A new method for improving the inductively coupled data transmission rate of mooring buoy based on carrier signal frequency selection

In the inductively coupled data transmission system of the mooring buoy, the carrier signal frequency of the transmission channel is limited due to the inherent characteristics of the system, resulting in limited channel bandwidth. The limited channel bandwidth limits the increase in inductively coupled data transmission rate.In order to improve the inductively coupled data transmission rate of mooring buoy as much as possible without damaging the data transmission performance, a new method was proposed in this paper. The method is proposed to improve the data transmission rate by selecting the appropriate carrier signal frequencies based on the principle of maximizing the amplitude value of amplitude-frequency characteristic curve of the system. Research has been done according to this method as follows. Firstly, according to the inductively coupled transmission mooring buoy structure, the inductively coupled data transmission circuit model was established. The binary frequency shift keying(2FSK) digital signal modulation mode was selected. Through theoretical analysis, the relation between the carrier signal frequency and the data transmission performance, the relation between the carrier signal frequency and the 2FSK signal bandwidth were obtained. Secondly, the performance and the bandwidth of the signal transmission were studied for the inherent characteristics of the actual inductively coupled data transmission system. The amplitude-frequency characteristic of the system was analyzed by experiments. By selecting the appropriate carrier signal frequency parameters, an excellent data transmission performance was guaranteed and a large 2FSK signal bandwidth was obtained. Finally, an inductively coupled data transmission rate optimization experiment and a bit error rate analysis experiment were designed and carried out. The results show that the high-speed and reliable data transmission of the system was realized and the rate can reach 100 kbps.

Detection and Classification on Amateur Drones Based on Cepstrum of Radio Frequency Signal

As a prospective component of the future air transportation system,unmanned aerial vehicles(UAVs)have attracted enormous interest in both academia and industry.However,small UAVs are barely supervised in the current situation.Crash accidents or illegal airspace invading caused by these small drones affect public security negatively.To solve this security problem,we use the back-propagation neural network(BPNN),the support-vector machine(SVM),and the k-nearest neighbors(KNN)method to detect and classify the non-cooperative drones at the edge of the flight restriction zone based on the cepstrum of the radio frequency(RF)signal of the drone’s downlink.The signal from five various amateur drones and ambient wireless devices are sampled in an electromagnetic clean environment.The detection and classification algorithm based on the cepstrum properties is conducted.Results of the outdoor experiments suggest the proposed workflow and methods are sufficient to detect non-cooperative drones with an average accuracy of around 90%.The mainstream downlink protocols of amateur drones can be classified effectively as well.

A new method of demodulating the digital frequency modulation signals

A new method of estimating the frequency-known signals from the strong background noise was presented first. Then the new method was used in the demodulation of the digital frequency modulation (FSK) signals. The new demodulation method can complete the demodulation of the FSK signals only with the carrier frequency and without any carrier phase information. The simulation results show that the performance of anti-noise of the new method is better than that of the incoherent demodulation method and the fluctuation of the carrier phase has little effect on the new method. So the new demodulation method has a fine prospect in the practical applications.

Stochastic resonance in a single-mode laser driven by frequency modulated signal and coloured noises

By adding frequency modulated signals to the intensity equation of gain noise model of the single-mode laser driven by two coloured noises which are correlated, this paper uses the linear approximation method to calculate the power spectrum and signal-to-noise ratio （SNR） of the laser intensity. The results show that the SNR appears typical stochastic resonance with the variation of intensity of the pump noise and quantum noise. As the amplitude of a modulated signal has effects on the SNR, it shows suppression, monotone increasing, stochastic resonance, and multiple stochastic resonance with the variation of the frequency of a carrier signal and modulated signal.

Blind parameter estimation of pseudo-random binary code-linear frequency modulation signal based on Duffing oscillator at low SNR

Conventional parameter estimation methods for pseudo-random binary code-linear frequency modulation(PRBC-LFM)signals require prior knowledge,are computationally complex,and exhibit poor performance at low signal-to-noise ratios(SNRs).To overcome these problems,a blind parameter estimation method based on a Duffing oscillator array is proposed.A new relationship formula among the state of the Duffing oscillator,the pseudo-random sequence of the PRBC-LFM signal,and the frequency difference between the PRBC-LFM signal and the periodic driving force signal of the Duffing oscillator is derived,providing the theoretical basis for blind parameter estimation.Methods based on amplitude method,short-time Fourier transform method,and power spectrum entropy method are used to binarize the output of the Duffing oscillator array,and their performance is compared.The pseudo-random sequence is estimated using Duffing oscillator array synchronization,and the carrier frequency parameters are obtained by the relational expressions and characteristics of the difference frequency.Simulation results show that this blind estimation method overcomes limitations in prior knowledge and maintains good parameter estimation performance up to an SNR of-35 dB.

Sensibility to Changes of Vibrational Modes of Excited Electron： Sum Frequency Signals Versus Difference Frequency Signals

In this paper, we investigate a two electronic level system with vibrational modes coupled to a Brownian oscillator bath. The difference frequency generation （DFG） signals and sum frequency generation （SFG） signals are calculated. It is shown that, for the same model, the SFG signals are more sensitive than the DFG signals to the changes of the vibrational modes of the electronic two-level system. Because the SFG conversion efficiency can be improved by using the time-delay method, the findings in this paper predict that the SFG spectrum may probe the changes of the microstructure more effectively.

Breast Lump Recognition Algorithms Based on Ultrasound Radio-Frequency Signals

A method for evaluating the benign and malignant breast tumors based on radio?frequency(RF)data was explored by extracting the characteristic parameters of breast ultrasound RF signals.The breast biopsy data were used as the reference data for judging the lump benign or malignant.The extracted ultrasound RF data were reconstructed and segmented by computer aided method to obtain the breast tumor region of interest(ROI)and its characteristic parameters(entropy and standard deviation).The characteristic parameters were statistically analyzed to evaluate the relationship between characteristic parameters and benign or malignant breast tumors.The results indicate the entropy and standard deviation of normal region is much higher than that of lump region,which shows that the standard deviation and entropy characteristic parameters of ultrasonic RF signals are meaningful in the diagnosis of breast tumors.The proposed method provides a new direction for computer?aided diagnosis of benign and malignant breast tumors.

Estimation of the complex frequency of a harmonic signal based on a linear least squares method

In this study, we propose a simple linear least squares estimation method(LLS) based on a Fourier transform to estimate the complex frequency of a harmonic signal. We first use a synthetically-generated noisy time series to validate the accuracy and effectiveness of LLS by comparing it with the commonly used linear autoregressive method(AR). For an input frequency of 0.5 m Hz, the calculated deviations from the theoretical value were 0.004‰and 0.008‰ for the LLS and AR methods respectively; and for an input 5 10 6attenuation,the calculated deviations for the LLS and AR methods were 2.4% and 1.6%. Though the theory of the AR method is more complex than that of LLS, the results show LLS is a useful alternative method. Finally, we use LLS to estimate the complex frequencies of the five singlets of the0S2 mode of the Earth’s free oscillation. Not only are the results consistent with previous studies, the method has high estimation precisions, which may prove helpful in determining constraints on the Earth’s interior structures.

Parameter estimation method for a linear frequency modulation signal with a Duffing oscillator based on frequency periodicity

In view of the complexity of existing linear frequency modulation(LFM)signal parameter estimation methods and the poor antinoise performance and estimation accuracy under a low signal-to-noise ratio(SNR),a parameter estimation method for LFM signals with a Duffing oscillator based on frequency periodicity is proposed in this paper.This method utilizes the characteristic that the output signal of the Duffing oscillator excited by the LFM signal changes periodically with frequency,and the modulation period of the LFM signal is estimated by autocorrelation processing of the output signal of the Duffing oscillator.On this basis,the corresponding relationship between the reference frequency of the frequencyaligned Duffing oscillator and the frequency range of the LFM signal is analyzed by the periodic power spectrum method,and the frequency information of the LFM signal is determined.Simulation results show that this method can achieve high-accuracy parameter estimation for LFM signals at an SNR of-25 dB.

Study on System of Low Frequency Signal′s Measurement and Extraction of its Characteristic Quantity

The essay mainly studies a system of low frequency signal′s measurement and the extraction of its characteristic quantity,introduces how matlab analyzes and extracts low frequency signal.MATLAB,a visual programming software,designs testing program for mechanic vibration signal,analyzes and identifies vibration signal.The collected data shows the effectiveness of the system.Based on XM121 modules,the testing system collects and analyzes single channel signal.

Interrupt CW Approach for IF Video Signal Real Time Simulating and Its Validity

To determine the feasibility and practicability of interrupt continuous wave (CW) approach proposed for real time simulating radar intermediate frequency(IF) video signal, theoretical analysis and computer simulation were used. Phases at two linked points between the end and beginning of adjoined frames are always consistent; the bias Doppler frequency for the time delay of A/D sampling start responds to that for target acceleration. No digital phase compensation is required at continuous points, and the interrupt CW approach has apparently practical values.

Performances of improved Tent chaos-based FM radar signal

A novel algorithm is proposed to solve the poor per- formance problem of the Tent chaos-based frequency modulation （FM） signal for range-Doppler imaging, which takes it into complex multi-segment system by increasing its segments. The simulation results show that the effectiveness of the proposed algorithm, as well as the performance of the improved Tent FM signal is obvious in a multipath or noise propagation environment.

Combining Radon-ambiguity transform with second-order difference to improve detection probability of LFM signals in low SNR

The Radon-ambiguity transform （RAT）, although efficient for detecting the linear frequency modulated signals （LFMs）, is troubled by the energy accumulation of noise in low signal-to-noise ratio （SNR）. A secondorder difference （SOD） method is proposed to treat with this problem. In the SOD method, the optimal search step and difference step are derived from the LFM rate resolution formula. The sharpness of the peaks of RAT is measured by curvature, and the sharpness, but not the magnitude of the peaks, is used to detect the LFMs. The SOD method removes the noise energy accumulation and reserves the drastically changing components integrally; thus, it improves the detection probability of LFMs in low SNR. The expected performance of the new method is verified by 100 Monte Carlo simulations.

Wigner-Hough transform based on slice's entropy and its application to multi-LFM signal detection

To enhance the capacity of the radar-reconnaissance interception receiver recognizing linear frequency modulated (LFM) at a low signal-noise ratio, this paper presents WignerHough transform (WHT) of the LFM signal and its corresponding characteristics, derives the probability density functions of the LFM signal and Gaussian white noise within WHT based on entropy (WHTE), dimension under different assumptions and puts forward a WHT algorithm based on entropy of slice to improve the capacity of detecting the LFM signal. Entropy of the WHT domain slice is adopted to assess the information size of polar radius or angle slice, which is converted into the weight factor to weight every slice. Double-deck weight is used to weaken the influences of noise and disturbance terms and WHTE treatment and signal detection procedure are also summarized. The rationality of the algorithm is demonstrated through theoretical analysis and formula derivation, the efficiency of the algorithm is verified by simulation comparison between WHT, fractional Fourier transform and periodic WHT, and it is highlighted that the WHTE algorithm has better detection accuracy and range of application against strong noise background.

Grating lobes suppression method for stepped frequency GB-SAR system

A grating lobes suppression method for chirp-subpulse stepped frequency（CSSF） signals is proposed, which is applied to deformation monitoring using the ground based synthetic aperture radar（GB-SAR） system. This method is based on accurate estimation and correction of the phase and amplitude error along two dimensions（range and azimuth）, i.e., the error estimation inside the subpulse（in-subpulse error） and across the stepped frequency subpulses（cross-subpulse error） of transmitted CSSF signals. Validated both with simulated data and experimental data recorded in the deformation monitoring campaign, it can be seen that the method as well as the relative conclusions can be fully and effectively applied to most of the stepped frequency systems.

Chirp-Rate Resolution of Fractional Fourier Transform in Multi-component LFM Signal

Distinguishing close chirp-rates of different linear frequency modulation （LFM） signals under concentrated and complicated signal environment was studied. Firstly, detection and parameter estimation of multi-component LFM signal were used by discrete fast fractional Fourier transform （FrFT）. Then the expression of chirp-rate resolution in fractional Fourier domain （FrFD） was deduced from discrete normalize time-frequency distribution, when multi-component LFM signal had only one center frequency. Furthermore, the detail influence of the sampling time, sampling frequency and chirp-rate upon the resolution was analyzed by partial differential equation. Simulation results and analysis indicate that increasing the sampling time can enhance the resolution, but the influence of the sampling frequency can he omitted. What＇s more, in multi-component LFM signal, the chirp-rate resolution of FrFT is no less than a minimal value, and it mainly dependent on the biggest value of chirp-rates, with which it has an approximately positive exponential relationship.

Statistical performance of IF estimation of LFM signals with timevarying amplitude using the peak of WVD

The instantaneous frequency （IF） estimation of the linear frequency modulated （LFM） signals with time-varying amplitude using the peak of the Wigner-Ville distribution （WVD） is studied. Theoretical analysis shows that the estimation on LFM signals with time-varying amplitude is unbiased, only if WVD of time-varying amplitude reaches its maximum at frequency zero no matter in which time. The statistical performance in the case of additive white Guassian noise is evaluated and an analytical expression for the variance is provided. The simulations using LFM signals with Gaussian envelope testify that IF can be estimated accurately using the peak of WVD for four models of amplitude variation. Furthermore the statistical result of estimation on the signals with amplitude descending before rising is better than that of the signals with constant amplitude when the amplitude variation rate is moderate.

ALGORITHM FOR THE DETECTION AND PARAMETER ESTIMATION OF MULTICOMPONENT LFM SIGNALS

A novel algorithm based on Radon-Ambiguity Transform (RAT) and Adaptive Signal Decomposition (ASD) is presented for the detection and parameter estimation of multicompo-nent Linear Frequency Modulated (LFM) signals. The key problem lies in the chirplet estimation. Genetic algorithm is employed to search for the optimization parameter of chirplet. High estimation accuracy can be obtained even at low Signal-to-Noisc Ratio(SNR). Finally simulation results are provided to demonstrate the performance of the proposed algorithm.

Parameter estimation of LFM signals based on time reversal

In this paper,parameter estimation of linear frequency modulation(LFM)signals containing additive white Gaussian noise is studied.Because the center frequency estimation of an LFM signal is affected by the error propagation effect,resulting in a higher signal to noise ratio(SNR)threshold,a parameter estimation method for LFM signals based on time reversal is proposed.The proposed method avoids SNR loss in the process of estimating the frequency,thus reducing the SNR threshold.The simulation results show that the threshold is reduced by 5 dB compared with the discrete polynomial transform(DPT)method,and the root-mean-square error(RMSE)of the proposed estimator is close to the Cramer-Rao lower bound(CRLB).

Analysis and Process of Music Signals to Generate Two-Dimensional Tabular Data and a New Music

The processing of sound signals is significantly improved recently.Technique for sound signal processing focusing on music beyond speech area is getting attention due to the development of deep learning techniques.This study is for analysis and process of music signals to generate tow-dimensional tabular data and a new music.For analysis and process part,we represented normalized waveforms for each of input data via frequency domain signals.Then we looked into shorted segment to see the difference wave pattern for different singers.Fourier transform is applied to get spectrogram of the music signals.Filterbank is applied to represent the spectrogram based on the human ear instead of the distance on the frequency dimension,and the final spectrogram has been plotted by Mel scale.For generating part,we created two-dimensional tabular data for data manipulation.With the 2D data,any kind of analysis can be done since it has digit values for the music signals.Then,we generated a new music by applying LSTM toward the song audience preferred more.As the result,it has been proved that the created music showed the similar waveforms with the original music.This study made a step forward for music signal processing.If this study expands further,it can find the pattern that listeners like so music can be generated within favorite singer’s voice in the way that the listener prefers.