Estimation Scheme Research Articles

Weak Value Quantum Metrology beyond Weak Interaction

Abstract Weak value amplification is crucial in quantum metrology because it enhances the detection of subtle interactions between quantum entities. However, current weak value quantum metrology techniques are only effective for extremely weak interactions, significantly narrowing its range of potential applications. 
In this work, we present the `metrological weak value,' designed for use with quantum interactions of any strengths, making it unnecessary to have prior knowledge of how strong or weak a quantum interaction might be.
Additionally, we demonstrate an adaptive estimation scheme for weak value quantum metrology tailored for gauging an undetermined interaction strength. This scheme, rooted in metrological weak value measurements, aligns perfectly with the quantum Cramér-Rao lowest bound. 
The versatility and effectiveness of the metrological weak value enables weak value quantum metrology beyond weak interactions, paving the way for precision in quantum measurement and broadening its utility across various quantum systems.

Tropical Cyclone Wind Field Reconstruction for Hazard Estimation via Bayesian Hierarchical Modeling With Neural Network

AbstractTropical cyclones (TCs) are one of the biggest threats to life and property around the world. Accurate estimation of TC wind hazard requires estimation of catastrophic TCs having a very long return period spanning up to thousands of years. Since reliable TC data are available only for recently decades, stochastic modeling and simulation turned out to be an effective approach to achieve more stable hazard estimates. In common practice, hundreds of thousands of synthetic TCs are generated first, then wind fields are reconstructed along synthetic TC tracks for hazard estimation. A Bayesian hierarchical modeling approach to the reconstruction of TC wind field is proposed. A modified Rankine vortex is adopted as the wind field model, of which the four free parameters are modeled simultaneously through a multi‐output neural network as a latent process of the wind field. The four parameters are finally represented, spatially and temporally, by a set of neural network weights, The Bayesian model averaging technique is used for parameter estimation and wind field reconstruction, based on a ensemble of maximum a posteriori estimates of the set of weights. Together with previously proposed algorithm for synthetic TC simulation, a two‐stage scheme for TC wind hazard estimation has been formed, which is based on best‐track data only and thus is highly consistent. Application of this scheme to the offshore waters in the western North Pacific basin shows inspiring performance and great flexibility for various purposes of TC wind hazard estimation.

Coupling-Informed Data-Driven Scheme for Joint Angle and Frequency Estimation in Uniform Linear Array With Mutual Coupling Present

Coupling-Informed Data-Driven Scheme for Joint Angle and Frequency Estimation in Uniform Linear Array With Mutual Coupling Present

Truthful and privacy-preserving generalized linear models

This paper explores estimating Generalized Linear Models (GLMs) when agents are strategic and privacy-conscious. We aim to design mechanisms that encourage truthful reporting, protect privacy, and ensure outputs are close to the true parameters. Initially, we address models with sub-Gaussian covariates and heavy-tailed responses with finite fourth moments, proposing a novel private, closed-form estimator. Our mechanism features: (1) o(1)-joint differential privacy with high probability; (2) o(1n)-approximate Bayes Nash equilibrium for (1−o(1))-fraction of agents; (3) o(1) error in parameter estimation; (4) individual rationality for (1−o(1)) of agents; (5) o(1) payment budget. We then extend our approach to linear regression with heavy-tailed data, using an ℓ4-norm shrinkage operator to propose a similar estimator and payment scheme.

Joint RSU and agent vehicle cooperative localization using mmWave sensing

In vehicle networks, accurate vehicle localization is crucial. This paper proposes a joint roadside unit (RSU) and agent vehicles cooperative localization framework based on dual-function radar-communication (DFRC) technology. It utilizes unscented Kalman filtering (UKF) to process DFRC signals and obtain vehicle status information. To improve the angle prediction accuracy of the agent vehicle, an angle fusion estimation scheme based on the maximum likelihood algorithm is proposed. Furthermore, a weighted method is introduced within the joint RSU and agent vehicle cooperative localization to enhance vehicle localization accuracy. Experimental results demonstrate that the proposed angle fusion scheme reduces angle estimation error, and the joint RSU and agent vehicle localization framework significantly improves vehicle localization accuracy.

Developing and validating an adaptive multi-layer vehicle trajectory reconstruction method for outlier removal

Trajectory data is vital for traffic flow studies, and aerial photography-based methods are increasingly used to collect such data. However, these datasets often contain errors from various sources, which can be exacerbated by numerical derivative processes. Previous efforts have not fully addressed some of these issues such as consistency, varying length of outlier sequences, and unknown ground truth trends. Moreover, existing validation methods are often indirect and problematic. To address these limitations, we propose an adaptive multi-layer vehicle trajectory reconstruction method, which consists of three modules: the Initial Window Arrangement module to ensure the precise alignment between the reconstruction window and detected outlier fragments, maintaining internal consistency at boundary points; the Window Size Feasibility Test module to adaptively determine the window size according to varying length of outlier sequences, and XGBoost-based Ground Truth Estimation module to be combined with a least-square-based objective function to significantly improve reconstruction accuracy and more closely replicate the underlying trend. Additionally, we introduce the jerk-based reconstruction, which outperforms the acceleration-based reconstruction. To reliably assess and select the best ground truth estimation scheme and objective function, a novel synthetic dataset containing both the ground truth and realistic outlier fragments is proposed. Subsequently, a comparative evaluation of six different outlier removal methods was conducted using Zen Traffic Data. The validation results, utilizing both the synthetic dataset and Zen Traffic Data, demonstrate the exceptional performance of the proposed method across various evaluation perspectives. The good performance of the proposed outlier removal method is further demonstrated by comparing the IDM calibration results using the trajectories with and without outliers being removed. With just one parameter (jerk anomaly threshold), the parameter settings of our method are more objective and generalizable.

Deep Single Image Defocus Deblurring via Gaussian Kernel Mixture Learning.

This paper proposes an end-to-end deep learning approach for removing defocus blur from a single defocused image. Defocus blur is a common issue in digital photography that poses a challenge due to its spatially-varying and large blurring effect. The proposed approach addresses this challenge by employing a pixel-wise Gaussian kernel mixture (GKM) model to accurately yet compactly parameterize spatially-varying defocus point spread functions (PSFs), which is motivated by the isotropy in defocus PSFs. We further propose a grouped GKM (GGKM) model that decouples the coefficients in GKM, so as to improve the modeling accuracy with an economic manner. Afterward, a deep neural network called GGKMNet is then developed by unrolling a fixed-point iteration process of GGKM-based image deblurring, which avoids the efficiency issues in existing unrolling DNNs. Using a lightweight scale-recurrent architecture with a coarse-to-fine estimation scheme to predict the coefficients in GGKM, the GGKMNet can efficiently recover an all-in-focus image from a defocused one. Such advantages are demonstrated with extensive experiments on five benchmark datasets, where the GGKMNet outperforms existing defocus deblurring methods in restoration quality, as well as showing advantages in terms of model complexity and computational efficiency.

Moving Horizon Fault Estimation of a Hybrid System using the Switched ARX-Laguerre Model

Using moving horizon Fault estimation, we propose in this article a fault estimation of a linear hybrid system using a reduced complexity SARX-Laguerre model. The linear hybrid systems are approximated by a SARX-Laguerre model (Switched ARX-Laguerre).This latter is obtained by expanding a discrete time SARX model parameters on Laguerre orthonormal bases. The resulting model ensures an efficient complexity reduction with respect to the classical SARX model. This parametric complexity reduction is still subject to an optimal choice of the Laguerre poles defining Laguerre bases. Therefore, we propose in this paper to identify the parameters of the SARX-Laguerre model by using a recursive algorithm to identify the Fourier coefficients and a metaheuristic algorithm to identify the poles. The proposed model is built from the system input / output observations and is used to develop a fault estimation scheme. The scheme of the fault estimation based on the Moving Horizon fault Estimation (MHE). The proposed fault estimation using moving horizon procedure is tested on numerical simulation.

Fault Estimation and Tolerant Anti‐Disturbance Switching Control for Switched Systems With Its Application to Circuit Systems

ABSTRACTThis study proposes a fault estimation and tolerant anti‐disturbance switching control (ADSC) approach for the switched systems subject to the system fault and multiple disturbances. The fault dose not required to have a model. The disturbances contain the modeled unmeasurable part and the unmodeled measurable part. First, a composite switching estimator is constructed to simultaneously estimate the unavailable system state, fault and modeled disturbance. Then, by means of the estimator, a switching controller is developed to tolerant the fault and complement the modeled disturbance. Further, under the average dwell time relevant switching signals, criteria are established to ensure the fault estimation performance and robustness property for the switched systems. Finally, via implementing the presented fault estimation and tolerant ADSC scheme on a switched circuit system to regulate the charge and flux, the reasonability of the established result is verified.

Sensorless contact force estimation and robust impedance control for a quadrotor manipulation system

The research on aerial manipulation systems has been increased rapidly in recent years. These systems are very attractive for a wide range of applications due to their unique features. However, dynamics, control and manipulation tasks of such systems are quite challenging because they are naturally unstable, have very fast dynamics, have strong nonlinearities, are very susceptible to parameters variations due to carrying a payload besides the external disturbances, and have complex inverse kinematics. In addition, the manipulation tasks require estimating (applying) a certain force of (at) the end-effector as well as the accurate positioning of it. Thus, in this article, a robust force estimation and impedance control scheme is proposed to address these issues. The robustness is achieved based on the Disturbance Observer (DOb) technique. Then, a tracking and performance low computational linear controller is used. For teleoperation purpose, the contact force needs to be identified. However, the current developed techniques for force estimation have limitations because they are based on ignoring some dynamics and/or requiring of an indicator of the environment contact. Unlike these techniques, we propose a technique based on linearization capabilities of DOb and a Fast Tracking Recursive Least Squares (FTRLS) algorithm. The complex inverse kinematics problem of such a system is solved by a Jacobin based algorithm. The stability analysis of the proposed scheme is presented. The algorithm is tested to achieve tracking of task space reference trajectories besides the impedance control. The efficiency of the proposed technique is enlightened via numerical simulation.

Magnetotransport and Spin-Relaxation Signatures of the Radial Rashba and Dresselhaus Spin-Orbit Coupling in Proximitized Graphene.

Graphene-based van der Waals heterostructures take advantage of tailoring spin-orbit coupling (SOC) in the graphene layer by the proximity effect. At long wavelength-saddled by the electronic states near the Dirac points-the proximitized features can be effectively modeled by the Hamiltonian involving novel SOC terms and allow for an admixture of the tangential and radial spin-textures-by the so-called Rashba angle θ_{R}. Taking such effective models we perform realistic large-scale magnetotransport calculations-transverse magnetic focusing and Dyakonov-Perel spin relaxation-and show that there are unique qualitative and quantitative features allowing for an unbiased experimental disentanglement of the conventional Rashba SOC from its novel radial counterpart, called here the radial Rashba SOC. Along with that, we propose a scheme for a direct estimation of the Rashba angle by exploring the magneto response symmetries when swapping an in-plane magnetic field. To complete the story, we analyze the magnetotransport and spin-relaxation signatures in the presence of an emergent Dresselhaus SOC and also provide some generic ramifications about possible scenarios of the radial superconducting diode effect.

Robust adversarial attacks detection for deep learning based relative pose estimation for space rendezvous

Research on developing deep learning techniques for autonomous spacecraft relative navigation challenges is continuously growing in recent years. Adopting those techniques offers enhanced performance. However, such approaches also introduce heightened apprehensions regarding the trustability and security of such deep learning methods through their susceptibility to adversarial attacks. In this work, we propose a novel approach for adversarial attack detection for deep neural network-based relative pose estimation schemes based on the explainability concept. We develop for an orbital rendezvous scenario an innovative relative pose estimation technique adopting our proposed Convolutional Neural Network (CNN), which takes an image from the chaser’s onboard camera and outputs accurately the target’s relative position and rotation. We perturb seamlessly the input images using adversarial attacks that are generated by the Fast Gradient Sign Method (FGSM). The adversarial attack detector is then built based on a Long Short Term Memory (LSTM) network which takes the explainability measure namely SHapley Value from the CNN-based pose estimator and flags the detection of adversarial attacks when acting. Simulation results show that the proposed adversarial attack detector achieves a detection accuracy of 99.21%. Both the deep relative pose estimator and adversarial attack detector are then tested on real data captured from our laboratory-designed setup. The experimental results from our laboratory-designed setup demonstrate that the proposed adversarial attack detector achieves an average detection accuracy of 96.29%.

Stormwater digital twin with online quality control detects urban flood hazards under uncertainty

Urban drainage systems face increased floods and combined sewer overflows due to climate change and population growth. To manage these hazards, cities are seeking stormwater digital twins that integrate sensor data with hydraulic models for real-time response. However, these efforts are complicated by unreliable sensor data, imperfect hydrologic models, and inaccurate rainfall forecasts. To address these issues, we introduce a stormwater digital twin system that uses online data assimilation to estimate stormwater depths and discharges under sensor and model uncertainty. We first derive a novel state estimation scheme based on Extended Kalman Filtering that fuses sensor data into a hydraulic model while simultaneously detecting and removing faulty measurements. The system’s accuracy is evaluated through a long-term deployment in Austin’s flood-prone Waller Creek watershed. The digital twin model demonstrates enhanced accuracy in estimating stormwater depths at ungauged locations and delivers more accurate near-term forecasts. Moreover, it effectively identifies and removes sensor faults from streaming data, achieving a Receiver Operating Characteristic Area Under the Curve (ROC AUC) of over 0.99 and significantly reducing the potential for false flood alarms. This study provides a complete software implementation, offering water managers a reliable framework for real-time monitoring, rapid flood response, predictive maintenance, and active control of sewer systems.

A centralized frost detection and estimation scheme for Internet-connected domestic refrigerators

Frost accumulation on heat exchange units is a significant problem in refrigeration systems, adversely affecting their operating performance and thereby leading to increased power consumption. Therefore, timely detection and accurate quantification of frost are crucial for effective defrosting strategies. This study presents a novel centralized cloud-based IoT scheme for frost detection and thickness estimation. The image processing is performed on the cloud server to process evaporator coil images for frost thickness quantification. Experiments were conducted on a domestic refrigerator to assess the effectiveness of the proposed image-processing approach and determine latency and processing time. The presented scheme effectively quantifies frost thickness on the evaporator in the 1–5 mm range with a 10.8% error margin. The total inference time, which includes image acquisition, pre-processing, transmission latency, and frost thickness estimation, is approximately 5.15 seconds. The results demonstrate that the proposed image processing method performs comparably to conventional sensors and similar image processing techniques. Moreover, the centralized cloud-based IoT architecture presented effectively meets the scalability demands of consumer refrigerators.

Pilot spoofing attack detection and channel estimation for secure massive MIMO

AbstractPilot spoofing attack (PSA) is an active eavesdropping attack in massive multiple‐input multiple‐output systems, where the eavesdroppers transmit the same pilot sequence as the legitimate user does to the base station to confuse the normal channel estimation during the uplink channel training phase. With the contaminated channel estimations, more information will be leaked to eavesdroppers in the downlink transmission phase. However, it is a challenging issue to detect the PSA attack due to the similarity of the received signals and the variations of the wireless channels. Here, a new PSA detection scheme by using the difference of two different estimators is presented, that is, the least square estimator and the minimum mean square error estimator, without requiring any priori of eavesdroppers. Following that, a new data‐aided channel estimation scheme is proposed to eliminate the PSA effect. Simulation results demonstrate that the proposed PSA detection scheme outperforms the conventional energy detector, and more accurate legitimate channel estimation and higher sum secrecy rates can be obtained with the proposed scheme.

Intermodal container terminal location and capacity design with decentralized flow estimation

This paper studies an intermodal container terminal (IMT) location and design problem, where the IMT operator wants to locate a set of open-access IMTs and design their capacity levels to maximize its profit. Following the IMT operator’s decisions, network users, responsible for container transportation, will independently choose their routes and may procure intermodal services from the IMT operator. Since only limited information is available before the existence of the network, we employ the entropy maximization principle as a least-biased approach to estimate the flow distribution resulting from the network users’ route choices. This enables the IMT operator to predict profit and evaluate the quality of its network design decisions. We formulate the problem as a mixed-integer bilevel nonlinear program, automatically embedding a decentralized flow estimation scheme into the optimization of IMT location and capacity design. By exploring the rationale behind the entropy maximization principle, our problem can also be interpreted as a leader–follower game, in which the IMT operator (as the leader) aims to maximize its profit and the network users (as the follower) maximize their welfare. Due to the bilevel structure and the nonlinear entropy function, the problem is extremely changeling. To support its application in real-world contexts, we propose both exact and approximation algorithms. Finally, we conduct a real-world case study on Sydney Greater Metropolitan Area and draw managerial implications.

Assessing the efficiency of urban blue-green space in carbon-saving: Take a high-density urban area in a cold region as an example

Urban blue-green space responds to climate change by providing relatively cooler surrounding environments to indirectly reduce residents' energy consumption and directly adsorb CO2 from the air. However, the carbon-saving potential of urban blue-green space at the urban scale needs to be addressed in the literature. In this study, an improved ideal quantitative model of carbon-saving is constructed from the user's perspective, considering the cooling effects of urban blue-green space and integrating surface temperature, building information, and urban public space layout. A high-density urban area in a northern cold region of China (Tianjin center urban area) is taken as an example to estimate the annual carbon-saving by performing Landsat surface temperature inversions with the delineation of the effective cooling area, as well as regression analyses based on four seasons of data. The results show that the annual carbon-saving in 2021 is about 78 tonneC/km2, equivalent to 2.21 times the carbon sequestration of the same region. Compared with the edge of the center urban area, the urban blue-green space in the center has a carbon-saving with a 1.4 times higher fluctuation value and a cumulative annual difference of about 394 tonneC/km2. Large blue-green patches in the cold region, while capable of conserving more carbon in spring, summer, and fall, become carbon-source in winter due to increased heating energy consumption and reduced willingness of residents to adopt zero-carbon transportation. The indicators that have the most significant impact on the carbon-saving of urban blue-green space are the cooling area and the background surface temperature. The urban blue-green space coverage and the percentage of impervious surfaces, such as buildings and open spaces, will indirectly impact carbon-saving by affecting the cooling area. The above finding recognizes the carbon-saving potential of urban blue-green space in cold regions. It provides a feasible estimation scheme for comparing the carbon-saving capacity in different seasons.

Fixed‐Time State Observer‐Based Robust Adaptive Neural Fault‐Tolerant Control for a Quadrotor Unmanned Aerial Vehicle

ABSTRACTThis paper presents a fixed‐time state observer‐based robust adaptive neural fault‐tolerant control (RANFTC) for attitude and altitude tracking and control of quadrotor unmanned aerial vehicles (UAVs), considering multiple actuator faults, parametric uncertainty, and unknown external disturbances simultaneously. A novel fixed‐time state error estimation based on sliding mode observer is designed, which is independent of initial conditions. A proportional–integral–derivative (PID) based sliding mode control (SMC) is proposed to handle actuator faults and unknown disturbances in combination with the fixed‐time observer within the fault‐tolerant control (FTC) design scheme. The radial basis function neural network (RBFNN) is employed with the controller to approximate the uncertain parameters of the system. Furthermore, two new adaptive laws are designed to estimate the sudden actuator fault and the unknown upper bound of disturbances independently. Implementing these estimation schemes avoids overestimation, enhances the robustness of the presented controller, and substantially eliminates the control chattering problem. By applying the Lyapunov stability concept, the suggested control strategy guarantees that the states of the quadrotor UAV converge to the origin in a finite time. Finally, simulation studies are conducted to demonstrate the tracking performance and highlight the effectiveness of the proposed FTC design compared to the existing FTC methods.

A scheme of combining DFO and channel estimation scheme for mobile OFDM systems

AbstractTo reduce the impact of residual Doppler frequency offset (RDFO), a joint DFO estimation and CE scheme is proposed for the OFDM systems under the high‐speed mobile environment. In the paper, the expression of interference power caused by the RDFO is first derived, and the effect of RDFO on time‐varying characteristic of channel is analysed. Then, a joint DFO and channel estimation scheme is presented. Specifically, a high‐precision DFO estimator based on the convolutional neural network with anti‐noise is firstly designed. Due to its ability to use fewer samples to adapt well to the new environments, the meta learning is adopted to estimate the time‐varying channel. Moreover, to improve the practicality of the algorithm, the non‐ideal values rather than ideal values are used as the training targets in the two neural networks. Additionally, the proposed method is only based on the received signal and does not require any pilots or training sequences, which has higher transmission efficiency compared to the existing algorithms. The research results indicate that the proposed method has good estimation performance and good practicality, and it is suitable for high‐speed mobile scenarios.

Utilizing Bayesian generalization network for reliable fault diagnosis of machinery with limited data

To address the issues of overfitting, domain generalization challenges, and lack of credibility brought by limited data samples in mechanical fault diagnosis in practical engineering, this paper proposes a reliable Bayesian generalization network (BGNet). A Bayesian convolutional layer is constructed based on variational inference, treating all parameters in the convolutional layer as random variables. This approach makes a single model function similar to an ensemble of an infinite number of models, and thus enhancing the model's capability of overfitting resistance and domain generalization. The parameters of the variational distribution are updated to approximate the posterior distribution by local reparametrization and Monte Carlo sampling to optimize the evidence lower bound (ELBO) loss. Confidence information is extracted from the model results and, uncertainty estimation and decomposition schemes are designed to provide interpretability. The proposed method is applied to analyze the experimental data of bearing and gearbox faults. The results show that in a multi-source domain scenario with limited samples, the proposed method demonstrates high diagnostic accuracy, effectively describes the relationship between domain variability and uncertainty, and significantly outperforms several benchmark and state-of-the-art models.