Frequency Domain Processing Research Articles

GNSS spoofing detection based on frequency domain processing

Global Navigation Satellite System (GNSS) spoofing detection has attracted much attention in recent years to ensure security. However, most of the existing methods use the time domain feature and can be easily affected by interference, which limits the practical application. To solve this problem, the frequency domain processing approach is investigated and a novel spoofing detection method is proposed. The basic idea is to exploit the spoofing-induced feature by transforming the correlator output into the frequency domain. The proposed method has a simple implementation logic and requires only minor software upgrades to existing GNSS receivers. Simulation and experimental results are presented to verify the effectiveness of the proposed method. The results demonstrate that it exhibits superior detection performance and shows robustness to interference. Consequently, this method has great application value and can serve as a valuable complement to current detection methods.

An online decoupling-whitening frequency domain filtered-error least mean square algorithm for active road noise control.

Active road noise control (ARNC) systems have been widely investigated for low-frequency road noise attenuation in vehicle cabins. Multiple reference and error sensors are usually required to ensure noticeable noise reduction. However, this tends to slow down the convergence speed of adaptive algorithms due to the coupling of secondary paths and the cross correlation of reference signals. Furthermore, the high computational burden of normally utilized multichannel control algorithms exacerbates the difficulty of practical implementations. In this paper, an online decoupling-whitening frequency domain filtered-error least mean square (ODW-FDFeLMS) algorithm is proposed to address the aforementioned problems. Secondary path decoupling through inner-outer product decomposition and online reference whitening through spectral factorization effectively accelerate the convergence rate. Additionally, the utilization of the filtered-error algorithm based on frequency domain processing mitigates the computational complexity. Simulations with measured road noise data confirm the superiority of the ODW-FDFeLMS algorithm over existing algorithms in terms of convergence speed and computational complexity. Real-time experiments in a vehicle cabin further confirm the effectiveness of the proposed algorithm.

FLRNN-FGA: Fractional-Order Lipschitz Recurrent Neural Network with Frequency-Domain Gated Attention Mechanism for Time Series Forecasting

Time series forecasting has played an important role in different industries, including economics, energy, weather, and healthcare. RNN-based methods have shown promising potential due to their strong ability to model the interaction of time and variables. However, they are prone to gradient issues like gradient explosion and vanishing gradients. And the prediction accuracy is not high. To address the above issues, this paper proposes a Fractional-order Lipschitz Recurrent Neural Network with a Frequency-domain Gated Attention mechanism (FLRNN-FGA). There are three major components: the Fractional-order Lipschitz Recurrent Neural Network (FLRNN), frequency module, and gated attention mechanism. In the FLRNN, fractional-order integration is employed to describe the dynamic systems accurately. It can capture long-term dependencies and improve prediction accuracy. Lipschitz weight matrices are applied to alleviate the gradient issues. In the frequency module, temporal data are transformed into the frequency domain by Fourier transform. Frequency domain processing can reduce the computational complexity of the model. In the gated attention mechanism, the gated structure can regulate attention information transmission to reduce the number of model parameters. Extensive experimental results on five real-world benchmark datasets demonstrate the effectiveness of FLRNN-FGA compared with the state-of-the-art methods.

GFRENet: An Efficient Network for Underwater Image Enhancement with Gated Linear Units and Fast Fourier Convolution

Underwater image enhancement is critical for a variety of marine applications such as exploration, navigation, and biological research. However, underwater images often suffer from quality degradation due to factors such as light absorption, scattering, and color distortion. Although current deep learning methods have achieved better performance, it is difficult to balance the enhancement performance and computational efficiency in practical applications, and some methods tend to cause performance degradation on high-resolution large-size input images. To alleviate the above points, this paper proposes an efficient network GFRENet for underwater image enhancement utilizing gated linear units (GLUs) and fast Fourier convolution (FFC). GLUs help to selectively retain the most relevant features, thus improving the overall enhancement performance. FFC enables efficient and robust frequency domain processing to effectively address the unique challenges posed by the underwater environment. Extensive experiments on benchmark datasets show that our approach significantly outperforms existing state-of-the-art techniques in both qualitative and quantitative metrics. The proposed network provides a promising solution for real-time underwater image enhancement, making it suitable for practical deployment in various underwater applications.

Information Entropy Analysis of a PIV Image Based on Wavelet Decomposition and Reconstruction.

In particle image velocimetry (PIV) experiments, background noise inevitably exists in the particle images when a particle image is being captured or transmitted, which blurs the particle image, reduces the information entropy of the image, and finally makes the obtained flow field inaccurate. Taking a low-quality original particle image as the research object in this research, a frequency domain processing method based on wavelet decomposition and reconstruction was applied to perform particle image pre-processing. Information entropy analysis was used to evaluate the effect of image processing. The results showed that useful high-frequency particle information representing particle image details in the original particle image was effectively extracted and enhanced, and the image background noise was significantly weakened. Then, information entropy analysis of the image revealed that compared with the unprocessed original particle image, the reconstructed particle image contained more effective details of the particles with higher information entropy. Based on reconstructed particle images, a more accurate flow field can be obtained within a lower error range.

Coupling effect of machine tool dynamic characteristics and cutting conditions on the cutting process vibration and high-speed micro-planing surface mid-frequency waviness

In addition to the dynamic characteristics of the machine tool, cutting conditions significantly influences the vibration of the cutting system and mid-frequency waviness of the workpiece surface in ultra-precision machining (UPM). In this work, the effects of cutting conditions on machining vibration and surface waviness were investigated by high-speed micro-planning experiments. The origin of vibration and waviness were elucidated by frequency-domain processing of the vibration signal and surface waviness analysis. The dynamic characteristics of machine tool are determined using modal test and analysis. The results revealed that the cutting force has different contributions to the workpiece vibration and tool vibration. In the time domain, distinct differences can be found in stability and attenuation of various waviness components. The divergent effects of cutting force components in different vibration forms are confirmed through a novel cutting force model of micro-planing. The investigation can further reveal the formation mechanisms of cutting system vibration and surface waviness during the micro-cutting process, which can provide practical guidance for suppressing mid-frequency waviness on machined surfaces.

A comparative Analysis of Image Enhancement Using Autoencoders and Generative Adversarial Networks

The purpose of this study, which focuses on image enhancement methods, is to investigate how various methods contribute to higher-quality photos in particular applications. Tracing the history of image improvement, encompassing the technological evolution of early photography from the 19th century to the present. The foundation of conventional image enhancement technology, which directly affects image elements and transformation coefficients, respectively, emphasizes frequency domain and spatial domain processing. It is noted that the application of autoencoders and generative adversarial networks to image enhancement as a deep learning training strategy has shown effectiveness in picture classification, target recognition, and image segmentation. This article primarily examines and contrasts autoencoders and generative adversarial networks, summarizing their applicability, constraints, benefits, and drawbacks and talking about the patterns in their upcoming development. The study has guiding value for advancing the development of picture technology and offers significant theoretical and empirical support for the field of image enhancement.

An intelligent network traffic prediction method based on Butterworth filter and CNN–LSTM

Accurate and real-time network traffic prediction is of paramount importance in the fields of network management, performance optimization, and fault diagnosis. It provides strong support for autonomous network control, network administration and network services. Therefore, we propose a novel approach for network traffic prediction, which integrates the Butterworth filter, Convolutional Neural Network and Long Short-Term Memory network(BWCL). First, this method the network traffic data to frequency domain processing, utilizing the Butterworth filter to extract its low-frequency component. The residual component is generated by subtracting the low-frequency component from the network traffic sequence. Then, CNN–LSTM prediction models are employed to capture the spatial and temporal features of the data in different frequency bands. Finally, the prediction results of the two models are linearly summed to represent the final prediction value. To validate the feasibility of the proposed model, we construct a variety of datasets with statistical features by taking the raw network traffic in single\\multi-cell scenarios at two different temporal granularities: minutes and hours. In the Pytorch experimental environment, we evaluate the performance of the model using MSE, RMSE, MAE, and R2 performance metrics. The experimental results show that the prediction accuracy of the model is improved by 25% compared to the existing time series prediction models. This innovative approach provides new ideas in the field of time series forecasting, which has a broad application prospect.

A Smart Low-Cost QCM-D Based on Lightweight Frequency Domain Processing

A Smart Low-Cost QCM-D Based on Lightweight Frequency Domain Processing

АВТОМАТИЧНИЙ АНАЛІЗ ЗАВАНТАЖЕНОСТІ РАДІОЧАСТОТНОГО СПЕКТРА ТА СЕЛЕКЦІЯ СИГНАЛІВ

Constant increasing of radio-electronic devices number leads to an increase of spectrum occupancy and complication of radio-electronic environment. In such conditions, it is necessary to use automatic algorithms for prompt detection and selection of radio signals in radio monitoring systems. The purpose of the article is to develop algorithms and recommendations for their practical implementation to automate the process of radio frequency spectrum occupancy analysis and signal selection in a wide dynamic range with high occupancy of analyzed frequency band in conditions of a priori uncertainty of noise level. To achieve the stated goal were developed generalized algorithm for radio frequency spectrum analysis, algorithm of frequency domain processing and algorithm for frequency channels analysis. Examples of spectrum bands analysis with high occupancy and recommendations for developed algorithms implementation were represented. Depending on variability of noise power in analyzed frequency band an adaptive change of estimation time interval is provided, which will reduce the computational complexity. In conditions of no uniform noise level or a wide dynamic range of signals, it is proposed to use special test statistics to determine fullness of analyzed frequency channels. In the course of experimental studies, the analysis of recordings in frequency bands of 900 MHz, 1800 MHz and 2100 MHz was carried out. It was established that the developed algorithms provide a correct estimation of radio frequency spectrum occupancy and selection of signals with a dynamic range of at least 35 dB and occupancy of up to 92%. Developed algorithms do not require calibration of the equipment in case of fluctuations in characteristics of separate system elements and changes in radio-electronic environment and can be implemented in existing and perspective automatic radio monitoring systems. Keywords: radio frequency spectrum; spectrum occupancy; signal selection; test statistics; frequency channel.

Hybrid Wideband Beamforming for Sum Spectral Efficiency Maximization in Millimeter-Wave Relay-Assisted Multiuser MIMO Cognitive Radio Networks

Relay-assisted hybrid beamforming plays an inevitable role in enhancing network coverage, transmission range, and spectral efficiency while simultaneously reducing hardware cost, power consumption, and hardware implementation complexity. This study investigates a cognitive radio network (CRN)-based hybrid wideband transceiver for millimeter-wave (mm-wave) decode-and-forward (DF) relay-assisted multiuser (MU) multiple-input multiple-output (MIMO) systems. It is worth mentioning that the underlying problem has not been addressed so far, which is a real motivation behind the proposed algorithm. The joint optimization of hybrid processing components and the constant amplitude constraints imposed by the analog beamforming solution make this problem non-convex and NP-hard. Furthermore, the analog beamformer common to all sub-carriers is another challenging aspect of the underlying problem. To derive the frequency-flat analog processing component in the radio frequency (RF) domain and frequency-dependent baseband processing matrices in the baseband domain, the original complicated problem is reformulated as two single-hop sum-rate maximization sub-problems. Taking advantage of this decomposition, the sum spectral efficiency is maximized through RF precoding and combining. On the other hand, the impact of interference among transmitted data streams and inter-user interference (IUI) is minimized via baseband processing matrices. Finally, computer simulations are conducted by changing system parameters, considering both perfect and imperfect channel state information (CSI). Simulation results demonstrate that the proposed algorithm achieves performance close to full-complexity precoding and outperforms other well-known hybrid beamforming techniques. Specifically, more than 95% efficiency is achieved with perfect CSI, and more than 90% efficiency is attained under the assumption of 30% error in the estimated channels.

Weather Radar Parameter Estimation Based on Frequency Domain Processing: Technical Details and Performance Evaluation

Parameter estimation is important in weather radar signal processing. Time-domain processing (TDP) and frequency-domain processing (FDP) are two basic parameter estimation methods used in the weather radar field. TDP is widely used in operational weather radars because of its high efficiency and robustness; however, it must be assumed that the received signal has a symmetrical or Gaussian power spectrum, which limits its performance. FDP does not require assumptions about its power spectrum model and has a seamless connection to spectrum analysis; however, its application performance has not been fully validated to ensure its robustness in an operational environment. In this study, we introduce several technical details in FDP, including window function selection, aliasing correction, and noise correction. Additionally, we evaluate the performance of FDP and compare the performance of FDP and TDP based on simulated and measured weather in-phase/quadrature (I/Q) data. The results show that FDP has potential for operational applications; however, further improvements are required, e.g., windowing processing for signals mixed with severe clutter.

Integrated multipole acoustic modeling and processing in general stressed formations, Part 2: A well case study

Stress-induced borehole multipole waveform characterization and processing interpretation are critical for borehole stabilization and hydraulic fracturing development. In the first paper (Part 1), an effective approach was presented to simulate the multipole acoustic responses of a stressed formation. In this second paper (Part 2), the objective is to analyze the shear (S)-wave slowness and anisotropy processing methods for stressed formations using the borehole acoustic logging data from the Mesopotamia Basin, Iraq. An integrated approach for evaluating acoustic slowness and anisotropy is presented. This approach combines a time-domain controlled slowness-time-coherence (TCSTC) method for the monopole logging, waveform inversion and frequency-domain processing methods for the four-component (4C) data from the cross-dipole acoustic logging measurement, to provide an overall assessment of acoustic characteristics around borehole under stress. The results of single depth point and interval data processing demonstrate that the proposed TCSTC can be well applied to the slowness extraction of monopole fast and slow shear waves, especially in the case of fast formation where the slowness of fast and slow transverse waves is difficult to distinguish. Meanwhile, the anisotropy results show that stress-induced S-wave anisotropy results from dipole logging data based on frequency domain processing methods are generally larger than those from dipole and monopole logging data based on time domain methods. Integrating the slowness results of the fast and slow shear waves extracted from the monopole and dipole measurements allows us to assess the velocity variations at radial distances along the borehole under stress, which can be well interpreted using the radial shear-wave velocity profile. A potential limitation of this integration approach is the need for analysis of acoustic multipole logging data, which can be available as part of routine logging measurements. The processing and analysis results can be used as a guideline for understanding and interpreting acoustic measurements in stressed formations.

Super-Resolution Degradation Model: Converting High-Resolution Datasets to Optical Zoom Datasets

Despite remarkable progress in single-image super-resolution based on neural networks, the results are not ideal when applied to real-world images, because the real-world degradation process is unknown and complex. The emergence of some optical zoom datasets shows that neural networks still achieve good results on real-world images as long as the low-resolution images used for training have similar features and distributions with the real-world images. However, obtaining such optical zoom datasets is complicated and the datasets are only applicable to specific cameras and shooting conditions. By studying the optical zoom datasets, we propose a super-resolution image degradation model consisting of blurring, frequency domain processing, adding noise and downsampling. Specifically, blurring uses a blur kernel with a wave-like shape inferred from the point spread function, which produces the artifacts like real-world images. Frequency domain processing simulates the frequency domain aliasing of real-world images, such as jagged edges and background stripes. Experiments demonstrate that the new degradation model achieves visual effects comparable to optical zoom datasets. Existing high-resolution datasets can be converted to “optical zoom datasets” by the degradation model, where the synthetic low-resolution images have real-world image features, thereby extending super-resolution methods to real-world images.

Channel Estimation for LEO Satellite Massive MIMO OFDM Communications

In this paper, we investigate the massive multiple-input multiple-output orthogonal frequency division multiplexing channel estimation for low-earth-orbit satellite communication systems. First, we use the angle-delay domain channel to characterize the space-frequency domain channel. Then, we show that the asymptotic minimum mean square error (MMSE) of the channel estimation can be minimized if the array response vectors of the user terminals (UTs) that use the same pilot are orthogonal. Inspired by this, we design an efficient graph-based pilot allocation strategy to enhance the channel estimation performance. In addition, we devise a novel two-stage channel estimation (TSCE) approach, in which the received signals at the satellite are manipulated with per-subcarrier space domain processing followed by per-user frequency domain processing. Moreover, the space domain processing of each UT is shown to be identical for all the subcarriers, and an asymptotically optimal vector for the per-subcarrier space domain linear processing is derived. The frequency domain processing can be efficiently implemented by means of the fast Toeplitz system solver. Simulation results show that the proposed TSCE approach can achieve a near performance to the MMSE estimation with much lower complexity.

Short‐critical‐path algorithm for allpass transform

AbstractAllpass transform is a key element of the discrete systems which produces signal processing in warped frequency domain. The paper presents a new short‐critical path algorithm for allpass transform, which essentially consists of calculating the outputs of allpass filters chain. The algorithm is based on the proposed dual allpass filter structure, which computes the outputs of a cascade of two allpass filters in one computational pass. The dual allpass filter structure is obtained using the state variable representation of the allpass filter. The efficiency of the proposed algorithm is analyzed in the context of allpass transform implementation using a general purpose CPU. The results of the experiment show that the use of the proposed algorithm accelerates the calculation of the allpass transform by 1.81 times.

Research on Common Fault Diagnosis of Mud Pump Based on Wavelet Packet Analysis

The utilization of vibration signals to diagnose faults in centrifugal pumps is a crucial research direction in this field. Traditional frequency domain processing methods, such as Fast Fourier Transform (FFT), may not be effective in analyzing complex signals where multiple types of faults occur simultaneously at the same frequency. This paper proposes the use of wavelet packet analysis as an alternative. By analyzing fault conditions caused by blade damage and clearance wear in a mud pump and comparing the results to those obtained through the FFT analysis method, the superior accuracy of wavelet packet analysis is demonstrated.

Millimeter-Wave Imaging via Circular-Arc MIMO Arrays

Millimeter-wave (MMW) imaging has a promising prospect in application of concealed weapons detection. To that end, we propose a circular-arc multiple-input–multiple-output (MIMO) array scheme with uniformly spaced transmit and receive antennas along the horizontal-arc direction while scanning along the vertical direction. The antenna beams of the circular-arc MIMO array can provide more uniform coverage of the imaging scene than those of linear or planar MIMO arrays, which is especially important for the detection of concealed weapons carried by personnel. Furthermore, a near-field 3-D imaging technique, based on spatial frequency-domain processing, is presented together with a detailed analysis of sampling criteria and resolutions. Numerical and experimental results are provided to demonstrate the performance of the proposed method in comparison with those of the currently most used array schemes and imaging algorithms.

Empirical Correlations for Predicting Flow Rates Using Distributed Acoustic Sensor Measurements, Validated with Wellbore and Flow Loop Data Sets

Summary Distributed acoustic sensing (DAS) is an emerging surveillance technology that is becoming increasingly popular in the oil and gas industry for real-time flow monitoring. However, there are limited studies that rigorously quantify flow rates using DAS. This work expands the existing literature by presenting a detailed workflow for accurately estimating fluid flow rates from DAS data using time- and frequency-domain signal processing. Three simple empirical correlation functions (linear, exponential, and cubic) are developed and tested to predict flow rates from DAS. The proposed correlations are demonstrated for flow rates ranging from 50 to 300 gallons per minute (GPM) in a vertical 5,163-ft-deep wellbore and from 12 to 36 GPM in a horizontal surface flow loop. Tests were performed using a single-phase flow of water as well as using synthetic oil-based drilling mud. Time-domain DAS processing using root-mean-square (RMS) value and frequency-domain processing using frequency band energy (FBE) is evaluated, followed by a statistical approach to minimize the influence of outliers. The RMS and FBE approaches are individually compared for flow prediction, and the performance of the correlations is rigorously evaluated on a blind data set that was not originally used for developing the correlations. For both the wellbore and flow loop data sets, a coefficient of determination (or R2) greater than 0.95 with an average flow rate prediction error of less than 10% was achieved for the best-performing correlation for the blind test data. The analysis procedure and workflow presented in this study can be adopted and extended to different operating conditions for quantitative flow rate prediction using DAS.

A Radar Detection Method of Reentry Target Based on Frequency-Domain Processing

When the reentry target passes through the earth’s atmosphere, its surface is covered with a layer of plasma sheath. In the process of radar detection of the reentry target, the echo signal will couple with multi-Doppler frequency components and form multiple mismatch effects with matched filter, which will produce false targets on the 1-D range profile. Meanwhile, the excessive speed of the reentry target will lead to the phenomenon of migration through range cells (MTRCs), which will further affect the detection and recognition of the target. This article proposes an improved frequency-domain compensation method that aims at false target and cross-range unit migration. By constructing the intrapulse Doppler frequency and interpulse Doppler frequency compensation functions, the multiperiod echo signal is processed to eliminate the MTRC phenomenon and improve the energy gain of the real target. Finally, the feasibility of the proposed algorithm is verified by simulation, which lays a theoretical foundation for the effective detection and reliable tracking of plasma-sheath-covered targets.