Echo Signal Research Articles

Research on intelligent identification algorithm of coal and rock strata based on Hilbert transform and amplitude stacking

AbstractThe high precision identification of coal–rock layers is a significant challenge in intelligent mining. There is a large amount of electromagnetic noise and metal reflector signals in the full space detection environment of mining roadway, which makes it hard to distinguish the reflected waves at interface from a set of echo signals generated by the interface due to the similar amplitudes among them. So the method of identifying layers solely based on amplitude characteristics has poor stability and accuracy in coal mining environments. This paper proposes a method for identifying coal–rock layers based on Hilbert transform and tracking–scanning–stacking technology. There are two steps to achieve the recognition of air–coal–rock interfaces. First, by analysing the instantaneous amplitude spectrum obtained from the Hilbert transform, the first extreme point that is always the maximum value within a wavelength range is determined as the rough position of the air–coal interface. To solve the problem of recognition errors caused by noise and energy dispersion, the density difference method is used to remove discrete points. Second, the precise position of the air–coal interface is determined by tracking the extreme points within the 1.5 wavelength range around the rough position, and using the amplitude stacking method to quantitatively analyse and compare the degree of energy concentration. The data between zero time and the reflected waves at the air–coal interface is removed to avoid the impact of them on the recognition of the coal–rock interface. Results of physical model experiments and actual coal mine experiments show that this method yields better results and has high stability compared to conventional recognition method. Moreover, the average relative thickness errors are 4.5% for air layer and 4.2% for coal layer.

Echo-Level SAR Imaging Simulation of Wakes Excited by a Submerged Body.

The paper introduces a numerical simulation method for Synthetic Aperture Radar (SAR) imaging of submerged body wakes by integrating hydrodynamics, electromagnetic scattering, and SAR imaging simulation. This work is helpful for better understanding SAR images of submerged body wakes. Among these, the hydrodynamic model consists of two sets of ocean dynamics closely related to SAR imaging, namely the wake of the submerged body and wind waves. For the wake, we simulated it using computational fluid dynamics (CFD) numerical methods. Furthermore, we compared and computed the electromagnetic scattering characteristics of wakes under various navigation parameters and sea surface conditions. Following that, based on the operational principles and imaging theory of synthetic aperture radar (SAR), we established the SAR raw echo signal of the wake. Employing a Range-Doppler (RD) algorithm, we generated simulated SAR images of the wake. The results indicate that utilizing Computational Fluid Dynamics (CFD) numerical methods enables the simulation of wake characteristics generated by the motion of a submerged body with different velocities. The backscattering features of wakes are closely associated with the relative orientation between the wake and the radar line of sight. Under specific wind speeds, the wake gets masked within the sea surface background, resulting in less discernible characteristics of the wake in SAR images. This suggests that at lower speeds of submerged body or under specific wind conditions, the detectability of the wake in SAR images significantly diminishes.

Enhancement of Vital Signals for UWB Through-Wall Radar Using Low-Rank and Block-Sparse Matrix Decomposition

Ultra-wideband (UWB) vital detection radar plays an important role in post-disaster search and rescue, but the vital signal acquired in practice is often submerged in noise. In this paper, an advanced signal processing algorithm based on low-rank block-sparse representation is proposed to enhance the vital signal in life detection radar applications. The preprocessed echo signal can be decomposed into low-rank and block-sparse parts. The alternate direction method (ADM) is employed to obtain the block-sparse part containing the desired vital signal. We solve the subproblems involved in the ADM method using the Douglas/Peaceman–Rachford (DR) monotone operator splitting method. The projection method is applied to accelerate the calculation. Simulation and experimental results show that the proposed algorithm outperforms existing methods in terms of output signal-to-noise ratio (SNR).

Multidimensional high-harmonic echo spectroscopy: Resolving coherent electron dynamics in the EUV regime

We theoretically propose a multidimensional high-harmonic echo spectroscopy technique which utilizes strong optical fields to resolve coherent electron dynamics spanning an energy range of multiple electronvolts. Using our recently developed semi-perturbative approach, we can describe the coherent valence electron dynamics driven by a sequence of phase-matched and well-separated short few-cycle strong infrared laser pulses. The recombination of tunnel-ionized electrons by each pulse coherently populates the valence states of a molecule, which allows for a direct observation of its dynamics via the high harmonic echo signal. The broad bandwidth of the effective dipole between valence states originated from the strong-field excitation results in nontrivial ultra-delayed partial rephasing echo, which is not observed in standard two-dimensional optical spectroscopic techniques in a two-level molecular systems. We demonstrate the results of simulations for the anionic molecular system and show that the ultrafast valence electron dynamics can be well captured with femtosecond resolution.

An Airborne Arc Array Synthetic Aperture Radar Vibration Error Compensation Method

Compared to conventional radars, arc array synthetic aperture radar (SAR) enables wide-area observation under ideal conditions. However, helicopters carrying arc array SAR platforms are generally smaller in size and more sensitive to vibration, which has a greater impact on the imaging quality. In this paper, the vibration error of the arc array SAR platform is investigated, and a vibration error model of the arc array SAR platform is established. Based on the study of the vibration error model, a vibration phase estimation and compensation algorithm based on the delayed conjugate multiplication method is proposed. In the first step, distance pulse pressure processing is performed on the echo signal. In the second step, the pulse pressure signals and their delays in the same distance unit are subjected to conjugate multiplication, and the phase of the signal after conjugate multiplication is extracted. The extracted phase is then amplitude- and phase-compensated to estimate the vibration phase. In the third step, the vibration phase is compensated in the azimuthal direction of the distance pulse pressure signal, and the pairwise echo is eliminated, which completes the compensation of the airborne arc array SAR vibration platform.

Improved cancer risk stratification of isoechoic thyroid nodules to reduce unnecessary biopsies using quantitative ultrasound.

Gray-scale ultrasound (US) is the standard-of-care for evaluating thyroid nodules (TNs). However, the performance is better for the identification of hypoechoic malignant TNs (such as classic papillary thyroid cancer) than isoechoic malignant TNs. Quantitative ultrasound (QUS) utilizes information from raw ultrasonic radiofrequency (RF) echo signal to assess properties of tissue microarchitecture. The purpose of this study is to determine if QUS can improve the cancer risk stratification of isoechoic TNs. Patients scheduled for TN fine needle biopsy (FNB) were recruited from the Thyroid Health Clinic at Boston Medical Center. B-mode US and RF data (to generate QUS parameters) were collected in 274 TNs (163 isoechoic, 111 hypoechoic). A linear combination of QUS parameters (CQP) was trained and tested for isoechoic [CQP(i)] and hypoechoic [CQP(h)] TNs separately and compared with the performance of conventional B-mode US risk stratification systems. CQP(i) produced an ROC AUC value of 0.937+/- 0.043 compared to a value of 0.717 +/- 0.145 (p >0.05) for the American College of Radiology Thyroid Imaging, Reporting and Data System (ACR TI-RADS) and 0.589 +/- 0.173 (p >0.05) for the American Thyroid Association (ATA) risk stratification system. In this study, CQP(i) avoids unnecessary FNBs in 73% of TNs compared to 55.8% and 11.8% when using ACR TI-RADS and ATA classification system. This data supports that a unique QUS-based classifier may be superior to conventional US stratification systems to evaluate isoechoic TNs for cancer and should be explored further in larger studies.

Relevance of phase information for object classification in automotive ultrasonic sensing using convolutional neural networks

Automotive ultrasonic sensors come into play for close-range surround sensing in parking and maneuvering situations. In addition to ultrasonic ranging, classifying obstacles based on ultrasonic echoes to improve environmental perception for advanced driver-assistance systems is an ongoing research topic. Related studies consider only magnitude-based features for classification. However, the phase of an echo signal contains relevant information for target discrimination. This study discusses and evaluates the relevance of the target phase in echo signals for object classification in automotive ultrasonic sensing based on lab and field measurements. Several phase-aware features in the time domain and time-frequency features based on the continuous wavelet transform are proposed and processed using a convolutional neural network. Indeed, phase features are found to contain relevant information, producing only 4% less classification accuracy than magnitude features when the phase is appropriately processed. The investigation reveals high redundancy when magnitude and phase features are jointly fed into the neural network, especially when dealing with time-frequency features. However, incorporating the target phase information facilitates the identification quality in high clutter environments, increasing the model's robustness against signals with low signal-to-noise ratios. Ultimately, the presented work takes one further step toward enhanced object discrimination in advanced driver-assistance systems.

High resolution imaging of acoustic orbital angular momentum wave based on orthogonal matching tracking

The orbital angular momentum (OAM) wave has shown great potential for improving radar imaging and underwater communication performance due to its helical wavefront phase and infinite orthogonal modes. However, there are currently no known applications of this technology in underwater imaging. In this paper, we employed acoustic OAM wave for underwater imaging and established transceiver signal models using the uniform circular array. We concurrently achieved two-dimensional imaging of azimuth and elevation angles, which differs from radar imaging. We proposed a matching process for the echo signal in the modal domain, the OAM wave beam image's sidelobe decreased by 7.9 dB in the elevation direction and 6.1 dB in the azimuth direction compared to the plane wave, with the mainlobe decreased by 0.2° in the elevation direction and 0.4° in the azimuth direction. Furthermore, this paper introduced OAM wave high-resolution image reconstruction based on the orthogonal matching pursuit (OMP) algorithm. Finally, we implemented broadband acoustic OAM wave for underwater imaging and introduced an image reconstruction method based on the modal domain OMP algorithm. Simulation results demonstrate that the use of OAM wave in underwater imaging is feasible, and the proposed scheme can achieve high-resolution imaging.

Real-Time Walk Error Compensation Method Using Echo Signal Magnitude Measurement in ToF Laser Scanners.

The rapid advancement of mobile laser scanner technology used for terrain mapping, among other things, imposes increasing requirements for scanning frequency and distance measurement accuracy. To meet these requirements, rangefinder modules are expected to operate with high echo signal dynamics and to allow accurate distance measurement even based on single-laser-pulse echo detection. Such performance can be potentially achieved using pulsed time-of-flight (ToF) laser rangefinders (LRF). In conventional ToF modules, however, the STOP signal (for time counter interruption) is generated using a straightforward fixed-threshold comparator method. Unfortunately, it corresponds to the so-called walk error, i.e., the dependence of the measured time of flight on the magnitude of the echo signal. In most ranging applications, however, the LRF detection channel can be exposed to an extremely large span of received echo power levels, which depend on the distance measured, type of target surface, atmospheric transmission, etc. Thus, the walk error is an inseparable element of the conventional ToF technique and creates a fundamental limit for its precision. This article presents a novel method of walk error compensation in real time. By using our authorial electronic circuit for measuring the magnitude of the echo signal, it is possible to effectively compensate for the walk error even when the echo signal brings the detection channel amplifiers into saturation. In addition, the paper presents a laboratory method for calibrating the walk error compensation curve.

A feature extraction and recognition method for interrupted sampling repeater jamming

In order to achieve anti-jamming, radar needs to identify the jamming signals to distinguish between the target echo signals and the jamming signals. Interrupted sampling repeater jamming (ISRJ) is a widely used jamming type in radar and electronic countermeasures. In this paper, we propose an efficient channel attention (ECA)-based one-dimensional residual neural network method for the ISRJ signal recognition. The joint features based on axially integral bispectrum and square integral bispectrum are used to extract the target echo signal and ISRJ signal. The ECA-based one-dimensional residual neural network is applied to the ISRJ recognition. The experiment results show that the proposed ECA-based one-dimensional residual neural network has better correct recognition rate under low signal-to-noise ratio and can offer 30.17% reduction in timeliness than the improved convolutional neural network-residual network (CNN-ResNet) method.

ПРИНЦИП СУБЪЕКТИВНОЙ ИЗБЫТОЧНОСТИ И МЕТОД СЖАТИЯ ДАННЫХ, ПОЛУЧАЕМЫХ ОТ ЦИФРОВЫХ АНТЕННЫХ РЕШЕТОК

The problem of limiting the capacity of the diagram-forming circuit of the digital antenna array is considered, due to the capabilities of existing communication lines with serial data transmission from the receiving channels to the computer. This problem limits the possibilities of implementing various methods of signal processing in digital antenna arrays. The subject of the study is the possibility of increasing the capacity of the diagram-forming circuit of the digital antenna array due to software compression of radar data. The purpose of the work is to create a scientific and methodological apparatus that will increase the capcity of the diagram-forming scheme by using the principle of subjective redundancy in relation to radar data processing tasks. The results of the work include: justification and formulation of the principle of subjective redundancy of radar data and the method of compression of radar data, as well as the results of numerical studies confirming the operability of the proposed scientific and methodological apparatus. The principle of subjective redundancy is based on the independence of the separation of signal processing processes in the spatial and temporal domains when receiving a single echo signal, and in the case of receiving several echo signals, on the principle of superposition of fields from various sources in the far antenna zone. The method of radar data compression is based on the principle of subjective redundancy of radar data and differs from the known iterative procedure for approximating the matrix of complex samples in the form of a superposition of products of one-dimensional cosine spectra. It is shown that the application of the radar data compression method makes it possible to increase the transmission capacity of the digital antenna array transmission lines several times.

Scanning‐Driven Photon‐Counting 3D Imaging through Scattering Media Via Asynchronous Polarization Modulation

AbstractImaging through scattering media is widely studied in various applications including industrial inspection and autonomous driving. Most existing image sensors always suffer from the aliasing effect that generates a diffused image, leading to an extremely challenging problem in recovering targets of high quality, especially for 3D imaging. In this study, a scanning‐driven photon‐counting 3D imaging system through scattering media is proposed via asynchronous polarization modulation. To eliminate the aliasing effect, we utilize a point‐to‐point scanning strategy is used to illuminate the target and a photon‐counting detector to perceive weak echo signals passing through scattering media. Then, an asynchronous polarization‐modulated ranging method is proposed innovatively based on the Gamma pulse model to calculate the relative depth of the target for 3D imaging with the consideration of scattering media's difference. Therefore, the accuracy of the calculated depth and the generalization capability of the proposed method in real applications can be significantly improved. In addition, a time reshaping method is proposed to cope with the depth ambiguity caused by asynchronous measurements. In the experiment, a variety of strong scattering media is used to verify the excellent imaging capability of the proposed method.

Lidar Research during the First Stage of the 89th Cruise of the R/V “Academic Mstislav Keldysh”

A lidar survey of the western part of the Kara Sea was carried out in September 2022. The shipborne radiometric (profiling) lidar PLD-1 was used. The lidar optical unit was located on the 8th deck of the R/V ‘Akademik Mstislav Keldysh’ at an altitude of 15 m above the water surface. Lidar sounding was carried out at stations and underway. The vessel route passed through water areas characterized by a wide range of changes in hydrooptical characteristics. Lidar measurements were accompanied by synchronized measurements of hydrooptical and hydrological characteristics. These measurements were carried out using submersible instruments at stations and using a flow-through measuring complex along the ship's route. The hydrooptical characteristics vertical distribution uniformity in the upper ten-meter layer was controlled remotely using underway lidar data.Good agreement between the spatial distributions of the lidar echo signals parameters, of the hydrooptical and of the hydrological characteristics (coincidence of the locations of various distribution features, local maxima, minima and frontal zones) was demonstrated. A large volume of measurement data has been obtained, which allows for further statistical processing in order to find relationships between the parameters of lidar echo signals and hydrooptical characteristics recorded by contact methods.

Ultrasound signal processing based on joint GWO-VMD wavelet threshold functions

To overcome the problem of noise interference in the ultrasonic echo signal received by the probe in ultrasonic nondestructive testing, a method of ultrasonic signal processing using the jointed wavelet threshold function of GWO-VMD (Grey Wolf Optimisation, Variational Mode Decomposition) is proposed. The method first uses the GWO algorithm to optimise the VMD parameters to find the optimal parameter combination penalty factor α and decomposition modulus number K, and then uses the correlation coefficient method to differentiate between the desired signal and noise components after decomposition to obtain the intrinsic mode function (IMF) components. Secondly, the noise components are processed by combining the improved wavelet threshold function algorithm, and lastly, each modal component is restored to produce a denoised signal. Experimental results show that the method improves the signal-to-noise ratio (SNR) by 2.55%, reduces the root mean square error (RMSE) by 3.8%, preserves more useful information, and is more effective in denoising.

Material Classification based on Non-contact Ultrasonic Echo Signal Using Deep Learning Approach

Non-contact ultrasonic sensors are generally used for range measurement and object detection. In addition to the shape and size of the target object, the identification of the material types plays a vital role in robotic navigation and autonomous vehicle applications. Ultrasonic echo signals contain a significant amount of information and can be used to recognize and categorize different materials. Echo signals can help robots detect the obstacle's material on the path and comply with its behavior accordingly. The emergence of deep neural networks has shown great promise, offering cutting-edge performance for a wide range of signal-processing tasks. This work uses the non-contact ultrasonic echo signal from a set of materials (glass, wood, metal plate, sponge, and cloth) for classification. The main idea is to classify the materials using the embedded information of reflected echo signals. Hilbert transform is used to get the envelope from the raw echo ultrasonic signal. In this paper, a novel architecture for one-dimensional convolutional neural network (1D-CNN) has been proposed to accomplish the classification task. The CNN model takes raw echo signals as input to detect and classify materials accurately. The performance of the classifier model is evaluated using accuracy, precision, recall, and F1-score. The proposed 1D-CNN deep learning-based multi-class classifier model can classify the different types of materials with an accuracy of 96%, precision of 95%, recall of 95%, and F1-score of 95%.

Fast sampling based image reconstruction algorithm for sheared-beam imaging

Sheared-beam imaging (SBI) is an unconventional ground-based optical imaging technique. It breaks through the traditional optical imaging concept by using three coherent laser beams, which are laterally displaced at the transmit plane, to illuminate the target, reconstructing the target image from echo signals. However, the echo data sampling of the imaging system is still not fast enough to reconstruct the high resolution and clear image of the target when imaging the target that is at rapidly changing position and attitude. In order to solve this problem, in this work an image reconstruction method is proposed based on five-beam fast sampling. An emitted beam array arranged in the cross shape with a central symmetrical structure is proposed, and the encoding and decoding method of the imaging system are changed. With a single exposure, the echo signals carry more spectrum information of the target, and the number of reconstructed images can be increased from 1 to 8, which quickly suppresses the speckle effect of the reconstructed image. Firstly, the principle of the imaging technique based on fast sampling is presented. Then, an image reconstruction algorithm based on fast sampling is studied. Eight groups of phase differences and amplitude information of the target can be extracted from echo signals. The wavefront phases are solved by the least-squares method, and wavefront amplitude can be obtained by the algebraic operation of speckle amplitude. The target image is reconstructed by the inverse Fourier transform. The simulation results show that comparing with the traditional three-beam image reconstruction method, the sampling times of echo data needed to obtain the same quality image are reduced from 20 to 5 , which greatly reduces the sampling times of echo data and improves the sampling rate of echo data.

Anti-scattering signal processing method of VMD-ICA based underwater lidar

This paper proposes an underwater signal processing method based on the combination of variational mode decomposition (VMD) and independent component analysis (ICA). In this method, the VMD is used to decompose a set of echo signals into groups of eigenmodal signals arranged according to the order of their frequencies. These modal signals are then used as an observation matrix for ICA to ensure the integrity of the separated target echo signals. In this method, the correlation between the decomposed modal signals and the original signal is used to select the signals which are used as input matrix rows for ICA. The signal-to-clutter ratio is used to determine the number of decomposed layers. The ICA is used to separate the scattering and target echoes, so as to recover the weak target echoes in the strongly scattered water and greatly improve the ranging accuracy. A 532 nm intensity-modulated continuous wave (CW) laser is used in the underwater ranging experiments. The attenuation coefficient of the water is changed by adding Mg(OH)<sub>2</sub>, ranging experiments are carried out at different attenuation lengths. The experimental results show that the signal processing method can be used to successfully measure the target within 9 AL (attenuation length) when the laser output power is 2.3 W, and the ranging accuracy is improved from 16 cm to less than 5 cm by using the algorithm. The proposed method can be applied to underwater lidar application in turbid water and long distance ranging underwater where the scattering dominates the echoes.

RadarFormer: End-to-End Human Perception With Through-Wall Radar and Transformers.

For fine-grained human perception tasks such as pose estimation and activity recognition, radar-based sensors show advantages over optical cameras in low-visibility, privacy-aware, and wall-occlusive environments. Radar transmits radio frequency signals to irradiate the target of interest and store the target information in the echo signals. One common approach is to transform the echoes into radar images and extract the features with convolutional neural networks. This article introduces RadarFormer, the first method that introduces the self-attention (SA) mechanism to perform human perception tasks directly from radar echoes. It bypasses the imaging algorithm and realizes end-to-end signal processing. Specifically, we give constructive proof that processing radar echoes using the SA mechanism is at least as expressive as processing radar images using the convolutional layer. On this foundation, we design RadarFormer, which is a Transformer-like model to process radar signals. It benefits from the fast-/slow-time SA mechanism considering the physical characteristics of radar signals. RadarFormer extracts human representations from radar echoes and handles various downstream human perception tasks. The experimental results demonstrate that our method outperforms the state-of-the-art radar-based methods both in performance and computational cost and obtains accurate human perception results even in dark and occlusive environments.

Study of off-axis incident rotational speed measurement based on coherent synthetic vortex beams

Vortex beam (VB) is a structured light beam with a helical wavefront and carrying orbital angular momentum (OAM). Compared with Gaussian beam, the VB possesses the rotational Doppler effect (RDE), which is anticipated to compensate for the shortcoming of traditional detection methods in the spin motion of the target object. However, in practical applications, the rotational speed measurement technology based on the VB is facing some challenges, such as weak echo signal intensity due to low vortex beam light power and OAM spectrum expansion caused by off-axis incidence of the vortex beam. These above-mentioned problems directly limit the accuracy and application range of rotational speed measurement. To expand the application range of detection scheme based on the VB, we study the measurement scheme of the target rotational speed based on the combined vortex beam (CVB), which is on the basis of the experimental device for rotational speed measurement with CVB generated by fibre laser arrays. Firstly, the OAM spectra of the off-axis incidence situation are simulated. According to the simulation results, we derive a general model of the peak distribution of echo signals under the off-axis incidence, and propose a rotational speed measurement scheme based on the frequency interval between adjacent spectral peaks. Secondly, we carry out the target rotational speed measurement experiment in off-axis incidence case, and the difference in frequency between two adjacent spectral peaks is obtained from the spectrum map of the echo signal to measure the rotational speed of the target object. The results show that the target rotational speed can be accurately measured regardless of the lateral displacement and angular deflection in the case of off-axis incidence, which confirms the validity of the universal model for rotational speed measurement. The rotational speed measurement scheme proposed in this study takes into consideration the off-axis incidence prevalent in practical application, thereby improving the applicability in the target object rotational speed measurement, and providing technical reference for remote sensing detection application based on the VB.

Proposal of dental demineralization diagnosis with OCT echo based on multiscale entropy analysis.

Optical coherence tomography (OCT) has been widely used for the diagnosis of dental demineralization. Most methods rely on extracting optical features from OCT echoes for evaluation or diagnosis. However, due to the diversity of biological samples and the complexity of tissues, the separability and robustness of extracted optical features are inadequate, resulting in a low diagnostic efficiency. Given the widespread utilization of entropy analysis in examining signals from biological tissues, we introduce a dental demineralization diagnosis method using OCT echoes, employing multiscale entropy analysis. Three multiscale entropy analysis methods were used to extract features from the OCT one-dimensional echo signal of normal and demineralized teeth, and a probabilistic neural network (PNN) was used for dental demineralization diagnosis. By comparing diagnostic efficiency, diagnostic speed, and parameter optimization dependency, the multiscale dispersion entropy-PNN (MDE-PNN) method was found to have comprehensive advantages in dental demineralization diagnosis with a diagnostic efficiency of 0.9397. Compared with optical feature-based dental demineralization diagnosis methods, the entropy features-based analysis had better feature separability and higher diagnostic efficiency, and showed its potential in dental demineralization diagnosis with OCT.