 5 dB - with the technique based on the fast Fourier transform.Предложены различные методы демодуляции частотно-манипулированных сигналов и произведено их сравнение по критерию количества ошибок демодулированного сигнала относительно сигнала до модуляции при различном отношении "сигнал/шум"

Разработка и исследование демодуляторов сигналов c псевдослучайной перестройкой рабочей частоты

Demodulation task is encountered in many practical applications including digital signal processing and digital communications. Demodulation is connected with the communication system performance. Demodulation depends on a number of factors including signal-to-noise ratio (SNR) in the received message. In practice, it is necessary to minimize the number of errors for the given SNR and therefore new demodulation techniques are constantly developed with increased interference immunity. Demodulators aimed at for frequency-hopping spread spectrum signals have to meet special requirements since the message length can reach several ms and the number of messages can exceed several dozens.Frequency-hopping spread spectrum is a technique of information transmission via radio channel and it is distinguished by variable carrier frequency that can change many times. The carrier frequency changes according to a pseudo random number sequence, which is available to both a sender and a recipient. This technique improves interference immunity of a communication channel.Frequency-hopping spread spectrum is used in civil and special applications. This signal is stable to jamming (until the third side finds out the number sequence), which makes it possible to use it for special purposes (however, the signal still needs additional encryption).Demodulation includes signal detection, synchronization, message type determination (modulation speed and modulation type), decoding, determination of autostarting and autostop combinations (for message identification), composition of the received message. The paper considers the tasks beginning with message type determination.Message type determination can be carried out several ways: using the cross-correlation function, spectral analysis, etc. Since the structure of a synchrosequence is known, it is possible to obtain more precise results using the crosscorrelation function. Several synchrosequences are formed for each message and then we compute their cross-correlation with the received message. The analysis includes the comparison all the results of cross-correlation function computa-tion and finally we make a decision regarding the message.Determination of autostarting and autostop combinations is performed by comparing autostarting and autostop combinations from the database. Each autostarting combination determines the receiver operation mode (single-channel or frequency-hopping spread spectrum). Determination of combinations is performed during signal demodulation.Reception of a frequency-hopping spread spectrum signal is performed according to the frequency plan. According to this plan, the carrier frequency changes in fixed time points. After receiving the autostarting combination of frequencyhopping spread spectrum, a reception mode for frequency-hopping spread spectrum signal is switched on. After receiving the autostop combination this mode is terminated. The output of a demodulator is the message itself, modulation type, and carrier frequency.The outcome of demodulator performance can be represented with a table. The first column of this table contains the carrier frequency, the second column contains frequency deviation, the third column - modulation type, the fourth one - message speed, the fifth one and further - the message itself.In the paper, we provide new demodulation techniques of frequency-modulated messages for the given SNR. The developed techniques are based on spectral analysis and correlation analysis. We determine the computational complexity of the developed demodulation techniques. The total error is computed for each SNR and the selected demodulation technique using the developed MATLAB/Simulink model for a communication channel. Finally, we conclude about the best demodulation technique for the selected message type for the given SNR. Представлены разработка и исследования различных методов демодуляции частотно-манипулированных сообщений в режиме псевдослучайной перестройки рабочей частоты при заданном диапазоне изменения отношения "сигнал/шум" (ОСШ). Разработанные методы основаны на использовании спек­ трального и корреляционного анализа. На основе разработанной в MATLAB/Simulink модели канала связи вычислена ошибка исследуемых методов демодуляции при различных значениях ОСШ. В результате исследования определен наилучший метод демодуляции при заданном ОСШ.

Development and Study of Demodulation Techniques for Frequency Manipulated Signals

The goal of our research is development and study of different demodulation techniques for a frequency shift keyed signals. The signal under study is a frequency-shift keyed (FSK) signal when the information signal regulates the carrier frequency. We consider a model imitating data transmission channels and allowing us to perform error counting. The data transmission channels have ideal synchronization. Several techniques of demodulator design are introduced for demodulating frequency-shift keyed (FSK) signal and we compare the techniques using the criterion of error number of a demodulated signal relative to a signal prior to modulation for different signal-to-noise ratio. Signal-to-noise ratio is calculated as a ratio of the energy of the information symbol to the noise power spectral density. We have tested different demodulation techniques with different signal-to-noise ratio and produced a table containing information on demodulation accuracy for different techniques. Overall, we have simulated 100 sec. of continuous bit packages. We indicate that the best results for signal-to-noise ratio exceeding 5 dB are provided with the technique based on double correlation, and for signal-to-noise ratio less than 5 dB - with the technique based on the fast Fourier transform

Development and Study of Demodulators for Frequency Hopping Spread Spectrum Signals

Demodulation task is encountered in many practical applications including digital signal processing and digital communications. Demodulation is connected with the communication system performance. Demodulation depends on a number of factors including signal-to-noise ratio (SNR) in the received message. In practice, it is necessary to minimize the number of errors for the given SNR and therefore new demodulation techniques are constantly developed with increased interference immunity. Demodulators aimed at for frequency-hopping spread spectrum signals have to meet special requirements since the message length can reach several ms and the number of messages can exceed several dozens.Frequency-hopping spread spectrum is a technique of information transmission via radio channel and it is distinguished by variable carrier frequency that can change many times. The carrier frequency changes according to a pseudo random number sequence, which is available to both a sender and a recipient. This technique improves interference immunity of a communication channel.Frequency-hopping spread spectrum is used in civil and special applications. This signal is stable to jamming (until the third side finds out the number sequence), which makes it possible to use it for special purposes (however, the signal still needs additional encryption).Demodulation includes signal detection, synchronization, message type determination (modulation speed and modulation type), decoding, determination of autostarting and autostop combinations (for message identification), composition of the received message. The paper considers the tasks beginning with message type determination.Message type determination can be carried out several ways: using the cross-correlation function, spectral analysis, etc. Since the structure of a synchrosequence is known, it is possible to obtain more precise results using the crosscorrelation function. Several synchrosequences are formed for each message and then we compute their cross-correlation with the received message. The analysis includes the comparison all the results of cross-correlation function computa-tion and finally we make a decision regarding the message.Determination of autostarting and autostop combinations is performed by comparing autostarting and autostop combinations from the database. Each autostarting combination determines the receiver operation mode (single-channel or frequency-hopping spread spectrum). Determination of combinations is performed during signal demodulation.Reception of a frequency-hopping spread spectrum signal is performed according to the frequency plan. According to this plan, the carrier frequency changes in fixed time points. After receiving the autostarting combination of frequencyhopping spread spectrum, a reception mode for frequency-hopping spread spectrum signal is switched on. After receiving the autostop combination this mode is terminated. The output of a demodulator is the message itself, modulation type, and carrier frequency.The outcome of demodulator performance can be represented with a table. The first column of this table contains the carrier frequency, the second column contains frequency deviation, the third column - modulation type, the fourth one - message speed, the fifth one and further - the message itself.In the paper, we provide new demodulation techniques of frequency-modulated messages for the given SNR. The developed techniques are based on spectral analysis and correlation analysis. We determine the computational complexity of the developed demodulation techniques. The total error is computed for each SNR and the selected demodulation technique using the developed MATLAB/Simulink model for a communication channel. Finally, we conclude about the best demodulation technique for the selected message type for the given SNR