Doppler Shift Research Articles

Rydberg-atom-based radio-frequency sensors: amplitude-regime sensing

Rydberg atom-based radio frequency electromagnetic field sensors are drawing wide-spread interest because of their unique properties, such as small size, dielectric construction, and self-calibration. These photonic sensors use lasers to prepare atoms and read out the atomic response to a radio frequency electromagnetic field based on electromagnetically induced transparency, or related phenomena. Much of the theoretical work has focused on the Autler-Townes splitting induced by the radio frequency wave. The amplitude regime, where the change in transmission observed on resonance is measured to determine electric field strength, has received less attention. In this paper, we deliver analytic expressions that are useful for calculating the absorption coefficient in the amplitude regime. Our main goal is to describe the analytic expressions for the absorption coefficient and demonstrate their validity over a large range of the interesting parameter space. The effect of the thermal motion of the atoms is explicitly addressed. The analytic formulas for the absorption coefficient for different types of Doppler broadening are compared to estimate the sensitivity under conditions where it is limited by the laser shot noise. Residual Doppler shifts are shown to limit sensitivity. The expressions, approximations and descriptions presented in the paper are important for understanding the absorption of Rydberg atom-based sensors in the amplitude regime. This provides insight into the physics of multi-level interference phenomena.

A Hybrid Integration Method Based on SMC-PHD-TBD for Multiple High-Speed and Highly Maneuverable Targets in Ubiquitous Radar

Based on the characteristic of ubiquitous radar emitting low-gain wide beam, a method of long-time coherent integration (LTCI) is required to enhance target detection capability. However, high-speed and highly maneuverable targets can cause Doppler frequency migration (DFM), range migration (RM), and velocity ambiguity (VA), severely degrading the performance of LTCI. Additionally, the number of targets is unknown and variable, and the presence of clutter further complicates the target tracking problem. To address these challenges, we propose a hybrid integration method to achieve joint detection and estimation of multiple high-speed, and highly maneuverable targets. Firstly, we compensate for first-order RM using the keystone transform (KT) and generate corresponding sub-range-Doppler (SRD) planes with different folding factors to achieve VA compensation. These SRD planes are then stitched together to form an extended range-Doppler (ERD) plane, which covers a broader velocity range. Secondly, during the track-before-detect (TBD) process, tracking is performed directly on the ERD plane. We use the sequential Monte Carlo (SMC) approximation of the probability hypothesis density (PHD) to propagate multi-target states. Additionally, we propose an amplitude-based adaptive prior distribution method and a line spread model (LSM) observation model to compensate for DFM. Since the acceleration of the target is included in the particle state, using particles to search for DFM does not increase the computational load. To address the issue of misclassifying mirror targets as real targets in the SRD plane, we propose a particle space projection method. By stacking the SRD planes to create a folding range-Doppler (FRD) space, particles are projected along the folding factor dimension, and then, the particles are clustered to eliminate the influence of the mirror targets. Finally, through simulation experiments, the superiority of the LSM for targets with acceleration was demonstrated. In comparative experiments, the proposed method showed superior performance and robustness compared to traditional methods, achieving a balance between performance and computational efficiency. Furthermore, the proposed method’s capability to detect and track multiple high-speed and highly maneuverable targets was validated using actual data from a ubiquitous radar system.

E-BDL: Enhanced Band-Dependent Learning Framework for Augmented Radar Sensing.

Radar sensors, leveraging the Doppler effect, enable the nonintrusive capture of kinetic and physiological motions while preserving privacy. Deep learning (DL) facilitates radar sensing for healthcare applications such as gait recognition and vital-sign measurement. However, band-dependent patterns, indicating variations in patterns and power scales associated with frequencies in time-frequency representation (TFR), challenge radar sensing applications using DL. Frequency-dependent characteristics and features with lower power scales may be overlooked during representation learning. This paper proposes an Enhanced Band-Dependent Learning framework (E-BDL) comprising an adaptive sub-band filtering module, a representation learning module, and a sub-view contrastive module to fully detect band-dependent features in sub-frequency bands and leverage them for classification. Experimental validation is conducted on two radar datasets, including gait abnormality recognition for Alzheimer's disease (AD) and AD-related dementia (ADRD) risk evaluation and vital-sign monitoring for hemodynamics scenario classification. For hemodynamics scenario classification, E-BDL-ResNet achieves competitive performance in overall accuracy and class-wise evaluations compared to recent methods. For ADRD risk evaluation, the results demonstrate E-BDL-ResNet's superior performance across all candidate models, highlighting its potential as a clinical tool. E-BDL effectively detects salient sub-bands in TFRs, enhancing representation learning and improving the performance and interpretability of DL-based models.

A PLL-Based Doppler Method Using an SDR-Receiver for Investigation of Seismogenic and Man-Made Disturbances in the Ionosphere

The article describes in detail the equipment and method for measuring the Doppler frequency shift (DFS) on an inclined radio path, based on the principle of the phase-locked loop using an SDR receiver for the investigation of seismogenic and man-made disturbances in the ionosphere. During the two M7.8 earthquakes in Nepal (25 April 2015) and Turkey (6 February 2023), a Doppler ionosonde detected co-seismic and pre-seismic effects in the ionosphere, the appearances of which are connected with the various propagation mechanisms of seismogenic disturbance from the lithosphere up to the ionosphere. One day before the earthquake in Nepal and 90 min prior to the main shock, an increase in the intensity of Doppler bursts was detected, which reflected the disturbance of the ionosphere. A channel of geophysical interaction in the system of lithosphere–atmosphere–ionosphere coupling was traced based on the comprehensive monitoring of the DFS of the ionospheric signal, as well as of the flux of gamma rays in subsoil layers of rocks and in the ground-level atmosphere. The concept of lithosphere–atmosphere–ionosphere coupling, where the key role is assigned to ionization of the atmospheric boundary layer, was confirmed by a retrospective analysis of the DFS records of an ionospheric signal made during underground nuclear explosions at the Semipalatinsk test site. A simple formula for reconstructing the velocity profile of the acoustic pulse from a Dopplerogram was obtained, which depends on only two parameters, one of which is the dimension of length and the other the dimension of time. The reconstructed profiles of the acoustic pulses from the two underground nuclear explosions, which reached the height of the reflection point of the sounding radio wave, are presented.

Architecting CubeSat constellations for messaging service, Part I

In today’s modern and globalized world, connectivity is a key factor for businesses, production facilities, sensor networks, and ordinary people. However, there are still populated areas which are not covered by ground-based telecommunications infrastructure. This is where telecommunication satellite constellations come in, as they can provide coverage to remote and uninhabited regions and fill existing connectivity gaps to ensure data transfer.LoRa is one of the technologies designed for data transmissions over long distances with low power consumption. Alongside with other technologies of the Low-Power Wide Area Network family, it is widely used for Internet of Things applications. LoRa chirp spread spectrum modulation is robust against the Doppler frequency shifts encountered in low earth orbits, and it has already been used in IoT satellite communications. Due to the low transmitted signal power, the achieved data rate is not high, making it a suitable technology for telecommunications payloads on CubeSat platforms for messaging services. As compared to existing traditional communication satellite systems, CubeSat constellations are low-cost and may offer an affordable connectivity service to developing regions.This study is divided in two parts. In Part I the demand model is built based on the population distribution not covered by cell towers. The LoRa link performance is analyzed, considering the impact of LoRa channel parameters variation, such as spreading factor and channel bandwidth, while satellite orbital height, transmission antenna beamwidth, and transmitter peak power have a direct impact on the payload mass. Among thousands of possible configurations, 73 feasible payload designs have been downselected. In Part II of the study, the satellite mass and the total system cost are estimated based on the payload parameters obtained. Messages transmission simulation via a constellation is conducted in order to identify optimal constellation architectures for messaging service, as well as the main drivers of the system economic profitability.The presented analysis results provide a deeper understanding of LoRa connectivity advantages and limitations together with the performance drivers, which will support the optimization of future LoRa-based satellite communication systems and other IoT satellite constellations.

Design and Implementation of Software-Defined Receiver

Acquisition in software-defined GPS receivers is computationally intensive and time-consuming, especially with the increasing demand for rapid and accurate positioning services driven by globalization and digitization. Software-defined receivers face challenges in satellite signal acquisition, which becomes more demanding with longer signals. This paper aims to enhance the efficiency of GPS signal acquisition, a critical step for determining the code phase of PRN codes and the doppler shift in carrier frequency. Real-time applications encounter issues under dynamic conditions and degraded signals, as conventional computers lack the computational power for the necessary correlation and FFT operations. To address these challenges, a method using GPU acceleration for L1 signal acquisition is proposed. This method combines signal acquisition with GPU parallel computing using the SIMT model. A CPU-GPU platform via CUDA programming allows the CPU to handle data reading and intermediate processing, while the GPU performs the core acquisition algorithm in parallel.

Photon‐Counting 3D Velocimetry Empowered by OAM‐Based Multi‐Point Doppler Effect

AbstractVelocimetry of a motion target within 3‐D space is highly desirable in numerous applicable realms, ranging from explosion and shock wave physics, aerospace engineering to astronomical surveys. However, it is challenging to achieve synchronous, real‐time, and photon‐counting 3‐D velocimetry in modern frameworks as they either require separate multi‐directional detections, and cumbersome calculation processes or are confined to achieve in situ measurements. Here, a new conceptual paradigm is proposed to circumvent these constraints using orbital‐angular‐momentum (OAM)‐driven multi‐point Doppler effect at the photon‐counting level. This scheme, emanating from a single‐direction launch of an on‐demand engineered sequence OAM light mode onto a motion surface, enables simultaneous and independent detections of time‐varying Doppler photon‐count events from three orthogonal echo light paths. Concretely, at the range of motion velocity of 0.25–0.5 ms−1, the relative measurement errors of this proof‐of‐principle prototype are below 1.5%, thus achieving high‐accuracy 3‐D velocimetry at the photon‐counting level for the first time. The exploration of the OAM‐photon‐counting 3‐D velocimetry techniques provides unprecedented advantages in potential applications of synchronous, real‐time, high‐efficiency, and long‐range quantum lidar.

Wind comparisons between meteor radar and Doppler shifts in airglow emissions using field-widened Michelson interferometers

Wind comparisons between meteor radar and Doppler shifts in airglow emissions using field-widened Michelson interferometers

Distributional loss for convolutional neural network regression and application to parameter estimation in satellite navigation signals

Convolutional Neural Network (CNN) have been widely used in image classification. Over the years, they have also benefited from various enhancements and they are now considered state-of-the-art techniques for image-like data. However, when they are used for regression to estimate some function value from images, few recommendations are available to construct robust CNN regressor models. In this study, a robustness enforcing mechanism is proposed for CNN regression models. It combines convolutional neural layers to extract high level features representations from images with a soft labelling technique that helps generalization performance. More specifically, as the deep regression task is challenging, the idea is to account for some uncertainty in the targets that are seen as distributions around their mean. Building from earlier work (Imani and White, 2018), a specific histogram loss function based on the Kullback-Leibler (KL) divergence is applied during training. The prior distributions are selected according to the physical characteristics of the parameters to estimate. To assess and illustrate the technique, the model is applied to Global Navigation Satellite System (GNSS) multipath estimation where multipath signal parameters have to be estimated from correlator output images from the I and Q channels. The multipath signal delay, magnitude, Doppler shift frequency and phase parameters are estimated from synthetically generated datasets of satellite signals. Experiments are conducted under various receiving conditions and various input images resolutions to test the estimation performances quality and robustness. The results show that the proposed soft labelling CNN technique using distributional loss outperforms classical CNN regression under all conditions. Furthermore, the extra learning performance achieved by the model allows the reduction of input image resolution from 80x80 down to 40x40 or sometimes 20x20.

SupSpec: A Python Package for Measuring the Doppler Shifts of Supernova Spectral Lines

Here we present an open-source Python package, SupSpec, which aids in finding the velocity structure of spectral features throughout supernova (SN) ejecta. This code is designed to require minimal user interaction and can ingest an arbitrarily large number of spectra. By fitting a Gaussian distribution to the spectral sequence of a single SN over time, it finds the “velocity structure”—the evolution of the velocity of a specific absorption feature over time. SupSpec is available on GitHub and on Zenodo.

Super-resolution delay-Doppler estimation for OTFS-based automotive radar

In this paper, we consider the problem of joint delay-Doppler estimation in an automotive radar based on orthogonal time frequency space (OTFS) modulation. We propose a super-resolution estimation approach that operates in the continuous domain and employs a two-dimensional atomic norm carrying delay and Doppler shift information to mitigate the off grid errors. The estimation task of target parameters is reformulated as a semidefinite program (SDP) problem. To solve this problem in an efficient way, we leverage the alternating direction method of multipliers (ADMM) and adopt an iterative procedure to obtain the optimal solution. The effectiveness of the proposed method is confirmed by theoretical analyses. Numerical results are presented to demonstrate the superior performance of the proposed method in comparison with the state-of-the-arts.

Velocity selective multiple two-photon dark and bright resonances in Potassium vapor

We report the observation of two additional sub-natural line width quantum interferences in the D 2 manifold of 39 K vapor, in addition to the usual single Electromagnetically Induced Transparency (EIT) peak. In a typical three level Λ-type system, only one EIT peak is observed. However, here we report observation of two additional line shapes riding on top of the absorption profile. The fact that the hyperfine splitting is smaller than the Doppler width in 39 K allows the probe and control beams to swap their transition pathways in different velocity groups of atoms even when their frequencies are kept constant. Our observations are in striking contrast to standard EIT measurements. These findings are in quantitative agreement with density matrix formalism taking into account velocity-selective two-photon resonances. Owing to the favorably low ground hyperfine splitting (Δ hf ) in 39 K, which allows a significantly large number of atoms with a Doppler shift greater than or equal to the Δ hf , the strength of these additional resonances is strong compared to that of other alkali atoms such as 87 Rb, 133 Cs where these resonances can not be observed. The control photon detuning to atomic transition captures the nature of the coherence; therefore an unusual phenomenon of conversion from perfect transparency to enhanced absorption of the probe photon is observed and explained by utilizing the adiabatic elimination of the excited state in the Master equation. Controlling such dark and bright resonances leads to new applications in quantum technologies such as frequency-offset laser stabilization and long-lived quantum memory.

Doppler-enhanced quantum magnetometry with thermal Rydberg atoms

We report experimental measurements showing how one can combine quantum interference and thermal Doppler shifts at room temperature to detect weak magnetic fields. We pump 87 Rb atoms to a highly-excited, Rydberg level using a probe and a coupling laser, leading to narrow transmission peaks of the probe due to destructive interference of transition amplitudes, known as Electromagnetically Induced Transparency. While it is customary in such setups to use counterpropagating lasers to minimize the effect of Doppler shifts, here we show, on the contrary, that one can harness Doppler shifts in a copropagating arrangement to produce an enhanced response to a magnetic field. In particular, we demonstrate an order-of-magnitude bigger splitting in the transmission spectrum as compared to the counterpropagating case. We explain and generalize our findings with theoretical modeling and simulations based on a Lindblad master equation. Our results pave the way to using quantum effects for magnetometry in readily deployable room-temperature platforms.

Observational Evidence for Hot Wind Impact on Parsec Scales in Low-luminosity Active Galactic Nuclei

Supermassive black holes in galaxies spend the majority of their lifetime in the low-luminosity regime, powered by hot accretion flow. Strong winds launched from the hot accretion flow have the potential to play an important role in active galactic nuclei (AGN) feedback. Direct observational evidence for these hot winds with temperatures around 10 keV, has been obtained through the detection of highly ionized iron emission lines with Doppler shifts in two prototypical low-luminosity AGNs, namely M81* and NGC 7213. In this work, we further identify blueshifted H-like O/Ne emission lines in the soft X-ray spectra of these two sources. These lines are interpreted to be associated with additional outflowing components possessing velocity around several 103 km s−1 and lower temperature (∼0.2–0.4 keV). Blueshifted velocity and the X-ray intensity of these additional outflowing components are hard to explain by previously detected hot wind freely propagating to larger radii. Through detailed numerical simulations, we find the newly detected blueshifted emission lines would come from circumnuclear gas shock-heated by the hot wind instead. Hot wind can provide a larger ram pressure force on the clumpy circumnuclear gas than the gravitational force from the central black hole, effectively impeding the black hole accretion of gas. Our results provide strong evidence for the energy and momentum feedback by the hot AGN wind.

Extreme-ultraviolet (EUV) observables of simulated plasmoid-mediated reconnection in the solar corona

Context. Understanding the role of magnetic reconnection in the heating and dynamics of the solar atmosphere requires detailed observational data of any observable aspect of the reconnection process, including small-scale features such as plasmoids. Aims. Here, we examine the capability of active and upcoming instruments to detect plasmoids generated by reconnection in the corona including low-density regimes. Methods. We used the Bifrost code to perform simulations of plasmoid-mediated reconnection in the corona with a 2D idealized setup: a fan-spine topology with uniform density including thermal conduction. Through a forward-modeling of extreme-ultraviolet (EUV) observables, we checked whether our simulated plasmoids could be detected with the instruments of Solar Dynamics Observatory (SDO) and Solar Orbiter (SO), as well as the upcoming Multi-Slit Solar Explorer (MUSE) and Solar-C missions. Results. Short-lived (∼10 − 20 s) small-scale (∼0.2 − 0.5 Mm) coronal plasmoids are not resolvable with the Atmospheric Imaging Assembly (AIA) on board SDO. In contrast, they could be captured with the EUV High-Resolution Imager at the Extreme Ultraviolet Imager (EUI-HRIEUV) of SO. The spatial and temporal high-resolution planned for the MUSE spectrograph (SG) is adequate to obtain full spectral information of these plasmoids. To achieve a sufficient signal-to-noise ratio (S/N) for ∼0.8 MK plasmoids in the MUSE/SG 171 Å channel, full-raster images are attainable for regions with electron densities above 109 cm−3, while sit-and-stare observations are recommended for lower-density regions. The future Solar-C mission could also capture these coronal plasmoids using the EUV High-Throughput Spectroscopic Telescope (EUVST), considering rapid changes in Doppler shift and line widths in different EUV lines caused by plasmoid motions along the current sheet. Conclusions. With the combined spectra of MUSE/SG and Solar-C/EUVST in multiple emission lines, along with high-resolution images from SO/EUI-HRIEUV and MUSE/CI, it should be possible to gain new insights about plasmoid formation in the corona.

Deep Reinforcement Learning Assisted Hybrid Precoding for V2I Communications With Doppler Shift and Delay Spread

Deep Reinforcement Learning Assisted Hybrid Precoding for V2I Communications With Doppler Shift and Delay Spread

Ion temperature and rotation velocity measurements of carbon and boron ions using VUV spectroscopy on EAST.

A space-resolved vacuum ultraviolet spectroscopy is employed to measure impurity emission profiles (500-3200Å) on EAST. This study successfully captures C IV (1548.20 and 1550.77Å) lines emitted from carbon ions and derives ion temperatures using Doppler broadening and a collision model based on their intensity ratios. Both the emission intensity and ion temperature profiles are determined. However, the calculated results reveal a lower temperature of around 10-20eV with the collision model, suggesting a potential need for further correction in subsequent calculations. Furthermore, this study explores relative rotation velocities from the Doppler shift, indicating an increase in toroidal rotation velocity with applied neutral beam injection. The measured results exhibit concordance with the charge exchange recombination spectrometer data. Furthermore, during boron powder dropping discharges on EAST, B II (1623.60, 1623.79, 1623.95, 1624.02, 1624.17, and 1624.38Å) emission lines exhibiting a similar time behavior trend with boron powder injection are identified. Ion temperatures are measured using B II (1362.46Å) through the Doppler broadening method. These techniques hold significant promise for future impurity analysis at the edge of EAST, providing valuable insights into the behavior of carbon and boron ions.

Robust speckle covariance matrix estimation of sea clutter based on spectral symmetry

Speckle covariance matrix estimation plays an important role in adaptive target detection of maritime radars. Spatial heterogeneity of sea clutter incurs insufficient secondary data in estimation, which degrades detection performance. It is an effective way of estimation improvement to exploit the Doppler spectrum knowledge of sea clutter for structural dimension reduction of the speckle covariance matrix. This paper proposes a speckle covariance matrix estimation method based on the spectral symmetry knowledge of sea clutter, indicating that the spectrum of sea clutter is symmetric with respect to the Doppler offset. First of all, the spectral symmetry of sea clutter is analyzed by using the measured data from several opened databases and the results show that most of the data excluding sea spikes have symmetric Doppler spectra. Secondly, the spectral symmetry constrains the speckle covariance matrix into the Hadamard product of a special rank-one Toeplitz matrix and a real positive definite symmetric matrix. The degrees of freedom are reduced to almost half of that of a Hermitian matrix and thus fewer secondary data are required. Thirdly, a robust iterative maximum likelihood estimator is proposed to estimate the speckle covariance matrix with structural constraint and is free of the clutter texture distribution. The performance of the estimator is verified by the measured data and the results show that it is superior to traditional estimators, particularly in fewer secondary data.

Underwater acoustic propagation and scattering from a dynamic rough sea surface using the Finite-Difference Time-Domain method.

Models for underwater acoustic propagation typically assume that the sea surface is smooth or rough but frozen in time. Long-duration transmissions on the order of tens of seconds are being considered for next-generation SONAR. These types of signals improve target resolution and tracking. However, they can interact with the sea surface at many different wave displacements during transmission. This violates the "frozen" boundary assumption and causes additional transmission losses and Doppler effects on the received signal. Full-wave propagation models can be used to better understand the mechanisms behind these phenomena. This understanding leads to better system design without having to perform expensive at-sea experiments. In this paper, a finite-difference time-domain (FDTD) method is implemented to model the impact of roughness and motion on the sea surface. The FDTD method is a full-wave numeric technique that allows an arbitrary function to define the boundary points. Surface motion is accomplished by modifying these boundary points at each time step. A variable subgrid sea surface boundary technique is developed to improve the accuracy of these FDTD simulations. The rough, time-evolving sea surface is modeled using a Pierson-Moskowitz frequency spectrum, which is simple to implement and fully defined by wind speed and direction.

Decay Timescales of Chromospheric Condensations in Solar Flare Footpoints

Chromospheric condensations (CCs) are a prominent feature of flare footpoint heating in the solar flare standard model, yet their timescales and velocities are not well understood. Fisher derived several important analytical relationships, which have rarely been examined with modern spectral observations. The Interface Region Imaging Spectrograph (IRIS) provides a wealth of flare data with a high enough cadence to sufficiently capture CC evolution. We analyzed Doppler shifts in Mg ii 2791 and Fe ii 2814 from a sample of flare footpoint pixels observed by IRIS to compare with Fisher's analytics and recent flare models. We found a detection lifetime of 1 minute occurs in 50% of the sample, with Mg ii showing several pixels with longer values and Fe ii almost categorically shorter, and both growing with the maximum velocity, v max. The shifts’ half-life is commonly <40 s and is inversely related to v max, indicating that the first half of the CC evolution has more efficient kinetic energy loss. The lifetime’s wide range and growth with v max indicate that the footpoint atmospherics and heating scenarios can vary more widely than first postulated in Fisher. Around 90% of the sample had observable acceleration periods, lasting an average of 38 and 32 s for Mg ii and Fe ii, respectively. These acceleration periods, as well as serving as flare model diagnostics themselves, could potentially be used to calculate other model diagnostics such as the initially accelerated mass.