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.

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.

Analysis of echo signal modulation characteristic parameters on aerial and space targets

Based on the scattering characteristic,the comparison of RCS(radar cross-section)at different positions of a target in the same direction of incidence can be obtained first by extruding or deleting part of the entity.A simulation method of aerial&space targets echo characteristics(A&STEC)is proposed that is universal to aerial and space targets.We utilize a fixed-wing UAV(unmanned aerial vehicle)and typical missiles in simulation.The echo signal modulation characteristic parameters are calculated theoretically by the atmospheric attenuation model,the finite element method and a MUMPS solver.The verification simulations show that this method can analyze the influence of the target shape,incident direction,detection position and detection frequency on echo waveform,intensity and energy distribution.The results show that the profile of echo waveform can invert the general shape of the target.The relationship between time and intensity can determine whether the target is moving towards or away from the detector in addition.These conclusions can provide a reference for the ballistic missile target tracking and the defense against UVA intrusion in theory.

Intelligent Modulation Recognition of Communication Signal for Next-Generation 6G Networks

In recent years,the need for a fast,efficient and a reliable wireless network has increased dramatically.Numerous 5G networks have already been tested while a few are in the early stages of deployment.In noncooperative communication scenarios,the recognition of digital signal modulations assists people in identifying the communication targets and ensures an effective management over them.The recent advancements in both Machine Learning(ML)and Deep Learning(DL)models demand the development of effective modulation recognition models with self-learning capability.In this background,the current research article designs aDeep Learning enabled Intelligent Modulation Recognition of Communication Signal(DLIMR-CS)technique for next-generation networks.The aim of the proposed DLIMR-CS technique is to classify different kinds of digitally-modulated signals.In addition,the fractal feature extraction process is appliedwith the help of the Sevcik Fractal Dimension(SFD)approach.Then,the extracted features are fed into the Deep Variational Autoencoder(DVAE)model for the classification of the modulated signals.In order to improve the classification performance of the DVAE model,the Tunicate Swarm Algorithm(TSA)is used to finetune the hyperparameters involved in DVAE model.A wide range of simulations was conducted to establish the enhanced performance of the proposed DLIMR-CS model.The experimental outcomes confirmed the superior recognition rate of the DLIMR-CS model over recent state-of-the-art methods under different evaluation parameters.

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.

Research on Feature Extraction Method for Low-Speed Reciprocating Bearings Based on Segmented Short Signal Modulation Signal Bispectrum Slicing

Bearing condition monitoring and fault diagnosis (CMFD) can investigate bearing faults in the early stages, preventing the subsequent impacts of machine bearing failures effectively. CMFD for low-speed, non-continuous operation bearings, such as yaw bearings and pitch bearings in wind turbines, and rotating support bearings in space launch towers, presents more challenges compared to continuous rolling bearings. Firstly, these bearings have very slow speeds, resulting in weak collected fault signals that are heavily masked by severe noise interference. Secondly, their limited rotational angles during operation lead to a restricted number of fault signals. Lastly, the interference from deceleration and direction-changing impact signals significantly affects fault impact signals. To address these challenges, this paper proposes a method for extracting fault features in low-speed reciprocating bearings based on short signal segmentation and modulation signal bispectrum (MSB) slicing. This method initially separates short signals corresponding to individual cycles from the vibration signals based on encoder signals. Subsequently, MSB analysis is performed on each short signal to generate MSB carrier-slice spectra. The optimal carrier frequency and its corresponding modulation signal slice spectrum are determined based on the carrier-slice spectra. Finally, the MSB modulation signal slice spectra of the short signal set are averaged to obtain the overall average feature of the sliced spectra.

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.

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.

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.

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.

DESIGN OF INTERPOLATION FILTER FOR ALL DIGITAL RECEIVER-ON THE INTERPOLATION PROBLEM OF FULL RESPONSE MODULATION SIGNALS

All digital implementation of receiver is a main topic on digital communication recently. The design of interpolation filter is one of the important problems for all digital implementation of receiver. In this paper, for full response linear modulation signal, a interpolation criterion is proposed. An interpolation formula is presented on bandwidth-limited transmission signal. For example, using the raised cosine roll off function as the system pulse response, the feasibility and effectiveness on the interpolation formula are certified by theoretical and numerical analysis. The computer simulation result on 16-QAM signal is given.

Research of Hybrid Modulation Radar Signal

Based on the advantage of phase coded signal and stepped frequency signal,a new hybrid modulation signal is introduced in this paper. It combines phase code modulation during the pulse with stepped frequency modulation between the pulses, which is named as phase-coded stepped-frequency ( PCSF ) signal. By analyzing its waveform and ambiguity function,the comparison between Stepped-Frequency ( SF) signal and PCSF signal is given,which shows that the PCSF signal is better than SF signal. Finally,the signal processing method with two stage compressed processing is presented. The simulation results show that this new hybrid modulation radar signal can get a higher stepped frequency than ordinary SF signal,realize the same equivalent bandwidth with less pulse number,and solve the conflict among the stepped frequency,the number of pulse, and transmit average power. Under the premises of a certain range resolution,this new hybrid modulation radar signal not only raises the data rate of radar system,but also reduces Doppler sensitivity with a good prospect, and the effect of one-dimensional range profile is much better than that of traditional SF signal. Therefore,this new hybrid modulation radar signal can be widely used in application.

Effects of signal modulation and coloured cross-correlation of coloured noises on the diffusion of a harmonic oscillator

The diffusion in a harmonic oscillator driven by coloured noises ξ（t） and η（t） with coloured cross-correlation in which one of the noises is modulated by a biased periodic signal is investigated. The exact expression of diffusion coefficient d as a function of noise parameter, signal parameter, and oscillator frequency is derived. The findings in this paper are as follows. 1） The curves of d versus noise intensity D and d versus noises cross-correlation time z3 exist as two different phases. The transition between the two phases arises from the change of the cross-correlation coefficient A of the two Ornstein-Uhlenbeck （O-U） noises. 2） Changing the value of T3, the curves of d versus Q, the intensity of colored noise that is modulated by the signal, can transform from a phase having a minimum to a monotonic phase. 3） Changing the value of signal amplitude A, d versus Q curves can transform from a phase having a minimum to a monotonic phase. The above-mentioned results demonstrate that a like noise-induced transition appears in the model.

Improved partial response maximum likelihood method combining modulation code for signal waveform modulation multi-level disc

In this paper, we describe an improved adaptive partial response maximum likelihood （PRML） method combining modulation code tbr signal waveform modulation multi-level disc. This improved adaptive PRML method employs partial response equalizer and adaptive viterbi detector combining modulation code. Compared with the traditional adaptive PRML detector, the improved PRML detector additionally employs illogical sequence detector and corrector. Illogical sequence detector and corrector can aw）id the appearance of illogical sequences effectively, which do not follow the law of modulation code for signal waveform modulation multi-level disc, and obtain the correct sequences. We implement the improved PRML detector using a DSP and an FPGA chip. The experimental results show good performance. The higher efficient and lower complexity can be obtained by using the improved PRML method than by using the previous PRML method. Meanwhile, resource utilization of the improved PRML detector is not changed, but the bit error rate （BER） is reduced by more than 20%.