Spectrum Reconstruction Method Research Articles

Super-resolution spatial spectrum reconstruction under unknown near-field interferences

In underwater acoustic signal processing, conventional spatial spectrum estimation and the associated direction-of-arrival estimation are often impaired by strong near-field interferences at the same frequency as the far-field target signal, leading to significant performance degradation and even detection failure. In contrast to prevalent near-field interference suppression algorithms that require prior knowledge of near-field interference positions, this paper proposes a super-resolution spatial spectrum reconstruction algorithm designed for more general scenarios where crucial information about the near-field interference, such as positions and magnitudes, is unknown. The proposed algorithm demonstrates its adaptability in mitigating unknown near-field interference and achieves this by leveraging rank constraint-based relaxation and alternating minimization, resulting in an effective spatial spectrum reconstruction strategy. The efficacy of the proposed spatial spectrum reconstruction method in handling strong near-field interference is confirmed through analysis of simulated and synthetic experimental data. It exhibits superiority over traditional competitors in terms of resolution, denoising capabilities, and estimation accuracy. Moreover, it achieves comparable results to algorithms that utilize prior information about near-field interference positions. The enhanced performance remains consistent even in challenging scenarios such as snapshot deficiency and low signal-to-noise ratios.

Research on Azimuth DBF Method of HRWS SPC MAB SAR Imaging Mode with Non-Ideal Antenna Mode

Single-phase center multiple azimuth beam (SPC MAB) mode is an effective method for high-resolution wide-swath (HRWS) SAR imaging. The traditional azimuth spectrum reconstruction method for SPC MAB mode is based on the combination scheme from which fake targets along the azimuth direction arise because the inter-beam interference is not considered. When the real antenna mode is inconsistent with the ideal one, the disadvantages of the combination scheme become more serious. In this paper, based on the basic theory of the low-pass, band-limited, multiple-channel under-sampling and reconstruction, a novel digital beam-forming method is proposed for the SPC MAB imaging mode with ideal antenna mode first. The method analyzes the system functions of the sub-beams, based on which digital beam-forming filters are designed for all the sub-beams. The designed filters can reconstruct the correct wide-bandwidth azimuth spectrum and suppress the inter-beam interference simultaneously. Furthermore, the proposed method is extended to SPC MAB mode with the non-ideal antenna mode. The simulation experiments prove the validities of the proposed method both for azimuth spectral reconstruction and the inter-beams interfering suppressing, no matter that the SPC MAB’s antenna mode is ideal or non-ideal.

From Pseudo to Real: Generalized Subspace Method for Power Spectrum Reconstruction

Subspace methods are widely used in statistical signal processing owing to their super-resolution. Conventional subspace methods, such as multiple signal classification (MUSIC) identify parameters containing frequency and direction of arrival by calculating pseudo-spectra without physical amplitudes. However, the real value of amplitude plays a significant role in fault diagnosis and condition monitoring. Additionally, due to hardware limitations and increased frequency, uniform (Nyquist) sampling becomes challenging. Nonuniform sampling, an alternative to high-rate sampling is common in practice. Consequently, Nonuniform (sub-Nyquist) signal processing methods are urgently required. In this paper, a generalized subspace method (GSM) is proposed to bridge the pseudo spectrum with the real spectrum. The proposed method is a supplement to the existing subspace methods and is applicable to both uniform and nonuniform sampling. In the GSM, the Fourier coefficient of the autocorrelation function is derived by signal subspace from the perspective of basis matching. Then, an alias-free real power spectrum is obtained by combining the power spectrum reconstructed by GSM with the classical pseudo spectrum. Finally, simulation and experiment demonstrate the performance of the proposed method in power spectrum reconstruction. Even under the dual influence of undersampled and nonuniform, the proposed method is still effective.

Exploration of genetic algorithms to build a balanced neutron spectra dataset useful to train unfolding techniques based on artificial neural networks

Diverse domains need neutrons unfolding technics to assess the incident neutron energy spectrum. Examples are radiation protection, nuclear reactor physics or criticality safety. Traditionally, methods based on the Bayesian approach requires an initial guess of the solution which may significantly impact the unfolding result. This work proposes a novel method for neutron spectrum reconstruction using machine learning (ML) techniques trained on a large dataset. To ensure the ML algorithm to perform on a large domain of application particular attention has been paid to the dataset creation. We propose a comparison of two methods of building large dataset where the most adequate solution is obtained using a dynamic genetic algorithm (GA). This GA targets optimal combinations of 48 parameters to generate a variety of neutron spectra. The resulting dataset is then used to train a new convolutional neural network architecture for unfolding neutron spectra. Obtained performance metrics of the tested architecture show high efficiency and emphasize the added value of the built dataset.

Compressed Spectrum Reconstruction Method Based on Coding Feature Vector Enhancement

Compressed Spectrum Reconstruction Method Based on Coding Feature Vector Enhancement

Research on Load Spectrum Reconstruction Method of Exhaust System Mounting Bracket of a Hybrid Tractor Based on MOPSO-Wavelet Decomposition Technique

To overcome the limitations of the hybrid tractor bumping tests, which include extended cycle times, high costs, and impracticality for single-part reliability verification, this study focuses on the exhaust system mounting bracket of a hybrid tractor. A novel approach that combines multi-objective particle swarm optimization (MOPSO) and wavelet decomposition algorithms was employed to enhance the reconstruction of shock vibration signals. This approach aims to enable the efficient acquisition of input signals for subsequent shaker table testing. The methodology involves a systematic evaluation of the spectral correlation between the original signal and the reconstructed signal at the stent’s response position, along with signal compression time. These parameters collectively constitute the objective function. The multi-objective particle swarm optimization algorithm is then deployed to explore a range of crucial parameters, including wavelet basic functions, the number of wavelet decomposition layers, and the selection of wavelet components. This exhaustive exploration identifies an optimized signal reconstruction method that accurately represents shock vibration loads. Upon rigorous screening based on our defined objectives, the optimal solution vector was determined, which includes the utilization of the dB10 wavelet basic function, employing a 12-layer wavelet decomposition, and selecting wavelet components a12 and d3~d11. This specific configuration enables the retention of 95% of the damage coefficients while significantly compressing the test time to just 46% of the original signal duration. The implications of our findings are substantial as the reconstructed signal obtained through our optimized approach can be readily applied to shaker excitation. This innovation results in a notable reduction in test cycle time and associated costs, making it particularly valuable for engineering applications, especially in tractor design and testing.

Time delay-based spectrum reconstruction for nonuniform and sub-Nyquist sampling in blade tip timing

Rotating blades play a functional role during the working process of turbomachinery, but they are prone to failure due to harsh working conditions. Blade tip timing (BTT) is a potential technique for operational blade health monitoring due to its noncontact feature and efficiency. The critical issue in the application of BTT is extracting vibration parameters or reconstructing the spectrum for subsequent fault diagnosis. However, the nonuniform and undersampling characteristics of the BTT signal challenge signal processing. In this work, we developed an effective and efficient time delay-based spectrum reconstruction method. On this basis, we proposed the conditions (minimal probe number and delay sampling criterion) for the spectrum reconstruction. The proposed reconstruction conditions apply to most BTT methods for parameter identification or spectrum reconstruction and provide guidance for BTT measurement. Finally, the effectiveness of the proposed method and the correctness of the reconstruction conditions were demonstrated by a series of simulations and experiments.

Unsupervised Representation Learning-Based Spectrum Reconstruction for Demodulation of Fabry–Perot Interferometer Sensor

This paper presents a novel unsupervised representation learning-based demodulation framework for Fabry-Perot interferometer (FPI) sensors, which is a straightforward and effective solution for obtaining interferometric spectrum without any optical spectrum analyzers. The proposed framework utilizes a simple spectrum reconstruction method to reconstruct the FPI sensor’s spectrum using relatively low-scale sample points, requiring less manual effort than conventional approaches. The proposed approach involves two steps: first, an optical system converts the FPI sensing signal to transmitted intensity, and second, the unsupervised representation learning-based reconstruction framework establishes a nonlinear relationship between the intensity signal and the actual changing spectrum. The proposed approach is validated using real-world datasets generated from pressure performance tests, achieving excellent performance with a reconstruction error of 0.039nm and a range of 73nm. The results demonstrate the practical potential viability of the proposed framework for large-scale remote monitoring systems.

Improved prediction of coherent structure in an intermediate turbine duct

Modeling the unsteady flow in an intermediate turbine duct in an aero-engine is highly challenging partly owing to the complex physics subjected to unsteady vortical flow upstream. In this paper, the high-fidelity detached eddy simulations of an aggressive intermediate turbine duct are performed and validated against published data. The dominating coherent flow motions are then captured with sparsity-promoting dynamics mode decomposition under different tip gap sizes and rotating speeds of the rotor blade. Furthermore, a novel low-order spectrum reconstruction method based on selected flow modes and Gaussian fitting is proposed for fast prediction of the instantaneous flowfield in the intermediate turbine duct. The sources of error are discussed and a potential approach to improve the prediction accuracy is proposed. It reveals that the proposed method achieves accurate and generalized performance in reconstructing and predicting flow fields with periodic features for a small cost of calculation time. The maximum root mean squared error in the scenario of tip gap size and rotating speed variation is below 5.96 and 8.41, respectively. The error analysis shows that the variation of flow modes under different inflow conditions and nonlinearity of interacting flow structure accounts for the major part of prediction error. By applying a Proper orthogonal decomposition (POD) interpolation on the flow modes, the maximum root mean squared error in the scenario of tip gap size and rotating speed variation can drop to 4.76 and 2.69 without breaking the physical meaning of the representative modes. The novelty of the present work lies at the proposed prediction method combining DMDSP and POD-interpolation, of which the majority of the physical information are maintained and the prediction accuracy is improved. It may provide a promising approach for turbomachinery design, flow prediction, and investigating underlying mechanisms with low data requirements.

Study on reconstruction and analytical method of seawater radioactive gamma spectrum

Gamma detector detection technology based on NaI(Tl) scintillation crystal has become a popular research topic and has been applied in the field of marine radioactive environment automatic monitoring because of its advantages of low power consumption, low cost and strong environmental adaptability. However, insufficient energy resolution of the NaI(Tl) detector and great Compton scattering in the low-energy region caused by the abundance of natural radionuclides in seawater hinder the automatic analysis of radionuclides in seawater. This study adopts the combination of theoretical derivation, simulation experiment, water tank test and seawater field test, establishing an effective and feasible spectrum reconstruction method. The measured spectrum in seawater is regarded as the output signal formed by the convolution of the incident spectrum and the detector response function. The acceleration factor p is introduced to construct the Boosted-WNNLS deconvolution algorithm, which is used to iteratively reconstruct the spectrum. The analysis results of the simulation test, water tank test and field test meet the radionuclide analysis speed and accuracy requirements for the in-situ automatic monitoring of seawater radioactivity. The spectrum reconstruction method in this study converts the physical problem of insufficient detection accuracy of spectrometer in the practical application into a mathematical problem of deconvolution solution, restores the original radiation information in seawater, and improves the resolution of the seawater gamma spectrum.

Wavenumber Spectrum Reconstruction Method for Microwave Computational Imaging With Re-Programmable Metasurface

Microwave computational imaging (MCI) employs uncorrelated illumination patterns via meta-materials to multiplex the information content of an entire scene into a few simple measurements, enabling image reconstruction through computational strategies. However, the reconstruction process often involves solving an ill-conditioned equation iteratively, which can result in a high computational burden and challenges in ensuring real-time performance. This paper presents a wavenumber spectrum reconstruction (WSR) method for fast imaging with reprogrammable metasurface (RMS). By constructing a series of code configurations, the electric field spectrum at the position of the RMS is measured. Then the wavenumber spectrum of the target is calculated by compensation in the wavenumber domain. Finally, the reconstruction of the target image is performed by inverse Fourier transformation (IFT) operation, which is less computationally burdensome and less time consuming. The proposed approach enables the construction of code configurations independent of the target's location and shape, thereby reducing the difficulty of optimizing measurement patterns. Simulation results show that the WSR method outperforms the traditional compressed sensing method in reconstructing target images with a low signal-to-noise ratio while significantly reducing computation time. The proposed method can satisfy the requirements for real-time performance and noise immunity in MCI systems, and can also be implemented on hardware platforms with limited computing resources.

Reconstruction of LWD-NMR T2 water spectrum and fluid recognition based on microscopic pore structure constraints

The existing fluid property identification methods are not applicable to low resistivity reservoirs. In order to accurately identify low-resistivity oil reservoirs, the effects of fluid properties on the NMR T2 spectrum under different pore structures were analyzed. The results show that the T2 spectrum of the low-resistivity oil section shows a broad and gentle hilly shape, and the T2 spectrum of the water section shows a steep and mountainous shape. Based on the difference characteristics of T2 spectrum of reservoirs with different fluid properties, a new NMR water spectrum reconstruction and fluid property identification method based on microscopic pore structure constraint is innovatively proposed. Based on the drilling NMR logging data, three parameters characterizing the microscopic pore structure are firstly extracted from the NMR T2 spectrum of standard water reservoirs, which reflect the overall shape of the T2 spectrum in terms of transverse relaxation time, longitudinal pore fraction and These three parameters reflect the pore structure, water and oil and gas information of the reservoir in terms of the transverse relaxation time, the longitudinal pore fraction and the overall morphology of the T2 spectrum. Secondly, the microscopic pore structure parameters are used as the coordinate axes to construct a 3D water spectrum sample library, and the standard water formation T2 spectrum samples of the target block are stored in the 3D water spectrum library through the constraints of the three parameters. Finally, the NMR T2 spectrum parameters are extracted from the whole well section and the samples in the water spectrum library are called to realize the water spectrum reconstruction. The parameters that can distinguish oil and water formations are extracted from the measured T2 spectrum and the reconstructed water spectrum and fluid identification is performed. The results show that the reconstructed water spectrum is highly similar to the actual measured T2 spectrum in the production-proven standard water layer, which verifies the accuracy of the water spectrum reconstruction technique; in Caofeidian A oilfield, the fluid identification was performed on 20 production-proven test sections, of which 18 reservoirs were correctly identified, and the identification compliance rate reached 90%. The method circumvents the influence of pore structure in NMR T2 spectrum and highlights the information of its fluid properties, which improves the fluid property identification capability of logging with drilling (LWD) NMR logging.

An AI-based spectral data analysis process for recognizing unique plant biomarkers and disease features

This study introduces a novel method using reduced datasets for extracting plant reflectance signatures. Plant reflectance data from hyperspectral imaging sensors were used to develop biomarker signatures for plant identification and early disease detection purposes. The Karhunen-Loeve Expansion (KLE) of spectral reflectance data, taken from healthy and diseased plants (two case studies of citrus canker in citrus and Laurel wilt in avocado), were used to identify a basis set of functions that represents the distribution of the reflected signal energy. By spectral decomposition, the eigenvalues were related to the KLE basis set. The eigenvalues can be used to identify the KLE eigenvectors, which comprise the highest variation in the data. These components can be interpreted as the weighted variables which carry with them most of the information on the reflectance spectrum of the plant. From indications presented by this multivariate KLE analysis, a frequency reconstruction was adapted to convert the eigenvector information to a wave function. This reconstruction via KLE and frequency transformation formed the signature identification process (i.e., unique biomarker). These frequency spectra can be used as average signature reflectance patterns for plant identification, classification, and disease biomarkers. Defining these spectral identification biomarkers or signatures is purposeful since it could lead to less invasive classification and disease diagnostics techniques. The techniques used to determine these reflectance spectra require a unique spectrum reconstruction method, using Fourier transform methods and KLE for data reduction.

Interrupted sampling repeater jamming countermeasure technology based on random interpulse frequency coding LFM signal

Interrupted sampling repeater jamming (ISRJ) is an active coherent jamming with the characteristic of suppression and deception, which poses a great threat to modern radars. In this work, a false target identification and suppression method based on random interpulse frequency coding linear frequency modulation (RIFC-LFM) signal processing is proposed. First, we analyze the characteristics of the RIFC-LFM signal and ISRJ using the ambiguity function and cross ambiguity function, respectively. Next, taking advantage of the property that most of the false targets formed by ISRJ will be whitened after range-Doppler processing, a false target range-Doppler (RD) spectrum reconstruction method is proposed. Using the reconstructed false target RD spectrum, we can obtain the positions of the unwhitened false targets in the RD spectrum and distinguish the true and false targets. Finally, we design the suppression matrix based on the reconstructed false target RD spectrum to suppress the false targets. The simulation results demonstrate that the proposed approach can effectively suppress three different ISRJs without estimating the ISRJ parameters.

A spectrum reconstruction method for blade vibration measurement based on probe waveform analysis

Blade tip timing (BTT) is regarded as a promising noncontact measurement technology for rotating blade health monitoring. However, the number and layout of probes, required by BTT, hinder the application of BTT. To overcome these shortcomings, a probe waveform spectral reconstruction method (PWSR) is proposed, which combines the advantages of waveform analysis method and wideband signal interpolation method. PWSR can be summarized in two steps: obtaining more displacement information from the output waveform of probes and identifying vibration parameters of blades. Simulations are conducted to investigate the effectiveness of PWSR for single-harmonic and multi-harmonic blade vibration parameters identification. Its sensitivity and robustness are also studied. An experiment to validate the proposed method is performed on a rotating blade test bench, and compared with strain gauge test data. Experimental results show that the proposed method can identify the amplitude, frequency and phase of the blade vibration accurately via one probe.

Investigation on Spectrum Estimation Methods for Bimodal Sea State Conditions.

The reliable monitoring of sea state parameters is a key factor for weather forecasting, as well as for ensuring the safety and navigation of ships. In the current analysis, two spectrum estimation techniques, based on the Welch and Thomson methods, were applied to a set of random wave signals generated from a theoretical wave spectrum obtained by combining wind sea and swell components with the same prevailing direction but different combinations of significant wave heights, peak periods, and peak enhancement factors. A wide benchmark study was performed to systematically apply and compare the two spectrum estimation methods. In this respect, different combinations of wind sea spectra, corresponding to four grades of the Douglas Scale, were combined with three swell spectra corresponding to different swell categories. The main aim of the benchmark study was to systematically investigate the effectiveness of the Welch and Thomson methods in terms of spectrum restitution and the assessment of sea state parameters. The spectrum estimation methods were applied to random wave signals with different durations, namely 600 s (short) and 3600 s (long), to investigate how the record length affected the assembled sea state parameters, which, in turn, were assessed by the nonlinear least square method. Finally, based on the main outcomes of the benchmark study, some suggestions are provided to select the most suitable spectrum reconstruction method and increase the effectiveness of the assembled sea state parameters.

A generalized health indicator for performance degradation assessment of rolling element bearings based on graph spectrum reconstruction and spectrum characterization

The performance degradation assessment (PDA) of rolling element bearings is a necessary link to ensure the reliability of high-end equipment. However, traditional health indicators (HIs) are not sensitive to early defects, and there are often large local fluctuations in the later stage of degradation. Hence, this paper propose a novel PDA method to obtain HIs with early warning capability and monotone trend. Firstly, an improved graph spectrum reconstruction method is proposed to enhance the characteristics of signals. The random phase space reconstruction strategy is introduced to solve the problem of large-scale graph Laplacian matrix decomposition. Then, the spectrums of the enhanced signals are characterized, namely the high-dimensional degradation features in frequency domain are extracted and smoothed by Kalman filter. Finally, the Laplacian Eigenmaps is used to extract the intrinsic degeneration manifolds from the high-dimensional degradation features as the established HIs. Life cycle degradation data and quantitative failure data are analyzed to verify the effectiveness of the proposed method. Compared with other state-of-art methods, the results show that the HIs established by the proposed method reflect the degradation earlier and have obvious degradation trend. It effectively realizes the mapping between degradation and HI.

Sparse Bayesian Learning Based Direction-of-Arrival Estimation under Spatially Colored Noise Using Acoustic Hydrophone Arrays

Direction-of-arrival (DOA) estimation in a spatially isotropic white noise background has been widely researched for decades. However, in practice, such as underwater acoustic ambient noise in shallow water, the ambient noise can be spatially colored, which may severely degrade the performance of DOA estimation. To solve this problem, this paper proposes a DOA estimation method based on sparse Bayesian learning with the modified noise model using acoustic vector hydrophone arrays. Firstly, an applicable linear noise model is established by using the prolate spheroidal wave functions (PSWFs) to characterize spatially colored noise and exploiting the excellent performance of the PSWFs in extrapolating band-limited signals to the space domain. Then, using the proposed noise model, an iterative method for sparse spectrum reconstruction is developed under a sparse Bayesian learning (SBL) framework to fit the actual noise field received by the acoustic vector hydrophone array. Finally, a DOA estimation algorithm under the modified noise model is also presented, which has a superior performance under spatially colored noise. Numerical results validate the effectiveness of the proposed method.

Denoising Autoencoder Aided Spectrum Reconstruction for Colloidal Quantum Dot Spectrometers

Recently, the colloidal quantum dot spectrometer has received much attention due to its advantages in cost, size, and operation. Yet, just like many other filter-based miniature spectrometers, spectrum reconstruction for the colloidal quantum dot spectrometer is typically prone to the measurement noise due to the correlation of the filters. In this paper, we propose an effective spectrum reconstruction method for the colloidal quantum dot spectrometer, which can recover high-quality spectra in noisy environments. Specifically, we employ a denoising autoencoder, a machine-learning approach, to reduce noise in the filters’ raw measurements before performing the reconstruction. After that, we reconstruct the spectra with the denoised data by a sparse recovery algorithm. We investigate the feasibility of the proposed reconstruction approach on a synthetic dataset and an experimental dataset collected by the colloidal quantum dot spectrometer. The results demonstrate that the proposed approach could deliver accurate reconstruction results even when data are corrupted with the measurement noise.

A Novel Azimuth Spectrum Reconstruction and Imaging Method for Moving Targets in Geosynchronous Spaceborne–Airborne Bistatic Multichannel SAR

Geosynchronous spaceborne–airborne bistatic multichannel synthetic aperture radar (GEO-SABM SAR) is a form of bistatic SAR constructed by a GEO transmitter and an airborne multichannel receiver. The frequent coverage of earth observations in GEO-SABM SAR provides a great advantage in SAR-ground moving target indication (SAR-GMTI). However, the bandwidth of the azimuth spectrum in this configuration, which is larger than the pulse repetition frequency (PRF), makes the whole spectrum fold into several subsegments, i.e., azimuth spectrum aliasing (ASA) occurs. In addition, azimuth Doppler ambiguity (ADA) caused by the larger radial velocity of the moving target may occur simultaneously. To address these problems, an improved velocity SAR (VSAR)-based azimuth spectrum reconstruction method for moving targets is proposed. The influence of ASA and ADA on the signal of the moving targets is analyzed, and a signal model after coarse imaging with static target parameters is deduced. From the signal model, each subsegment has different Doppler frequencies and can be extracted based on improved VSAR. Meanwhile, the Doppler ambiguous integer has a definite relationship with the slope of the range migration of the subsegments after a range cell migration correction (RCMC) based on the static target parameters and can be estimated by this relationship. Furthermore, based on the extracted spectrum subsegments and estimated Doppler ambiguous integer, the azimuth spectrum can be reconstructed. Finally, the effectiveness of the proposed method is verified by the numerical experiments.