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.

Gauss linear frequency modulation wavelet transforms and its application to seismic phases identification

Based on the characteristics of gradual change style seismic signal onset which has more high frequency signal components but less magnitude, this paper selects Gauss linear frequency modulation wavelet as base function to study the change characteristics of Gauss linear frequency modulation wavelet transform with difference wavelet and signal parameters, analyzes the error origin of seismic phases identification on the basis of Gauss linear frequency modulation wavelet transform, puts forward a kind of new method identifying gradual change style seismic phases with background noise which is called fixed scale wavelet transform ratio, and presents application examples about simulation digital signal and actual seismic phases recording onsets identification.

Social Robot Detection Method with Improved Graph Neural Networks

Social robot accounts controlled by artificial intelligence or humans are active in social networks,bringing negative impacts to network security and social life.Existing social robot detection methods based on graph neural networks suffer from the problem of many social network nodes and complex relationships,which makes it difficult to accurately describe the difference between the topological relations of nodes,resulting in low detection accuracy of social robots.This paper proposes a social robot detection method with the use of an improved neural network.First,social relationship subgraphs are constructed by leveraging the user’s social network to disentangle intricate social relationships effectively.Then,a linear modulated graph attention residual network model is devised to extract the node and network topology features of the social relation subgraph,thereby generating comprehensive social relation subgraph features,and the feature-wise linear modulation module of the model can better learn the differences between the nodes.Next,user text content and behavioral gene sequences are extracted to construct social behavioral features combined with the social relationship subgraph features.Finally,social robots can be more accurately identified by combining user behavioral and relationship features.By carrying out experimental studies based on the publicly available datasets TwiBot-20 and Cresci-15,the suggested method’s detection accuracies can achieve 86.73%and 97.86%,respectively.Compared with the existing mainstream approaches,the accuracy of the proposed method is 2.2%and 1.35%higher on the two datasets.The results show that the method proposed in this paper can effectively detect social robots and maintain a healthy ecological environment of social networks.

RECURSIVENESS OF ZERO STRUCTURES FOR SYSTEM MATRICES OF INTERCONNECTED LINEAR SYSTEMS VIA MODULE THEORETIC TOOLS

The importance of the zeros of multwariable linear systems is well-knoiun in terms of measure obstructions to the controllability and the. observability. In this paper, a recursive decarnposi Am oj interconnected systems is outlined by taking into account the sequential structure of the connnections. The paper extends the, coordinate, module-theoretic studies from the elementary algebraic systems theory to include the case oj such linear interconnected systems which need not to be controllable or observable. Also, the properties of controllability and observability, the decoupling zeros and the signal Making issues are characterized.

Symbol detection based on Voronoi surfaces with emphasis on superposition modulation

A challenging task when applying high-order digital modulation schemes is the complexity of the detector. Particularly, the complexity of the optimal a posteriori probability （APP） detector increases exponentially with respect to the number of bits per data symbol. This statement is also true for the Max-Log-APP detector, which is a common simplification of the APP detector. Thus it is important to design new detection algorithms which combine a sufficient performance with low complexity. In this contribution, a detection algorithm for two- dimensional digital modulation schemes which cannot be split-up into real and imaginary parts （like phase shift keying and phase-shifted snperposition modulation （PSM）） is proposed with emphasis on PSM with equal power allocation. This algorithm exploits the relationship between Max-Log-APP detection and a Voronoi diagram to determine planar surfaces of the soft outputs over the entire range of detector input values. As opposed to state-of-the-art detectors based on Voronoi surfaces, a priori information is taken into account, enabling iterative processing. Since the algorithm achieves Max-Log-APP performance, even in the presence of a priori information, this implies a great potential for complexity reduction compared to the classical APP detection.

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).

Mathematic principle of active jamming against wideband LFM radar

The inherent mathematic principle of active jamming against the wideband linear frequency modulated（LFM） radar is investigated. According to different generation strategies, the active jamming methods are reclassified into three groups, i.e.,non-coherent jamming（NCJ）, convolution jamming（CJ） and multiplying jamming（MJ）. Based on the classification, the mathematic principles of different active jamming groups are put forward, which describe the relationships between the modulated signals and the jamming results. The advantages and disadvantages of different groups are further analyzed, which provides a new perspective for the study of jamming/anti-jamming methods and a potential for engineers to integrate similar jamming methods into one jammer platform. The analyses and simulation results of some typical active jamming methods prove the validity of the proposed mathematics principle.

Multi-component LFM signal detection and parameter estimation based on Radon-HHT

A new method is proposed to analyze multi-component linear frequency modulated （LFM） signals, which eliminates cross terms in conventional Wigner-Ville distribution （WVD）. The approach is based on Radon transform and Hilbert-Huang transform （HHT）, which is a recently developed method adaptive to non-linear and non-stationary signals. The complicated signal is decomposed into several intrinsic mode functions （IMF） by the empirical mode decomposition （EMD）, which makes the consequent instantaneous frequency meaningful. After the instantaneous frequency and Hilbert spectrum are computed, multi-component LFM signals detection and parameter estimation are obtained using Radon transform on the Hilbert spectrum plane. The simulation results show its feasibility and effectiveness.

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.

A Novel Orthogonal LoRa Multiple Access Algorithm for Satellite Internet of Things

In recent years,LoRa has been extensively researched in the satellite Internet of Things(IoT).However,the multiple access technology of LoRa is still one of the bottlenecks of satellite IoT.To improve the multiple access performance of LoRa satellite IoT,based on the orthogonality of LoRa symbols in the fractional domain,this paper proposes a low complexity Orthogonal LoRa Multiple Access(OLMA)algorithm for multiple LoRa users occupying the same frequency bandwidth.The algorithm introduces the address code to divide the fractional bandwidth into multiple parts,and the OLMA users with different address codes occupy different parts to transmit the information code,thus avoiding mutual interference caused by collisions in the same frequency bandwidth.The multiple access capability of OLMA can be flexibly configured only by simply adjusting the length of the address code according to the actual application requirements of data transmission.Theoretical analysis and simulation results show that the OLMA algorithm can greatly improve the multiple access capability and the total transmission bit rate of LoRa IoT without changing the existing LoRa modulation parameters and process.

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.

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.

Simultaneous range and radial velocity estimation with a single narrowband LFM pulse

The mismatch effect induced by the radial motion of a target is analyzed for linear frequency modulated （LFM） signals. Then, a novel integrated processing scheme is proposed to re- solve the delay-Doppler coupling effect in LFM pulse compression. Therefore the range and radial velocity of the target can be si- multaneously estimated with a narrowband LFM pulse. Finally, numerical simulation results demonstrate the effectiveness and good performance of the proposed method.

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.

Adaptive digital self-interference cancellation based on fractional order LMS in LFMCW radar

Adaptive digital self-interference cancellation(ADSIC)is a significant method to suppress self-interference and improve the performance of the linear frequency modulated continuous wave(LFMCW)radar.Due to efficient implementation structure,the conventional method based on least mean square(LMS)is widely used,but its performance is not sufficient for LFMCW radar.To achieve a better self-interference cancellation(SIC)result and more optimal radar performance,we present an ADSIC method based on fractional order LMS(FOLMS),which utilizes the multi-path cancellation structure and adaptively updates the weight coefficients of the cancellation system.First,we derive the iterative expression of the weight coefficients by using the fractional order derivative and short-term memory principle.Then,to solve the problem that it is difficult to select the parameters of the proposed method due to the non-stationary characteristics of radar transmitted signals,we construct the performance evaluation model of LFMCW radar,and analyze the relationship between the mean square deviation and the parameters of FOLMS.Finally,the theoretical analysis and simulation results show that the proposed method has a better SIC performance than the conventional methods.

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.

NEW TYPE OF WINDOWS FOR DIGITAL PULSE COMPRESSION IN FREQUENCY DOMAIN FOR BROADBAND LFM SIGNALS

A new type of window called combined window is designed to get higher Ratio of Mainlobe to Sidelobes (RMS) and lower Mainlobe Widening Factor (MWF). Simulation results prove that the new window can solve the contradiction between RMS and MWF better than classic windows.

Novel LFM Jammer Suppression Algorithm for DSSS in FRFT Domain

A novel algorithm is proposed to resolve the defects in the traditional linear frequency modulation(LFM)jamming suppression algorithm in the fractional Fourier transform(FRFT)domain.The traditional LFM jamming suppression algorithm in the FRFT domain has some defects.For example,the exact threshold is difficult to set and both the signal-to-noise ratio(SNR)loss and spectrum leakage are serious in strong jamming-to-signal ratio(JSR)situations.Windowing,overlapping and inverse windowing techniques are used in the FRFT domain to reduce the spectrum leakage and the SNR loss.Under the condition that only the direct sequence spread spectrum(DSSS)signal and white Gaussian noise are present,the amplitude of the received signal after FRFT is assumed to be Rayleigh distributed.Based on the distribution characteristic,hypothesis testing is used to suppress the jamming spectrum using the algorithm.Simulation results show that the performance of proposed algorithm is better than that of the conventional algorithms,especially in a strong jamming and multi component LFM jamming environment.

HIGH RESOLUTION RANGE PROFILE FORMATION BASED ON LFM SIGNAL FUSION OF MULTIPLE RADARS

This paper presents a new method of High Resolution Range (HRR) profile formation based on Linear Frequency Modulation (LFM) signal fusion of multiple radars with multiple frequency bands. The principle of the multiple radars signal fusion improving the range resolution is analyzed. With the analysis of return signals received by two radars,it is derived that the phase difference between the echoes varies almost linearly with respect to the frequency if the distance between two radars is neg-ligible compared with the radar observation distance. To compensate the phase difference,an en-tropy-minimization principle based compensation algorithm is proposed. During the fusion process,the B-splines interpolation method is applied to resample the signals for Fourier transform imaging. The theoretical analysis and simulations results show the proposed method can effectively increase signal bandwidth and provide a high resolution range profile.

A HIGH PERFORMANCE UWB LFM WAVEFORM GENERATOR

In this paper, the design and implementation of a high performance Ultra-WideBand (UWB) Linear Frequency Modulation (LFM) waveform generator at Very High Frequency/Ultra High Frequency (VHF/UHF) band are introduced. Firstly, the design ideas for a high performance UWB LFM waveform generator are described. Then, a generation scheme for UWB LFM waveforms is presented according to the baseband digital generation method combining with the bandwidth ex-tension method via frequency doubling. An experimental system has been implemented and tested. The results show that the UWB LFM waveform generator achieves very high performance.