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.

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.

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.

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.

Pattern Recognition of Modulation Signal Classification Using Deep Neural Networks

In recent times,pattern recognition of communication modulation signals has gained significant attention in several application areas such as military,civilian field,etc.It becomes essential to design a safe and robust feature extraction(FE)approach to efficiently identify the various signal modulation types in a complex platform.Several works have derived new techniques to extract the feature parameters namely instant features,fractal features,and so on.In addition,machine learning(ML)and deep learning(DL)approaches can be commonly employed for modulation signal classification.In this view,this paper designs pattern recognition of communication signal modulation using fractal features with deep neural networks(CSM-FFDNN).The goal of the CSM-FFDNN model is to classify the different types of digitally modulated signals.The proposed CSM-FFDNN model involves two major processes namely FE and classification.The proposed model uses Sevcik Fractal Dimension(SFD)technique to extract the fractal features from the digital modulated signals.Besides,the extracted features are fed into the DNN model for modulation signal classification.To improve the classification performance of the DNN model,a barnacles mating optimizer(BMO)is used for the hyperparameter tuning of the DNN model in such a way that the DNN performance can be raised.A wide range of simulations takes place to highlight the enhanced performance of the CSM-FFDNN model.The experimental outcomes pointed out the superior recognition rate of the CSM-FFDNN model over the recent state of art methods interms of different evaluation parameters.

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.

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.

Optimal Bidirectional LSTM for Modulation Signal Classification in Communication Systems

Modulation signal classification in communication systems can be considered a pattern recognition problem.Earlier works have focused on several feature extraction approaches such as fractal feature,signal constellation reconstruction,etc.The recent advent of deep learning(DL)models makes it possible to proficiently classify the modulation signals.In this view,this study designs a chaotic oppositional satin bowerbird optimization(COSBO)with bidirectional long term memory(BiLSTM)model for modulation signal classification in communication systems.The proposed COSBO-BiLSTM technique aims to classify the different kinds of digitally modulated signals.In addition,the fractal feature extraction process takes place by the use of Sevcik Fractal Dimension(SFD)approach.Moreover,the modulation signal classification process takes place using BiLSTM with fully convolutional network(BiLSTM-FCN).Furthermore,the optimal hyperparameter adjustment of the BiLSTM-FCN technique takes place by the use of COSBO algorithm.In order to ensure the enhanced classification performance of the COSBO-BiLSTM model,a wide range of simulations were carried out.The experimental results highlighted that the COSBO-BiLSTM technique has accomplished improved performance over the existing techniques.

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.

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).

Carrier frequency and symbol rate estimation based on cyclic spectrum

Carrier frequency and symbol rate estimation are the main contents of parameter estimation,which is the basis of modulation recognition and further processing of signals especially in non-cooperative communication.With the development of wireless communication,the signal transmission environment has become increasingly bad,causing more difficulties in parameter estimation.It is well known that the signal cycle spectrum is robust to noises and signal parameters are closely related.In practice,it is impossible to calculate the cyclic spectrum of infinite length data signals.When using finite length data to obtain a cycle spectrum,the truncation noise is induced,resulting in interference.It is necessary to overcome the influence of noises in order to improve the detection ability of discrete spectral lines.An improved method of the discrete spectral line extraction algorithm is proposed by reflecting the amplitude advantage of discrete spectral lines through salient features of continuous noises in discrete spectral line neighborhood.

Acoustics Based Monitoring and Diagnostics for the Progressive Deterioration of Helical Gearboxes

Gearbox condition monitoring(CM)plays a significant role in ensuring the operational reliability and efficiency of a wide range of critical industrial systems such as wind turbines and helicopters.Accurate and timely diagnosis of gear faults will improve the maintenance of gearboxes operating under sub-optimal conditions,avoid excessive energy consumption and prevent avoidable damages to systems.This study focuses on developing CM for a multi-stage helical gearbox using airborne sound.Based on signal phase alignments,Modulation Signal Bispectrum(MSB)analysis allows random noise and interrupting events in sound signals to be suppressed greatly and obtains nonlinear modulation features in association with gear dynamics.MSB coherence is evaluated for selecting the reliable bi-spectral peaks for indication of gear deterioration.A run-to-failure test of two industrial gearboxes was tested under various loading conditions.Two omnidirectional microphones were fixed near the gearboxes to sense acoustic information during operation.It has been shown that compared against vibration based CM,acoustics can perceive the responses of vibration in a larger areas and contains more comprehensive and stable information related to gear dynamics variation due to wear.Also,the MSB magnitude peaks at the first three harmonic components of gear mesh and rotation components are demonstrated to be sufficient in characterizing the gradual deterioration of gear transmission.Consequently,the combining of MSB peaks with baseline normalization yields more accurate monitoring trends and diagnostics,allowing the gradual deterioration process and gear wear location to be represented more consistently.

Optical Nonlinearity in a Novel Optical Signal Regeneration System Based on Cross-Gain Modulation Using Cascaded Semiconductor Optical Amplifiers

We propose a novel optical signal regeneration system based on wavelength converters by use of cross gain modulation in cascaded semiconductor optical amplifiers. The nonlinearity in optical input/output characteristics and eye opening using NRZ signal were archived.

Experimental performance evaluation of quadrature amplitude modulation signal transmission in a silicon microring

We comprehensively characterize the transmission performance of m-ary quadrature amplitude modulation(m-QAM) signals through a silicon microring resonator in the experiment. Using orthogonal frequency-division multiplexing based on offset QAM(OFDM/OQAM) which is modulated with m-QAM modulations, we demonstrate low-penalty data transmission of OFDM/OQAM 64-QAM, 128-QAM, 256-QAM, and 512-QAM signals in a silicon microring resonator. The observed optical signal-to-noise ratio(OSNR) penalties are 1.7 dB for 64-QAM,1.7 dB for 128-QAM, and 3.1 dB for 256-QAM at a bit-error rate(BER) of 2 × 10^(-3) and 3.3 dB for 512-QAM at a BER of 2 × 10^(-2). The performance degradation due to the wavelength detuning from the microring resonance is evaluated, showing a wavelength range of ~0.48 nm with BER below 2 × 10^(-3). Moreover, we demonstrate data transmission of 191.2-Gbit/s simultaneous eight wavelength channel OFDM/OQAM 256-QAM signals in a silicon microring resonator, achieving OSNR penalties less than 2 dB at a BER of 2 × 10^(-2).

Low-power STED nanoscopy based on temporal and spatial modulation

Stimulated emission depletion(STED)nanoscopy enables the visualization of subcellular organelles in unprecedented detail.However,reducing the power dependency remains one of the greatest challenges for STED imaging in living cells.Here,we propose a new method,called modulated STED,to reduce the demand for depletion power in STED imaging by modulating the information from the temporal and spatial domains.In this approach,an excitation pulse is followed by a depletion pulse with a longer delay;therefore,the fluorescence decay curve contains both confocal and STED photons in a laser pulse period.With time-resolved detection,we can remove residual diffraction-limited signals pixel by pixel from STED photons by taking the weighted difference of the depleted photons.Finally,fluorescence emission in the periphery of an excitation spot is further inhibited through spatial modulation of fluorescent signals,which replaced the increase of the depletion power in conventional STED.We demonstrate that the modulated STED method can achieve a resolution of<100 nm in both fixed and living cells with a depletion power that is dozens of times lower than that of conventional STED,therefore,it is very suitable for long-term superresolution imaging of living cells.Furthermore,the idea of the method could open up a new avenue to the implementation of other experiments,such as light-sheet imaging,multicolor and three-demensional(3D)super-resolution imaging.

A WDM-PON with DPSK modulated downstream and OOK modulated upstream signals based on symmetric 10 Gbit/s wavelength reused bidirectional reflective SOA

We investigate a wavelength-division-multiplexing passive optical network(WDM-PON) with centralized lightwave and direct detection. The system is demonstrated for symmetric 10 Gbit/s differential phase-shift keying(DPSK) downstream signals and on-off keying(OOK) upstream signals,respectively. A wavelength reused scheme is employed to carry the upstream data by using a reflective semiconductor optical amplifier(RSOA) as an intensity modulator at the optical network unit(ONU). The constant-intensity property of the DPSK modulation format can keep high extinction ratio(ER) of downstream signal and reduce the crosstalk to the upstream signal. The bit error rate(BER) performance of our scheme shows that the proposed 10 Gbit/s symmetric WDM-PON can achieve error free transmission over 25-km-long fiber transmission with low power penalty.

Evaluation of the performance of linearly frequency modulated signals generated by heterodyning two free-running laser diodes

A method to evaluate the influence of the laser linewidth on the linearly frequency-modulated(LFM)signals generated by heterodyning two free-running laser diodes(LDs)is proposed.The Pearson correlation coefficient between the instantaneous frequency of the generated LFM signal and that of an ideal LFM signal is introduced to quantify the quality of the generated LFM signal.The closed-form solution of the correlation coefficient is given,which shows that the correlation coefficient is determined by the ratio of the LFM signal bandwidth to the square root of the total linewidth of the two LDs when the observation interval is fixed.Simulation results are also given,which proves the correctness of the theoretical results.

Directly Modulated 10 Gb/s Signal Transmission over 320 km of Negative Dispersion Fiber for Regional Metro Network

We demonstrate the transmission of directly modulated 10-Gb/s WDM signals over 320 km of negative dispersion fiber (dispersion: -2.5 ps/km/nm @1550 nm) without dispersion compensation. The results indicate that a regional metro WDM network could be implemented cost-effectively by using the proposed negative dispersion fiber and direct modulated lasers.