Physical Modeling and Correction of Quantum Gyroscope Errors in Precision Spacecraft Attitude Control

Light detection and ranging (LiDAR) has the advantage of simultaneous localization and mapping with high precision, making it one of the important sensors for intelligent robotics navigation, positioning, and perception. It is common knowledge that the random measurement error of global navigation satellite system (GNSS) observations is usually considered to be closely related to the elevation angle factor. However, in the LiDAR point cloud feature matching positioning model, the analysis of factors affecting the random measurement error of observations is unsophisticated, which limits the ability of LiDAR sensors to estimate pose parameters. Therefore, this work draws on the random measurement error analysis method of GNSS observations to study the impact of factors such as distance, angle, and feature accuracy on the random measurement error of LiDAR. The experimental results show that feature accuracy is the main factor affecting the measurement error in the LiDAR point cloud feature matching positioning model, compared with distance and angle factors, even under different sensor specifications, point cloud densities, prior maps, and urban road scenes. Furthermore, a simple random measurement error model based on the feature accuracy factor is used to verify the effect of parameter estimation, and the results show that the random error model can effectively reduce the error of pose parameter estimation, with an improvement effect of about 50%.

Controlling the evolution of photonic quantum states is crucial for most quantum information processing and metrology tasks. Due to its importance, many mechanisms of quantum state evolution have been tested in detail and are well understood; however, the fundamental phase anomaly of evolving waves, called the Gouy phase, has had a limited number of studies in the context of elementary quantum states of light, especially in the case of photon number states. Here we outline a simple method for calculating the quantum state evolution upon propagation and demonstrate experimentally how this quantum Gouy phase affects two-photon quantum states. Our results show that the increased phase sensitivity of multi-photon states also extends to this fundamental phase anomaly and has to be taken into account to fully understand the state evolution. We further demonstrate how the Gouy phase can be used as a tool for manipulating quantum states of any bosonic system in future quantum technologies, outline a possible application in quantum-enhanced sensing, and dispel a common misconception attributing the increased phase sensitivity of multi-photon quantum states solely to an effective de Broglie wavelength.

Much attention in robust identification and control has been focused on linear low order models approximating high order linear systems. We consider the more realistic situation with a linear model approximating a nonlinear system. We describe how a nonlinear model error model can be developed, that allows a complete linear design process that results in a closed loop system with performance robustness guarantees (in terms of gain from disturbance to output) against the nonlinear error. Clearly the design can be successful only if the linear model is a reasonably good approximation of the system. A particular aspect of the design process is to define a workable definition of ‘practical stability’ for robust control design, with possible non‐linear model errors. We use affine norms for that purpose. Copyright © 2004 John Wiley & Sons, Ltd.

The detection of microwave states is complicated by strong thermal noise, which is inevitably introduced by linear amplifiers. We show how to extract from measured data normally or anti-normally ordered moments of photon creation and annihilation operators, the set of which contains complete information on the quantum state of an electromagnetic field. Equations for the evolution of the quantum state are derived in terms of moments. Using this approach, we consider in detail issues of decoherence and thermalization of microwave quantum states. Results are illustrated using the examples of Fock, coherent, squeezed, thermal, and even and odd coherent states (Schrodinger cat states).

In the geodetic data processing field, most methods for dealing with inequality constraints model are based on additive random error (ARE) models, and there have been few studies on mixed additive and multiplicative random error (MAAMRE) models with inequality constraints. To address this problem, a MAAMRE model with inequality constraints is first established based on the definition of inequality constraint equations, and then, a corresponding parameter estimation algorithm is proposed based on the idea of an exhaustive search method. In addition, considering a MAAMRE model for an ill-posed problem, an iterative regularization solution for an ill-posed MAAMRE model is first derived, and then, a specific parameter estimation algorithm for an ill-posed MAAMRE model with inequality constraints is further proposed by applying the exhaustive search approach. Finally, the feasibility and advantages of the proposed algorithms are verified by global positioning system (GPS) elevation fitting model and digital terrain model (DTM) examples.Graphical

Abstract: In this paper, a method for determination of refractive index in membrane of fuel cell on basis of three-longitudinal-mode laser heterodyne interferometer is presented. The optical path difference between the target and reference paths is fixed and phase shift is then calculated in terms of refractive index shift. The measurement accuracy of this system is limited by nonlinearity error. In this study, nonlinearity error is modeled by multi-layer perceptrons (MLPs) and stacked generalization method (Stacking), using two learning methods; back propagation (BP) and genetic algorithm. Training neural networks with genetic algorithm, improves modeling of nonlinearity error in this system. In the proposed technique, a real code version of genetic algorithm is used. Parameters and genetic operators are set and designed accurately. The results indicate that the nonlinearity error can be effectively modeled by training the stacking with the genetic algorithm which has minimum mean square error (MSE). Keywords: Fuel cell, Genetic algorithm, Heterodyne interferometer, Multi-layer perceptrons, Nonlinearity error, Refractive index, Stacked Generalization.

Time fractional evolution of a single quantum state and entangled state

Attosecond transient absorption spectroscopy (ATAS) is an all-optical pump-probe technique that employs attosecond pulses (from the extreme ultraviolet to soft X-ray) to excite or probe a system, enabling real-time tracking of electronic transitions, quantum state evolution, and energy transfer processes. This approach possesses some key advantages: 1) ultrafast temporal resolution (sub-femtosecond) combined with high spectral resolution (millielectronvolt level); 2) broadband excitation of multiple quantum states, allowing simultaneous detection of multiple energy levels; and 3) element- and site-specific insights provided by the measurements of inner-shell to valence transition reveal charge transfer dynamics, spin state changes, and local structural evolution. To date, significant breakthroughs have been achieved in atomic/molecular physics, electronic coherent dynamics, and strong-field physics by using ATAS. This paper systematically reviews the technical principles and theoretical models related to ATAS by using medium intensity near-infrared pulses, analyzes the recent progress of the applications in gas-phase systems and condensed-phase systems, and explores their future prospects in ultrafast physical chemistry and quantum materials. In gas-phase environments, the ATAS has demonstrated significant capabilities in probing energy level shifts and population transfers in atomic systems, as well as capturing nonadiabatic dynamics and charge migration in diatomic and polyatomic molecules. While in condensed-phase systems, this technique has been effectively used to study the ultrafast dynamics of carriers in semiconductors and to examine the interaction dynamics of localized electrons in insulators and transition metals. Given the rapid evolution of attosecond laser technologies and the unique advantages of the ATAS detection method, this paper also outlines potential future directions. These prospects are expected to further expand the frontiers of ultrafast spectroscopy and drive advancements in a range of disciplines in basic research and technological applications.

It has been argued that the rate at which the interior of an AdS black hole evolves is dual to the rate of evolution of the (quantum state of the) strongly coupled matter on the boundary which, according to holography, is dual to the black hole. However, we have shown elsewhere that it seems to be possible, by adjusting the specific angular momentum of an AdS5-Kerr black hole, to reduce this rate to (effectively) zero. We argue that this is unphysical, and that it is prevented by the intervention of a superradiant instability, which causes the black hole to shed angular momentum when the angular velocity exceeds a certain critical value. The precise way in which this works has recently been explained by the “grey galaxy” model of the end state, in which the angular momentum is transferred to a “galactic disc.” Thus, the black hole itself cannot sustain a specific angular momentum beyond a critical value: there is an effective upper bound. The holographic interpretation is that, beyond a certain limiting specific angular momentum, strongly coupled matter (corresponding to the black hole) will spontaneously shed angular momentum to some other, confined, form of matter (corresponding to the disc). This idea is supported by recent numerical work on ultra-vortical plasmas. Such an upper bound on specific angular momentum would prevent arbitrarily small rates of quantum state evolution on the boundary. We give a tentative discussion of the relevant observational data in the case of the vortical Quark-Gluon Plasma, and suggest a way in which such an upper bound might appear in future observations.

A Star Sensor based Fiber Optical Gyroscope Error Separation method (SSFES) was proposed, which can be applied in the Fiber Optical Gyroscope (FOG) performance evaluation and fault diagnosis for the space-borne Inertial Navigation System (INS) & Celestial Navigation System (CNS) combined navigation system. The method takes the satellite rotational angular rate and the attitude quaternion to separate the long-term drift component from the FOG output and provides the useful information for FOG error compensation and model construction. The output quaternion of the star sensor was utilized to reconstruct the satellite rotational angular rate information, which was then used to separate the error component from the FOG output. Simulations have been carried out to verify the method and have shown that the error component of FOG output can be extracted efficiently by SSFES and the algorithm is easy to be performed by the onboard computers.

The Star Sensor can be used to assist the inertial navigation system (INS) for navigation and positioning, restraining the accumulated error caused by the inertial components. During the integrated navigation process, the Star Sensor is installed above the INS, generated installation errors between the INS body coordinate system and the Star Sensor body coordinate system. In order to increase the system accuracy, a new gyro error model is established in this paper which taken full account of various error factors. The gyro drifts and the gyro errors coefficients are considered as the state variables. The attitude information of Star Sensor is used as the high-accurate information to construct the observation with the attitude information of INS. Taken the nonlinearity of the actual system into account, the nonlinear filter Cubature Kalman Filter (CKF) is used to estimate the gyro errors commendably. The experiment results showed that the positioning error reduced a lot by utilizing this novel method.

Vehicle state estimation based on Minimum Model Error criterion combining with Extended Kalman Filter

This paper proposes a twice rapid transfer alignment algorithm based on dual models in order to solve the problems such as long convergence time, poor accuracy, and heavy computation burden resulting from the traditional nonlinear error models. The quaternion matching method based on quaternion error model along with the extended Kalman filter (EKF) is applied to deal with the large misalignment in the first phase. Then in the second transfer alignment phase, velocity plus attitude matching method as well as classical Kalman filter is adopted. The simulation and the results of vehicle tests demonstrate that this method combines the advantages of both nonlinear and linear error models with the guarantee of accuracy and fastness.

When satellite is running, it may be influenced by external factors, and the measurement models of gyro and star sensor are changing with time uncertainly. This situation may lead extended Kalman filter(EKF) divergent. So new adaptive filter can be used in this situation, and it can estimate process noise and measuring noise on-time. This paper has applied Sage-Husa adaptive filter in satellite attitude determination. This adaptive filter algorithm can estimate satellite attitude parameters and gyro drift on-time with high accuracy reaching 10-3 level, and stability of the algorithm is also very good.

This study proposes and evaluates an average consensus scheme for wireless sensor networks. For this purpose, two communication error models, the fading model and random error model, are introduced and incorporated into the proposed decentralized average consensus scheme, especially adapted to the constraints of wireless sensor networks. Finally, different simulation scenarios are introduced and their results analyzed to evaluate the performance of the proposed scheme and its effectiveness in meeting the needs of wireless sensor networks.