Coherent Signals Research Articles

Direction of Arrival Estimation Method Based on Eigenvalues and Eigenvectors for Coherent Signals in Impulsive Noise

In this paper, a Toeplitz construction method based on eigenvalues and eigenvectors is proposed to combine with traditional denoising algorithms, including fractional low-order moment (FLOM), phased fractional low-order moment (PFLOM), and correntropy-based correlation (CRCO) methods. It can improve the direction of arrival (DOA) estimation of signals in impulsive noise. Firstly, the algorithm performs eigenvalue decomposition on the received covariance matrix to obtain eigenvectors and eigenvalues, and then the Toeplitz matrix is created according to the eigenvectors corresponding to its eigenvalues. Secondly, the spatial averaging method is used to obtain an unbiased estimate of the Toeplitz matrix, which is then weighted and added based on the corresponding eigenvalues. Next, the noise subspace of the Toeplitz matrix is reconstructed to obtain the one that has less angle information. Finally, the DOA of the coherent signal is estimated using the Multiple Signal Classification (MUSIC) algorithm. The improved method based on the Toeplitz matrix can not only suppress the effect of impulsive noise but can also solve the problem of aperture loss due to its decoherence. A series of simulations have shown that they have better performances than other algorithms.

Observations of spatial coherence from a multibeam subbottom profiler

Sonar data from the Kongsberg SBP-29 (with a 64-channel receive array) aboard R/V Sally Ride is analyzed to judge the value of spatial coherence of signals reflected from the ocean floor. The multibeam sub-bottom profiler collected five days of sonar test data off the coast of Southern California over a variety of sediment types and marine geologies. The spatial coherence of measured signals is sensitive to different bottom types and to sediment layering deep below the ocean floor. Navigational data also permits comparison of sonar data to existing bathymetric maps of the ensonified floor. The flexibility of the transmit and receive arrays permit detailed study of potentially informative seafloor features and landmarks. The findings of primary interest are interpreted in the context of a bivariate Normal surface fit to characterize the spatial coherence of scattered signals and future work is reported based on statistical analysis of the fitted data.

Tunnel environment noise suppression by SVD-DSW method

There is strong random noise in seismic records of tunnel seismic advance detection, which makes it difficult to obtain the underground lithology information in front of tunnel face accurately. Aiming at the problem that random noise is difficult to suppress in tunnel environment, a method of SVD-DSW is proposed in our research. Different from the directional seismic wave method, the proposed method is to stack the target signal after leveling. Then, the target signal with stronger coherence is extracted by singular value decomposition and target signal reconstruction, and finally reverse correct the extracted coherent signal to get the target signal. Simulation results and field data analysis show that the SVD-ICA theoretical approach is feasible for improving the SNR of the target signal in three-component seismogram, which is of great significance to increase detection range and resolution of tunnel seismic advance prediction.

Coherent control of enhanced second-harmonic generation in a plasmonic nanocircuit using a transition metal dichalcogenide monolayer

Nonlinear nanophotonic circuits, renowned for their compact form and integration capabilities, hold potential for advancing high-capacity optical signal processing. However, limited practicality arises from low nonlinear conversion efficiency. Transition metal dichalcogenides (TMDs) could present a promising avenue to address this challenge, given their superior optical nonlinear characteristics and compatibility with diverse device platforms. Nevertheless, this potential remains largely unexplored, with current endeavors predominantly focusing on the demonstration of TMDs’ coherent nonlinear signals via free-space excitation and collection. In this work, we perform direct integration of TMDs onto a plasmonic nanocircuitry. By controlling the polarization angle of the input laser, we show selective routing of second-harmonic generation (SHG) signals from a MoSe2 monolayer within the plasmonic circuit. Routing extinction ratios of 14.86 dB are achieved, demonstrating good coherence preservation in this hybrid nanocircuit. Additionally, our characterization indicates that the integration of TMDs leads to a 13.8-fold SHG enhancement, compared with the pristine nonlinear plasmonic nanocircuitry. These distinct features—efficient SHG generation, coupling, and controllable routing—suggest that our hybrid TMD-plasmonic nanocircuitry could find immediate applications including on-chip optical frequency conversion, selective routing, switching, logic operations, as well as quantum operations.

Improving a method for non-coherent processing of signals by a network of two small-sized radars for detecting a stealth unmanned aerial vehicle

The object of this study is the process of detecting stealth unmanned aerial vehicles by a network of two small-sized radars with incoherent signal processing. The main hypothesis of the study assumed that combining two small-sized radars into a network could improve the quality of detection of stealth unmanned aerial vehicles with incoherent signal processing. The improved method for detecting a stealth unmanned aerial vehicle by a network of two small-sized radars with incoherent signal processing, unlike the known ones, provides for the following: – synchronous inspection of the airspace by small-sized radars; – sounding signal emission by each small-sized radar; – reception of echo signals from a stealth unmanned aerial vehicle by two small-sized radars; – coordinated filtering of incoming echo signals (separation of echo signals); – quadratic detection of signals at the outputs of matched filters; – summation of the detected signals at the outputs of the matched filters; – summation of the outputs of adders of two small-sized radars. The scheme of a stealth unmanned aerial vehicle detector is presented, optimal according to the Neumann-Pearson criterion, with incoherent signal processing. The quality of detection of a stealth unmanned aerial vehicle by a network of two small-sized radars with incoherent signal processing was evaluated. It was found that with incoherent processing, the gain in the value of the conditional probability of correct detection is on average from 19 % to 26 %, depending on the value of the signal-to-noise ratio. The gain in the value of the conditional probability of correct detection is greater at low values of the signal-to-noise ratio. At the same time, the gain in signal-to-noise value is more significant with coherent signal processing than with non-coherent signal processing by a network of two small-sized radars

Temperature and Thermal Diffusivity Diagnostics in Laminar Methane Flames Using Infrared Four-Wave Mixing Techniques.

Four-wave mixing techniques, such as coherent anti-Stokes Raman spectroscopy (CARS), laser-induced grating spectroscopy (LIGS), and degenerate four-wave mixing (DFWM), have been widely used in combustion diagnostics due to their advantages of high signal-to-noise ratio (S/N), coherent signal, and spatial resolution. In this work, a nano-second pulsed laser is utilized to generate mid-infrared (near 3 µm) pump beams, exciting the rovibrational transitions of nascent water in flames. Combined LIGS and DFWM measurements are demonstrated in premixed laminar CH4/O2/N2 flames with equivalence ratios from 0.6 to 1.5, to achieve precise thermometry in a wide range of flame conditions. The flame temperatures were also measured by thermocouple as a reference, and the results from LIGS and DFWM align well with the trends shown in the thermocouple measurements. In fuel-lean flames, where the mass-to-specific-heat ratio variation is minimal, LIGS provides temperature data with a precision better than 16 K (0.8%). In fuel-rich flames, where the increased H2 concentration in the flame introduces uncertainty in gas constants thus affecting the accuracy of LIGS thermometry, DFWM is instead employed for temperature measurement since it is less sensitive to the gas composition within the measured volume. The high-precision LIGS temperatures in lean flames serve as temperature reference during the DFWM calibration of the degree of saturation, and a precision better than 90 K (4.5%) is achieved for DFWM thermometry. In addition to temperature, a theoretical model is employed to fit LIGS signal time waveforms, extracting the local speed of sound and thermal diffusivity with precisions better than 0.5% and 1.3%, respectively. These high-precision measurements contribute additional data for flame research and simulation calculations. This way, the combined use of DFWM and LIGS proves the potential for accurate thermometry and diagnostics of other thermodynamic parameters across a wide range of flame conditions.

Dendroarchaeology of Birnirk and Thule Architectural Timbers (10–13th Centuries)

Abstract Along the coast of northwestern Alaska, architectural wood remains are well preserved in the Birnirk and Thule coastal sites of the early 2nd millennium CE. These structural wood elements are unique archives for documenting climatic variations and cultural transformations during this key development period of Inuit culture. Along this treeless Arctic coast, driftwood accumulates from the subarctic forests of interior Alaska. Except for northwestern Alaska, regional tree-ring chronologies are too short (at best 350–400 years) to successfully date archaeological wood remains from Birnirk and Thule coastal sites using conventional dendrochronology. This paper examines the potential of tree-ring derived δ18O signal to annually date eight architectural wood samples from the Rising Whale (KTZ-304) site at Cape Espenberg, northwestern Alaska. We developed a δ18O master chronology, covering the period 935–1157 CE, using five wood samples from the KTZ-304 site. Blind isotope cross-dating of individual series belonging to this δ18O master chronology (one against the other four) showed conclusive dating and a very strong coherence of the isotopic signal. We, then, used the δ18O master chronology to cross-date three other wood samples for which we knew, from previous 14C wiggle-matching procedure, the first measured ring to be in this time interval, within a ± 18 to 30-year precision. Oxygen isotope dendrochronology provided a plausible date for one of the samples (the first measured ring at 1073 CE). This preliminary study encourages us to acquire additional data to extend in time and strengthen the δ18O master chronology of northwestern Alaska (NWAK18O) and help refine our understanding of climate and culture change during the 2nd millennium CE.

Wood identification and tree-ring chronology building of oak pillars excavated from submerged prehistoric settlement (3000 BC), Ropotamo River estuary, Black Sea

Eighty one well-preserved wooden pillars were found аs a result of underwater archaeological excavations of a submerged prehistoric settlement in the estuary of Ropotamo River, Black Sea. That provokes a scientific interest to identify wood materials.  The longest tree-ring chronologies have been used to build floating dendrochronology series. Anatomical features of xylem showed that 34 out of the 81 studied wooden pillars belong to genus Quercus. The remaining 47 pillars belong to a species of the genera Fraxinus, Acer, Ulmus and Platanus.  It has been found that 8 out of 34 oak pillars have a length of tree-ring series over 30 years. This was the reason to select them for dendrochronological studies. The length of the built floating master chronology for the oaks is 84 years. The lack of strong coherent signal between 34 single oak dendrochronological series can be explained with three different types of oak forest ecosystems, in the area of the Ropotamo River estuary. These forest ecosystems are as follows: oak high stand forests in Strandzha Mountain, oak coppice forests on the Black Sea coast plains and riparian ‘Longoz’ forests of Ropotamo River. The tree-ring series of these forest types have different tree-ring signatures. Based on the tree-ring analysis it can be assumed that the timber of the above-mentioned three types of oak ecosystems was used in the construction of the prehistoric settlement.  

Theoretical Demonstration of Improved Performance in Multi-Channel Photonic RF Signals Based on Optical Injection Locking of Optical Comb and Array Lasers

Multi-channel radio frequency (RF) signal generation, facilitated by photonic technology, offers significant potential for generating coherent signals with a high frequency and low phase noise, providing multifunctional capabilities across diverse platforms, including RF and photonic systems. Traditional methods for multi-channel photonic RF signal generation typically entail the integration of diverse optical components, such as filters and amplifiers. However, this integration often results in compromises related to power efficiency, cost-effectiveness, and implementation simplicity. To address these challenges, we propose a novel method for generating multi-channel photonic RF signals based on optical injection locking technology. This approach eliminates the necessity for traditional optical components, leading to a substantial enhancement in the performance of photonic RF signals. We present the design of an optical injection locking-based multi-channel photonic RF signal generation schematic and theoretically evaluate its Signal-to-Noise Ratio (SNR) and eye pattern performance for data modulation using the Lumerical INTERCONNECT simulator. Our results reveal a significant 1.3-dB and 3.6-dB enhancement in SNR for 30-GHz and 60-GHz signals, respectively. Furthermore, we observed an improved communication performance, as evidenced by enhanced eye patterns in 3-Gbps data transmission compared to passive photonic RF signal generation methods.

Analysis of Cortico-Muscular Coupling and Functional Brain Network under Different Standing Balance Paradigms.

Maintaining standing balance is essential for people to engage in productive activities in daily life. However, the process of interaction between the cortex and the muscles during balance regulation is understudied. Four balance paradigms of different difficulty were designed by closing eyes and laying sponge pad under feet. Ten healthy subjects were recruited to stand for ten 15 s trials in each paradigm. This study used simultaneously acquired electroencephalography (EEG) and electromyography (EMG) to investigate changes in the human cortico-muscular coupling relationship and functional brain network characteristics during balance control. The coherence and causality of EEG and EMG signals were calculated by magnitude-squared coherence (MSC) and transfer entropy (TE). It was found that changes in balance strategies may lead to a shift in cortico-muscular coherence (CMC) from the beta band to the gamma band when the difficulty of balance increased. As subjects performed the four standing balance paradigms, the causality of the beta band and the gamma band was stronger in the descending neural pathway than that in the ascending neural pathway. A multi-rhythmic functional brain network with 19 EEG channels was constructed and analyzed based on graph theory, showing that its topology also changed with changes in balance difficulty. These results show an active adjustment of the sensorimotor system under different balance paradigms and provide new insights into the endogenous physiological mechanisms underlying the control of standing balance.

Improvement in Multi-Angle Plane Wave Image Quality Using Minimum Variance Beamforming with Adaptive Signal Coherence.

For ultrasound multi-angle plane wave (PW) imaging, the coherent PW compounding (CPWC) method provides limited image quality because of its conventional delay-and-sum beamforming. The delay-multiply-and-sum (DMAS) method is a coherence-based algorithm that improves image quality by introducing signal coherence among either receiving channels or PW transmit angles into the image output. The degree of signal coherence in DMAS is conventionally a global value for the entire image and thus the image resolution and contrast in the target region improves at the cost of speckle quality in the background region. In this study, the adaptive DMAS (ADMAS) is proposed such that the degree of signal coherence relies on the local characteristics of the image region to maintain the background speckle quality and the corresponding contrast-to-noise ratio (CNR). Subsequently, the ADMAS algorithm is further combined with minimum variance (MV) beamforming to increase the image resolution. The optimal MV estimation is determined to be in the direction of the PW transmit angle (Tx) for multi-angle PW imaging. Our results show that, using the PICMUS dataset, TxMV-ADMAS beamforming significantly improves the image quality compared with CPWC. When the p value is globally fixed to 2 as in conventional DMAS, though the main-lobe width and the image contrast in the experiments improve from 0.57 mm and 27.0 dB in CPWC, respectively, to 0.24 mm and 38.0 dB, the corresponding CNR decreases from 12.8 to 11.3 due to the degraded speckle quality. With the proposed ADMAS algorithm, however, the adaptive p value in DMAS beamforming helps to restore the CNR value to the same level of CPWC while the improvement in image resolution and contrast remains evident.

A data-driven methodology to design user-friendly tariffs in energy communities

In recent years, energy communities have emerged as a feasible solution to empower domestic end-users to engage in local power trading with their neighbours, in an attempt to improve the efficiency and economy of residential consumers. From a mercantilist point of view, launching local markets with eventual local electricity prices might be beneficial for community users as they are inhibited from external volatile prices and possible market imperfections. However, local pricing strategies should take into account users’ preferences and avoid undesirable effects of response fatigue (i.e. excessive number of response signals within a short-time period). This way, local electricity tariffs should be stable and send coherent response signals easily interpretable by users. In this sense, the necessity of developing proper designing tools for local electricity tariffs is clear. This paper focuses on this issue. In particular, the main novelties of this paper are twofold: on the one hand, the developed tool designs community tariffs over a year basis instead of daily spot prices, as made in existing approaches. Thereby, the resulting tariff keeps stable yearly similar to conventional tariffs offered by retailers worldwide. Secondly, the designed tariff takes into account the negative effects of response fatigue, so that the considered pricing mechanism limits the number of pricing signals sent to consumers, taking this feature as an external parameter. This way, the designer is able to tune up the total number of pricing signals that users received within a time period, thus ensuring that they are not discouraged to partake in the community. The proposed design approach is raised as a data-driven framework, taking advantage of real databases collecting demand, renewable generation and retailer prices. Such profiles serve as inputs for a designed bi-level Stackelberg-based problem, in which the reaction of prosumers is implicitly assumed. A case study is conducted on a benchmark energy community. Different tariff mechanisms are analysed such as flat, time-of-use and happy hours tariffs. The results obtained serve to validate the new proposal as well as analyse the effect of local market mechanisms in energy communities.

Signal Coherence and Water Detection Algorithms for the ESA HydroGNSS Mission

Signal Coherence and Water Detection Algorithms for the ESA HydroGNSS Mission

Mathematical model for calculating the uncertainty function of multi-frequency coherent probing signals for diagnosing shortwave radio channels

Mathematical model for calculating the uncertainty function of multi-frequency coherent probing signals for diagnosing shortwave radio channels

Direction-of-Arrival Estimation of Mixed Coherent and Uncorrelated Signals

Direction-of-Arrival Estimation of Mixed Coherent and Uncorrelated Signals

Robust Sparse Recovery Based Vehicles Location Estimation in Intelligent Transportation System

An architecture for vehicle position estimation is introduced in this paper to tackle the issue of vehicles positioning in traffic congestion of intelligent transportation system (ITS). The introduced architecture is related to three unmanned aerial vehicles (UAVs) equipped with uniform linear array (ULA), ITS center and the terminal of position estimation, in which the UAV collect data from the ULA, the ITS center store data and the terminal is responsible for executing the corresponding direction of arrival (DOA) estimation algorithm to estimate the vehicle position. In the introduced architecture, DOA estimation is the crucial issue, hence a robust sparse recovery framework based on the optimal weighted subspace fitting is put forward for DOA estimation in the presence of direction-dependent unknown mutual coupling. Firstly, a data model can be obtained by a new transformation approach. Then, a sparse recovery algorithm based on the weighted subspace fitting is used to estimate the desired DOAs. The proposed DOA estimation algorithm can achieve superior performance in both coherent and incoherent signals in the environment of direction-dependent mutual coupling, and this environment often occurs in traffic congestion. Based on the DOA estimation results obtained from the proposed efficient regularized sparse recovery algorithm, the position of vehicles is final estimated by a weighted three-points cross-positioning method. The results of various simulation experiments fully demonstrate the robustness and superiority of the proposed architecture and algorithm.

Sparsity-Based Adaptive Beamforming for Coherent Signals With Polarized Sensor Arrays

Sparsity-Based Adaptive Beamforming for Coherent Signals With Polarized Sensor Arrays

High-Accuracy DOA Estimation of Coherent Signals Based on the Time-Space Mutual Correlation Smoothing

High-Accuracy DOA Estimation of Coherent Signals Based on the Time-Space Mutual Correlation Smoothing

Train Axlebox Bearing Fault Diagnosis Based on MSC-SGMD.

Train axlebox bearings are subject to harsh service conditions, and the difficulty of diagnosing compound faults has brought greater challenges to the maintenance of high-quality train performance. In this paper, based on the traditional symplectic geometry mode decomposition (SGMD) algorithm, a maximum spectral coherence signal reconstruction algorithm is proposed to extract the intrinsic connection between the SGMD components with the help of the frequency domain coherence idea and reconstruct the key signal components so as to effectively improve the extraction of composite fault features of axlebox bearings under different speed conditions. Firstly, based on the traditional SGMD algorithm, the vibration signal of the axle box is decomposed to extract its symplectic geometry components (SGCs). Secondly, the spectral coherence coefficient between the SGCs is calculated, and the signal in which the maximum value is located is taken as the key component for the additive reconstruction Finally, the envelope spectrum is used to extract the reconstructed signal fault features. The inner race, outer race, and compound bearing failure vibration signal acquisition under different speed conditions were carried out on the equal scale axlebox bearing failure simulation test bench, and the effectiveness of the proposed algorithm was verified based on the axlebox vertical acceleration signal.

Coherent signals direction estimation using high-order quantities in the acoustic field

To meet the demands of underwater remote detection technology, the receiving array must be extended to low frequencies by increasing the array aperture required for detection. Furthermore, the base array aperture must be increased owing to the loss of effective array aperture in coherent targets detection using most decoherent algorithms; however, this prevents its use on small platforms, such as autonomous underwater platforms. Thus, reducing the array aperture while ensuring high resolution is required. In this article, high-order quantities in the acoustic field are utilized to construct a full rank covariance matrix to Multiple Signal Classification Method (MUSIC), which achieves high-resolution results for small aperture arrays with small incident angular intervals in low-frequency coherent targets. The correlated noise of each sensor is considered in the small aperture arrays and the gain of each element in the new matrix is derived. The theoretical analysis, numerical simulations, and experimental verifications show the validity of the method.