Phase Drift Research Articles

A Correction Method to Systematic Phase Drift of a High Resolution Radar for Foreign Object Debris Detection

Due to the small size and various types of foreign object debris (FOD), radar detection of FOD on airport runways is a great challenge, and there are often a large number of false alarms in the detection results. Arc-scanning synthetic aperture radar (AS-SAR) is an emerging method for detecting FOD targets, which achieves omnidirectional coverage with a very high azimuth resolution. However, this method faces a similar challenge. A direct way to reduce false alarms is to increase the detection threshold based on enhancing the target signal-to-noise ratio (SNR), and in this paper, the coherent accumulation of multiple images is used to improve the target SNR. The stable phase is also an important feature of the target distinguishing background. Therefore, it is important to maintain the stability of the target phase. Aiming at the systematic phase drift (SPD) caused by atmospheric disturbance and system hardware, a spatial and temporal model is established, a corresponding correction approach is proposed, and the performance of the correction approach is validated by field experiments.

Coupled metronomes on a moving platform with Coulomb friction

Using a combination of theory, experiment, and simulation, we revisit the dynamics of two coupled metronomes on a moving platform. Our experiments show that the platform's motion is damped by a dry friction force of Coulomb type, not the viscous linear friction force that has often been assumed in the past. Prompted by this result, we develop a new mathematical model that builds on previously introduced models but departs from them in its treatment of friction on the platform. We analyze the model by a two-timescale analysis and derive the slow-flow equations that determine its long-term dynamics. The derivation of the slow flow is challenging due to the stick-slip motion of the platform in some parameter regimes. Simulations of the slow flow reveal various kinds of long-term behavior including in-phase and antiphase synchronization of identical metronomes, phase locking and phase drift of non-identical metronomes, and metronome suppression and death. In these latter two states, one or both of the metronomes come to swing at such low amplitude that they no longer engage their escapement mechanisms. We find good agreement between our theory, simulations, and experiments, but stress that our exploration is far from exhaustive. Indeed, much still remains to be learned about the dynamics of coupled metronomes, despite their simplicity and familiarity.

Monitoring soil water content and its salinity with high-precision and low-cost in-situ sensors

The article presents an in situ sensor to monitor soil moisture and water salinity by measuring soil permittivity and conductivity at a frequency 24 MHz via an admittance approach. It addresses difficulties encountered in other permittivity-based sensors. A self-balanced bridge provides on one measurement a digital output linear to capacitance and conductance of electrodes in soil with low offset, high phase resolution, and taking into account all parasitic impedances. Circuit design and procedures to achieve it are described, using electric circuit theory and component specifications. Probe geometry, two parallel cylinders, permits insertion in soil from surface, low disturbances and a high sample volume around electrodes. It provides an analytical relation to convert admittance to soil permittivity. Calibration of a sensor with air, alcohols and water fixes its sensitivity and residual phase and inductance for an uncertainty lower than 2% over a range up to 80 and 200 mS m–1. Identical sensors can use same parameters after adjusting in one simple operation bridge phases. Thermal drift and its consequences on soil variable measurement are investigated and physical origins identified. It permits to reduce the phase drift within ±150 ppm °C– 1 rad to keep accuracy.

Cold Starting Temperature Drift Modeling and Compensation of Micro-Accelerometer Based on High-Order Fourier Transform.

The traditional temperature modeling method is based on the full heating of the accelerometer to achieve thermal balance, which is not suitable for the cold start-up phase of the micro-accelerometer. For decreasing the complex temperature drift of the cold start-up phase, a new temperature compensation method based on a high-order Fourier transform combined model is proposed. The system structure and repeatability test of the micro digital quartz flexible accelerometer are provided at first. Additionally, we analyzed where the complex temperature drift of the cold start-up phase comes from based on the system structure and repeatability test. Secondly, a high-order temperature compensation model combined with K-means clustering and the symbiotic organisms search (SOS) algorithm is established with repeatability test data as training data. To verify the proposed temperature compensation model, a test platform was built to transmit the measured values before and after compensation with the proposed Fourier-related model and the other time-related model, which is also a model aiming at temperature compensation in the cold start-up phase. The experimental results indicate that the proposed method achieves better compensation accuracy compared with the traditional temperature compensation methods and the time-related compensation model. Furthermore, the compensation for the cold start-up phase has no effect on the original accuracy over the whole temperature range. The stability of the accelerometer can be significantly improved to about 30 μg in the start-up phase of different temperatures after compensation.

Kalman filter-enabled parameter estimation for simultaneous quantum key distribution and classical communication scheme over a satellite-mediated link.

An accurate estimation of system parameters is of significance for the practical implementation of the simultaneous quantum key distribution and classical communication (SQCC) over a satellite-mediated link when considering the finite-size effect. In this paper, we propose a Kalman filter (KF)-enabled parameter estimation method for the SQCC over a satellite-mediated link. The fast and slow phase drift can be both estimated by using the improved vector KF carrier phase estimation algorithm, and thus the phase estimation error can be tracked in real time and be almost approximate to the theoretical mean square error limit. Taking advantage of the achieved phase estimation and the dual modulation of the SQCC scheme, the excess noise can be estimated with not only a higher precise but also a lower sacrificing rate of raw keys. Numerical simulations demonstrate the feasibility of the SQCC in both the downlink and uplink in terms of the finite-size effect. As a comparison of the Mth-power algorithm, we find that the secret key rate and achievable zenith angle perform better by using the vector KF algorithm. It paves the way of practical implementations for the SQCC system.

Modeling and Advanced Control of Dual-Active-Bridge DC–DC Converters: A Review

This article classifies, describes, and critically compares different modeling techniques and control methods for dual-active-bridge (DAB) dc–dc converters and provides explicit guidance about the DAB controller design to practicing engineers and researchers. First, available modeling methods for DAB including reduced-order model, generalized average model, and discrete-time model are classified and quantitatively compared using simulation results. Based on this comparison, recommendations for suitable DAB modeling method are given. Then, we comprehensively review the available control methods including feedback-only control, linearization control, feedforward plus feedback, disturbance-observer-based control, feedforward current control, model predictive current control, sliding mode control, and moving discretized control set model predictive control. Frequency responses of the closed-loop control-to-output and output impedance are selected as the metrics of the ability in voltage tracking and the load disturbance rejection performance. The frequency response plots of the closed-loop control-to-output transfer function and output impedance of each control method are theoretically derived or swept using simulation software PLECS and MATLAB. Based on these plots, remarks on each control method are drawn. Some practical control issues for DAB including dead-time effect, phase drift, and dc magnetic flux bias are also reviewed. This article is accompanied by PLECS simulation files of the reviewed control methods.

Characterizing modal exponential growth behaviors of self-excited transverse and longitudinal thermoacoustic instabilities

Self-excited thermoacoustic instabilities as frequently observed in rocket motors, gas turbines, ramjets, and aeroengine afterburners are highly detrimental and undesirable for engine manufacturers. Conventionally, modal analysis of such combustion instability is conducted by examining the eigenfrequencies. In this work, thermoacoustic dynamics coupling studies are performed as an alternative approach to predict and characterize modal growth behaviors in the presence of transverse and longitudinal combustion instabilities. Unsteady heat release is assumed to depend on the temperature rate of change that results from the chemical reaction. Coupling the unsteady heat release model with traveling waves enables the modal growth rate of acoustic disturbances to be predicted, thus providing a platform to gain insights onto stability behaviors of the combustor. Both modal growth and total energy analyses of acoustic disturbances are performed by linearizing the unsteady heat release model and recasting it into the classical time-lag N−τ formulation with respect to the velocity potential function ϕ. It is shown from both analyses that the amplitude of any acoustic disturbances tends to increase exponentially with time, until the growth rate is limited by some dissipative process ζ. The chemical reaction rate increase with temperature is shown to be unstable with respect to acoustic wave motions. Furthermore, the maximum modal “growth rate” is determined in the absence of acoustic losses, i.e., ζ = 0. The derived maximum growth rate is experimentally confirmed to be greater than those practically measured ones from both Rijke tubes and swirling combustors. A phase drift is also experimentally observed. Finally, the effects of (1) the interaction index N, (2) the time-delay τ, (3) the ratio γ of the specific heats, and (4) the acoustic losses/damping ζ are examined via cases studies. They are found to vary the critical temperature rate of change of the chemical reaction or the critical frequency ωcri above which the combustion system becomes unstable.

New measurement method for in-field measurements of liquid phase drift from cooling towers

This contribution introduces a newly proposed thermal based method for measurements of drift for in-field applications on real cooling towers up to an eliminator efficiency of approximately 0.0001% of circulating water flow. This method should thus become the main alternative to the methods used nowadays. The main advantage of this newly proposed measurement procedure should be its easy preparation and implementation and quick analysis of measurement results. The article summarizes the development of an innovative probe, and why this new probe and the entire method is needed for the industry. The design of this new probe consists of two main directions of development pathways: the electronic part and the aerodynamic part. The first one lies in the development of a sensor (and the accompanying electronics) and sums up the theoretical principle of the method (the calculation of droplet sizes scatter based on statistics, and the calculation of power needed for the sensor). The aerodynamics part derives from the desired efficiency and accuracy of the measurement and is based on precedent modelling and calculations of flow, containing droplets of various sizes through various uniquely shaped channels. The contribution also demonstrates an experiment made thus far, showing quantity measured and the drift evaluation process.

Bayesian Detection of a Sinusoidal Signal With Randomly Varying Frequency

The problem of detecting a sinusoidal signal with randomly varying frequency has a long history. It is one of the core problems in signal processing, arising in many applications including, for example, underwater acoustic frequency line tracking, demodulation of FM radio communications, laser phase drift in optical communications and, recently, continuous gravitational wave astronomy. In this paper we describe a Markov Chain Monte Carlo based procedure to compute a specific detection posterior density. We demonstrate via simulation that our approach results in an up to $25$ percent higher detection rate than Hidden Markov Model based solutions, which are generally considered to be the leading techniques for these problems.

Off-Grid Error and Amplitude–Phase Drift Calibration for Computational Microwave Imaging With Metasurface Aperture Based on Sparse Bayesian Learning

Computational microwave imaging (CMI) based on the frequency diversity metasurface apertures (FDMAs) is an emerging technology and has attracted wide attention. FDMA based CMI (FDMA-CMI) can be considered as microwave compressive sensing imaging with the frequency diversity pattern of the FDMA being the sensing matrix and solved by sparse signal reconstruction algorithms. However, the imaging quality is affected by the sensing matrix error and off-grid error seriously. In this paper, we propose a novel algorithm for FDMA-CMI, referred to as OGSISBL, by taking both the off-grid error and sensing matrix error into account. Firstly, we establish the measurement model with both the off-grid error and sensing matrix error. Specifically, the off-grid error is represented as a set of parameters to be estimated in the measurement model and the sensing matrix error is represented as the amplitude-phase drift of the transceiver channels of the imaging system due to the principle of the FDMA. Then, under the framework of the sparse Bayesian learning, a robust imaging algorithm OGSISBL is developed via the variational Bayesian expectation maximization (VBEM), which can not only recover the amplitude and position of the return of the scattered, but also simultaneously calibrate the amplitude-phase drift of the transceiver channels and the off-grid error. The performance of the proposed algorithm is evaluated by both the simulation data and the measured data collected by the self-designed experimental FDMA-CMI system, and the results validate the effectiveness and robustness of the proposed method.

Photonics-Based Near-Field Measurement and Far-Field Characterization for 300-GHz Band Antenna Testing

In this study, photonics-based near-field measurement and far-field characterization in a 300-GHz band are demonstrated using an electrooptic (EO) sensor with planar scanning. The field to be measured is up-converted to the optical domain (1550 nm) at the EO sensor and delivered to the measurement system with optical fiber. The typical phase drift of the system is 0.46° for the one-dimensional measurement time of 13 s, which is smaller than the standard deviation of the phase measurement of 1.2° for this time scale. The far-field patterns of a horn antenna calculated from the measured near-field distribution are compared with that measured with the direct far-field measurement system using a vector network analyzer. For the angular related parameters, the accuracy of the results obtained by our near-field measurement are comparable to that of those obtained by direct far-field measurements. The sidelobe level discrepancy (approximately 1 dB) between the results obtained based on our near-field measurement and those from the direct far-field measurements are attributed to the excess noise of the probe correction data. We believe that photonics-based near-field measurements with spherical EO probe scanning will pave the way for the characterization of high-gain antennas at the 300-GHz band.

A high precision phase measurement system implemented in FPGA with phase interpolator.

High precision timing distribution is crucial to many large scale cosmology and particle physics experiments. Besides the space and energy information, the accurate timing provides an extra dimension for physics event reconstruction. In the timing distribution system, accurate clock phase measurement is an indispensable tool to monitor the phase drift and to achieve accurate phase adjustment. This paper introduces a novel phase measurement method implemented in the Xilinx Field Programmable Gate Array (FPGA). It uses the dedicated phase interpolator in the multi-gigabit transceiver. A design based on this method is implemented within the Kintex Ultrascale series FPGA. The preliminary test result shows that a sub-picosecond level precision is achieved. With this system, the nonlinearity of the phase adjustment in the Xilinx transceiver is measured.

The Eccentric and Accelerating Stellar Binary Black Hole Mergers in Galactic Nuclei: Observing in Ground and Space Gravitational-wave Observatories

Abstract We study the stellar binary black holes (BBHs) inspiraling/merging in galactic nuclei based on our numerical method GNC. We find that 3%–40% of all newborn BBHs will finally merge due to various dynamical effects. In a five-year mission, up to 104, 105, and ∼100 of BBHs inspiraling/merging in galactic nuclei can be detected with signal-to-noise ration >8 in Advanced LIGO (aLIGO), Einstein/DECIGO, and TianQin/LISA/TaiJi, respectively. Roughly tens are detectable in both LISA/TaiJi/TianQin and aLIGO. These BBHs have two unique characteristics. (1) Significant eccentricities: 1%–3%, 2%–7%, or 30%–90% of them have e i > 0.1 when they enter into aLIGO, Einstein, or space observatories, respectively. Such high eccentricities provide a possible explanation for that of GW190521. Most highly eccentric BBHs are not detectable in LISA/Tianqin/TaiJi before entering into aLIGO/Einstein, as their strain becomes significant only at f GW ≳ 0.1 Hz. DECIGO becomes an ideal observatory to detect those events, as it can fully cover the rising phase. (2) Up to 2% of BBHs can inspiral/merge at distances ≲103 r SW from the massive black hole, with significant accelerations, such that the Doppler phase drift of ∼10–105 of them can be detected with signal-to-noise ratio >8 in space observatories. The energy density of the gravitational-wave backgrounds (GWBs) contributed by these BBHs deviates from the power-law slope of 2/3 at f GW ≲ 1 mHz. The high eccentricity, significant accelerations, and the different profile of the GWB of these sources make them distinguishable, and thus interesting for future gravitational-wave detections and tests of relativities.

Stable microwave LO distribution via a phase-locked OEO assisted by passive compensation

We present a stable microwave local oscillator (LO) distribution scheme based on a phase-locked optoelectronic oscillator (OEO) assisted by passive compensation. By injecting the LO reference into the phase-locked OEO and locking it with the oscillation signal, a microwave LO with a low phase noise can be tapped at the remote site. Meanwhile, a frequency-mixing-based passive compensation system is utilized to efficiently prevent the remote LO signal from the slow phase drift caused by the temperature susceptibility of fiber. In the experiment, a 10.8 GHz LO signal is stably distributed along a 6.8-km ring fiber to the remote site that is 1.8 km away from the local site. The frequency stability of 3.8 × 10−14 at 1s and 2.3 × 10−17 at 10000 s can be attained. The single-sideband (SSB) phase noise of the received signal at the remote site is up to –110 dBc/Hz at 10 kHz offset frequency with the root-mean-square phase jitter of 0.023 rad.

Cell-Free Massive MIMO Systems With Non-Ideal Hardware: Phase Drifts and Distortion Noise

In this paper, a generalized uplink cell-free massive multiple-input multiple-output (mMIMO) system with hardware imperfections is investigated. Specifically, the hardware impairments consist of multiplicative phase drifts, additive distortion, and amplified thermal noise. Leveraging on a generic hardware impairment model, closed-form expressions for the channel estimation and achievable rate are presented. Based on these tractable results, we explore the uplink achievable rate surrounding the key design parameters, i.e., the channel estimation error, the number of antenna arrays, the local oscillator (LO) setup, and the variance of phase drifts. Aside from this, we also design a power optimization algorithm to maximize the sum-rate under variant channel use. The weighted minimum mean square error (MMSE) methodology is introduced to iteratively determine the solution in closed form. Numerical simulations validate the analytical analyses and examine the effectiveness of the proposed power control algorithm.

Large Tunable 16-Tupled Millimeter Wave Generation Utilizing Optical Carrier Suppression With a Tunable Sideband Suppression Ratio

Coping up with the rising bandwidth demands for 5G ultra-high speed applications, utilizing millimeter (MM) wave spectrum for data transmission over the radio over a fiber-based system is the ideal approach. In this study, a highly conversant and spectrally pure photonic generation of a 16-tupled MM wave signal using a series-connected DD-MZM with a lower modulation index, a splitting ratio, and a wider tunable range is presented. A 160-GHz MM wave is generated through a double sideband optical carrier suppression technique having an optical sideband suppression ratio (OSSR) of 69 dB and a radio frequency sideband suppression ratio (RSSR) of 40 dB. However, the OSSR and the RSSR are tunable with values greater than 15 dB when the modulation index (M.I.) varies from 2.778 to 2.873, ±8° phase drift, and a 15-dB enhancement in the OSSR with a wider nonideal parameter variation range giving acceptable performance can be seen in the model as compared with previous research works.

Longitudinal beam splitting and coalescing using an off-momentum drifting barrier bucket

There has been much research on longitudinal bunch splitting and coalescing for accelerator performance improvements in rf synchrotrons. In this paper, we report a scheme with several induction cells that goes beyond the limitation in which the phase drift speed of beam buckets must be much lower than that of the maximum off-momentum particles to minimize longitudinal emittance blowup. In principle, additional pulse acceleration voltages can be applied to produce a momentum jump of the beam and save beam manipulation time, which is crucial for fast-cycling synchrotrons with limited injection and extraction times. This paper compares the new and conventional schemes with experimental results. Finally, the beam behaviors are discussed with macroparticle simulations.

Machine Learning-Assisted Measurement Device-Independent Quantum Key Distribution on Reference Frame Calibration.

In most of the realistic measurement device-independent quantum key distribution (MDI-QKD) systems, efficient, real-time feedback controls are required to maintain system stability when facing disturbance from either external environment or imperfect internal components. Traditionally, people either use a “scanning-and-transmitting” program or insert an extra device to make a phase reference frame calibration for a stable high-visibility interference, resulting in higher system complexity and lower transmission efficiency. In this work, we build a machine learning-assisted MDI-QKD system, where a machine learning model—the long short-term memory (LSTM) network—is for the first time to apply onto the MDI-QKD system for reference frame calibrations. In this machine learning-assisted MDI-QKD system, one can predict out the phase drift between the two users in advance, and actively perform real-time phase compensations, dramatically increasing the key transmission efficiency. Furthermore, we carry out corresponding experimental demonstration over 100 km and 250 km commercial standard single-mode fibers, verifying the effectiveness of the approach.

Neural networks for on-the-fly single-shot state classification

Neural networks have proven to be efficient for a number of practical applications ranging from image recognition to identifying phase transitions in quantum physics models. In this paper, we investigate the application of neural networks to state classification in a single-shot quantum measurement. We use dispersive readout of a superconducting transmon circuit to demonstrate an increase in assignment fidelity for both two and three state classifications. More importantly, our method is ready for on-the-fly data processing without overhead or need for large data transfer to a hard drive. In addition, we demonstrate the capacity of neural networks to be trained against experimental imperfections, such as phase drift of a local oscillator in a heterodyne detection scheme.

Experiment on a distributed fiber optic interferometric sensing system to monitor and locate urban high-density polyethylene gas pipe leakage

A distributed fiber optical interferometric sensing system was built to monitor and detect urban high-density polyethylene gas pipe leakage. To eliminate the phase drift, the digital phase generated carrier scheme was designed to demodulate the phase difference of interferometers induced by high-density polyethylene gas pipe leakage. Then the empirical mode decomposition and approximate entropy were combined to process fiber optic interferometric signals. Finally, leakage points were located by the null-frequency spectrum after empirical mode decomposition filter denoising. The lab-scale test validated the distributed fiber optical interferometric sensing system leakage location performance and demonstrated a reduced leakage accident missed alarm rate.