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.

Treatable focal region modulated by double excitation signal superimposition to realize platform temperature distribution during transcranial brain tumor therapy with high-intensity focused ultrasound

Recently, the phase compensation technique has allowed the ultrasound to propagate through the skull and focus into the brain. However, the temperature evolution during treatment is hard to control to achieve effective treatment and avoid over-high temperature. Proposed in this paper is a method to modulate the temperature distribution in the focal region. It superimposes two signals which focus on two preset different targets with a certain distance. Then the temperature distribution is modulated by changing triggering time delay and amplitudes of the two signals. The simulation model is established based on an 82-element transducer and computed tomography （CT） data of a volunteer＇s head. A finite- difference time-domain （FDTD） method is used to calculate the temperature distributions. The results show that when the distances between the two targets respectively are 7.5-12.5 mm on the acoustic axis and 2.0-3.0 mm in the direction perpendicular to the acoustic axis, a focal region with a uniform temperature distribution （64-65 ℃） can be created. Moreover, the volume of the focal region formed by one irradiation can be adjusted （26.8-266.7 mm3） along with the uniform temperature distribution. This method may ensure the safety and efficacy of HIFU brain tumor therapy.

Automatic modulation recognition of radiation source signals based on two-dimensional data matrix and improved residual neural network

Automatic modulation recognition(AMR)of radiation source signals is a research focus in the field of cognitive radio.However,the AMR of radiation source signals at low SNRs still faces a great challenge.Therefore,the AMR method of radiation source signals based on two-dimensional data matrix and improved residual neural network is proposed in this paper.First,the time series of the radiation source signals are reconstructed into two-dimensional data matrix,which greatly simplifies the signal preprocessing process.Second,the depthwise convolution and large-size convolutional kernels based residual neural network(DLRNet)is proposed to improve the feature extraction capability of the AMR model.Finally,the model performs feature extraction and classification on the two-dimensional data matrix to obtain the recognition vector that represents the signal modulation type.Theoretical analysis and simulation results show that the AMR method based on two-dimensional data matrix and improved residual network can significantly improve the accuracy of the AMR method.The recognition accuracy of the proposed method maintains a high level greater than 90% even at -14 dB SNR.

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.

WAVELET ANALYSIS OF MODULATED SIGNALS

The relationship between Haar wavelet decomposition coefficients and modulated signal parame-ters is discussed. A new modulation classification method is presented. The new method uses the amplitude, frequency and phase information derived from Haar wavelet decomposition as feature vectors to distinguish the modulation types of M-ary Frequency-Shift Keying (MFSK), M-ary Phase-Shift Keying (MPSK) and Quadrature Amplitude Modulation (QAM) modulation types. A parallel combined classifier is designed based on these feature vectors. The overall successful recognition rate of 92.4% can be achieved even at a low Sig-nal-to-Noise Ratio (SNR) of 5dB.

Research on Modulation Signal Denoising Method Based on Improved Variational Mode Decomposition

In order to further analyze the micro-motion modulation signals generated by rotating components and extract micro-motion features,a modulation signal denoising algorithm based on improved variational mode decomposition(VMD)is proposed.To improve the time-frequency performance,this method decomposes the data into narrowband signals and analyzes the internal energy and frequency variations within the signal.Genetic algorithms are used to adaptively optimize the mode number and bandwidth control parameters in the process of VMD.This approach aims to obtain the optimal parameter combination and perform mode decomposition on the micro-motion modulation signal.The optimal mode number and quadratic penalty factor for VMD are determined.Based on the optimal values of the mode number and quadratic penalty factor,the original signal is decomposed using VMD,resulting in optimal mode number intrinsic mode function(IMF)components.The effective modes are then reconstructed with the denoised modes,achieving signal denoising.Through experimental data verification,the proposed algorithm demonstrates effective denoising of modulation signals.In simulation data validation,the algorithm achieves the highest signal-to-noise ratio(SNR)and exhibits the best performance.

Instantaneous frequency estimation of multi-component Chirp signals in noisy environments

A classical time-varying signal, the multi-component Chirp signal has been widely used and the ability to estimate its instantaneous frequency （IF） is very useful. But in noisy environments, it is hard to estimate the 1F of a multi-component Chirp signal accurately. Wigner distribution maxima （WDM） are usually utilized for this estimation. But in practice, estimation bias increases when some points deviate from the true IF in high noise environments. This paper presents a new method of multi-component Chirp signal 1F estimation named Wigner Viterbi fit （WVF）, based on Wigner-Ville distribution （WVD） and the Viterbi algorithm. First, we transform the WVD of the Chirp signal into digital image, and apply the Viterbi algorithm to separate the components and estimate their IF. At last, we establish a linear model to fit the estimation results. Theoretical analysis and simulation results prove that this new method has high precision and better performance than WDM in high noise environments, and better suppression of interference and the edge effect. Compared with WDM, WVF can reduce the mean square error （MSE） by 50% when the signal to noise ration （SNR） is in the range of-15dB to -11dB. WVF is an effective and promising 1F estimation method.

Optimization of Output Properties for Bias Signal-Modulated in a Single-Mode Laser

Defining the quantity K as the signal-to-noise ratio （SNR） and the normalized intensity fluctuation C（O） of a single-mode laser for bias signal modulation driven by color noises with colored correlation, the whole output properties of the laser system is described by K. It is found that there is a maximum in the curves of K versus D, Q, and io. The optimization parameters are gained.

Modulation Index Estimation of Frequency and Phase Modulated Signals

Modulation index estimation is important in the demodulation and recognition of Angle Modulation (AM) signals which include Frequency Modulation (FM) and Phase Modulation (PM) signals. In this paper, we firstly analyzed the AM signals with baseband modulation types, such as monotone, PSK, FSK, in the time and frequency domain. Then, we established a unified mathematical representation for the AM signals. Finally, we derived a blind estimation algorithm for the modulation index without using any prior knowledge. Simulation results verify the capabilities of the proposed algorithm.

Modulation recognition of communication signals based on SCHKS-SSVM

A novel modulation recognition algorithm is proposed by introducing a Chen-Harker-Kanzow-Smale (CHKS) smooth function into the C-support vector machine deformation algorithm. A set of seven characteristic parameters is selected from a range of parameters of communication signals including instantaneous amplitude, phase, and frequency. And the Newton-Armijo algorithm is utilized to train the proposed algorithm, namely, smooth CHKS smooth support vector machine (SCHKS-SSVM). Compared with the existing algorithms, the proposed algorithm not only solves the non-differentiable problem of the second order objective function, but also reduces the recognition error. It significantly improves the training speed and also saves a large amount of storage space through large-scale sorting problems. The simulation results show that the recognition rate of the algorithm can batch training. Therefore, the proposed algorithm is suitable for solving the problem of high dimension and its recognition can exceed 95% when the signal-to-noise ratio is no less than 10 dB.

Stochastic Resonance in a Single-Mode Laser Driven by Quadratic Colored Pump Noise:Effects of Biased Amplitude Modulation Signal

A single-mode laser noise model driven by quadratic colored pump noise and biased amplitude modulationsignal is proposed.The analytic expression of signal-to-noise ratio is calculated by using a new linearized procedure.Itis found that there are three different typies of stochastic resonance in the model:the conventional form of stochasticresonance,the stochastic resonance in the broad sense,and the bona fide SR.

Modulation recognition of MIMO radar signal based on joint HOS and SNR algorithm

This paper presents a joint high order statistics （HOS） and signal-to-noise ratio （SNR） algorithm for the recognition of multiple-input multiple-output （MIMO） radar signal without a priori knowledge of the signal parameters. This method is capable of recognizing the MIMO radar signal as well as discriminating it from single-carrier signal adopted by conventional radar. Meanwhile, the sub-carrier number of the none-coding MIMO radar signal is estimated. Extensive simulations are carried out in different operating conditions. Simulation results prove the feasibility and indicate that the recognition probability could reach over 90% when the value of SNR is above 0 dB.

A new tracking error detection method using amplitude difference detection for signal waveform modulation multi-level discs

The sub-land/sub-pit affects the characteristic of the tracking error signal which is generated by the conventional differential phase detection （DPD） method in the signal waveform modulation multi-level （SWML） read-only disc. To solve this problem, this paper proposes a new tracking error detection method using amplitude difference. Based on the diffraction theory, the amplitude difference is proportional to the tracking error and is feasible to be used for obtaining the off-track information. The experimental system of the amplitude difference detection method is developed. The experimental results show that the tracking error signal derived from the new method has better performance in uniformity and signal-to-noise ratio than that derived from the conventional DPD method in the SWML read-only disc.

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 Linear Region of a Single-Mode Laser: Effects of Amplitude Modulation of Signal

A single-mode laser noise model driven by quadratic colored pump noise and amplitude modulation signal is proposed. The real and imaginary parts of the pump noise are assumed to be cross-correlation. The effect of cross- correlation of noise and amplitude modulation of signal on laser statistical properties is studied by using the linearized approximation. The analytic expression of signal-to-noise ratio （SNR） is calculated. It is found that the phenomena of stochastic resonance （SR） respectively exist in the curves of the SNR versus the noise cross-correlation coefficient λ and the SNR versus the pump parameter a, as well as the SNR versus the signal frequency ω in our model. It is shown that there are three different typies of SR in the model： the conventional form of SR, the SR in the broad sense, and the bona fide SR.

A Multiobjective Optimization Method for Designing <i>M</i>-Channel NPR Cosine Modulated Filter Bank for Image Compression

This paper proposes a method to design multichannel cosine modulated filter bank for image compression using multiobjective optimization technique. The design problem is a combination of stopband residual energy, least square error of the overall transfer function of the filter bank, coding gain with dc leakage free condition as constraint. The proposed algorithm uses Non-dominated Sorting Genetic Algorithm (NSGA) to minimize the mutually contradictory objective function by minimizing filter tap weights of prototype filter. The algorithm solves this problem by searching solutions that achieve the best compromise between the different objectives criteria. The performance of this algorithm is evaluated in terms of coding gain and peak signal to noise ratio (PSNR). Simulation results on different images are included to illustrate the effectiveness of the proposed algorithm for image compression application.

Effect on Intensity Correlation Time by Input Signal in a Single-Mode Laser with Bias Signal Modulation

The effect on intensity correlation time T by input signal is studied for gain-noise model of a single-mode laser driven by colored pump noise and colored quantum noise with colored cross-correlation with a bias signal modulation in this paper. By using the linear approximation method, we detect that there exists maximum （i.e., resonance） in the curve of the intensity correlation time T upon bias current io when the noise correlation coefficient λ is positive; and there exists minimum （i.e., suppression） in the T-io curve when λ is negative. And whenλ is zero, T increases monotonously with increasing io. Furthermore, the curve of T upon the signal frequency Ω is also studied. Our study shows that no matter what the value of λ is, there exists minimum （i.e., suppression） in the T-Ω curve.

Dual-Use Signal Design for Radar and Communication via Joint Orthogonal Signal and Phase Modulation

This paper proposes a new information modulation resorting to orthogonal signal and its phase for dual-function radar communication(DFRC)systems.Focusing on the standardized linear frequency modulation(LFM)signal by additional phase,a bank of signals enjoying satisfactory autocorrelation and cross-correlation characteristics,are generated.Then,these signals map the different information as well as their phases are also modulated to increase the communication bit rate,thus yielding a series of dual-use signals.Finally,the radar detection and communication performance of dual-use signals are also provided through numerical simulation and half-physical platform verification,confirming the effectiveness of the designed signals compared with the existing design strategy.

A novel M-ary modulation technique based on optimal chirp signal design

A novel M-ary chirp modulation technique based on an optimal chirp signal design is proposed in this paper to offer higher data rate for an indoor wireless chirp spread spectrum communication system. Both linear chirp signals and combined chirp signals are used in this system to reduce the effect of the cross correlation, and simplify the complexity of the system. The optimal scheme of de- signing both linear chirp signals and combined chirp signals is discussed to minimize the value of the cross correlation and obtain a better system performance. Simulation results show that, compared with the binary orthogonal chirp modulation technique, the M-ary chirp modulation technique based on an optimal chirp signal set has a higher data rate with a reasonable bit-error rate （BER） perform- ance under both additive white gaussian noise （AWGN） channel and indoor wireless channel.

IMMUNE RBF NETWORK AND ITS APPLICATION IN THE MODULATION-STYLE RECOGNITION OF RADAR SIGNALS

Based on Immune Programming(IP), a novel Radial Basis Function (RBF) networkdesigning method is proposed. Through extracting the preliminary knowledge about the widthof the basis function as the vaccine to form the immune operator, the algorithm reduces thesearching space of canonical algorithm and improves the convergence speed. The application ofthe RBF network trained with the algorithm in the modulation-style recognition of radar signalsdemonstrates that the network has a fast convergence speed with good performances.