Estimation Strategy Research Articles

Target Localization and Pursuit With Networked Robotic Vehicles: Theory, Simulation, and Experiments

ABSTRACTThis paper addresses the problem of range‐based simultaneous localization and pursuit (SLAP) with networked robotic vehicles from a theoretical and practical standpoint. The work presented builds upon and extends previous theoretical research by Hung, Rego, and Pascoal on the subject of range‐based SLAP using multiple trackers and a cooperative distributed estimation and control (DEC) strategy. The key novel contributions of the paper are twofold: Event‐triggered communication (ETC) mechanisms for the DEC strategy are proposed, with formal guarantees of stability of the multiple vehicle ensemble. In this approach, each tracking vehicle only communicates with its neighbors—both for target estimation and cooperative control purposes when deemed necessary—thus reducing the cost of communications. The paper presents experimental results from multiple field trials conducted with three autonomous marine vehicles. These trials assess the effectiveness of the proposed DEC/ETC strategy in a real‐world environment.Simulation results are also included and analyzed. For the sake of completeness, source codes and links to auxiliary materials are provided, enabling the readers to run simulations and even implement the DEC/ETC strategy using both Matlab and ROS/Gazebo platforms.Matlab codes: https://github.com/hungrepo/slap-etc.ROS packages: http://github.com/dsor-isr/slap.Aerial view of field trials: http://youtu.be/4LR4WSJHyz8.

Guaranteed efficient energy estimation of quantum many-body Hamiltonians using ShadowGrouping

Estimation of the energy of quantum many-body systems is a paradigmatic task in various research fields. In particular, efficient energy estimation may be crucial in achieving a quantum advantage for a practically relevant problem. For instance, the measurement effort poses a critical bottleneck for variational quantum algorithms. We aim to find the optimal strategy with single-qubit measurements that yields the highest provable accuracy given a total measurement budget. As a central tool, we establish tail bounds for empirical estimators of the energy. They are helpful for identifying measurement settings that improve the energy estimate the most. This task constitutes an NP-hard problem. However, we are able to circumvent this bottleneck and use the tail bounds to develop a practical, efficient estimation strategy, which we call ShadowGrouping. As the name indicates, it combines shadow estimation methods with grouping strategies for Pauli strings. In numerical experiments, we demonstrate that ShadowGrouping improves upon state-of-the-art methods in estimating the electronic ground-state energies of various small molecules, both in provable and practical accuracy benchmarks. Hence, this work provides a promising way, e.g., to tackle the measurement bottleneck associated with quantum many-body Hamiltonians.

Continuous single-ended depth-of-interaction measurement using highly multiplexed signals and artificial neural networks.

This study aims to enhance positron emission tomography (PET) imaging systems by developing a continuous depth-of-interaction (DOI) measurement technique using a single-ended readout. Our primary focus is on reducing the number of readout channels in the scintillation detectors while maintaining accurate DOI estimations, using a high-pass filter-based signal multiplexing technique combined with artificial neural networks (ANNs).
Approach: Instead of reading out all 64 signals from an 8×8 silicon photomultiplier array for DOI estimation, the proposed method technique reduces the signals into just four channels by applying high-pass filters with different time constants. To recover the original signal amplitudes, an ANN is used to demultiplex the multiplexed signals. Specifically, the ANN processes the sampled waveforms of these four multiplexed signals and estimates the energy information of the original 8×8 SiPM channels. In this study, two DOI estimation strategies were explored for a continuous DOI PET detector utilizing triangular teeth-shaped reflectors: a 'single-step estimation' method directly estimating DOI from multiplexed signals, and a 'two-stage cascade estimation' method that first demultiplexes the signals and then estimates DOI. The performances of proposed strategies were validated using data irradiated at five steps (2 mm, 6 mm, 10 mm, 14 mm, and 18 mm).
Results: The signal amplitude of row/column summed signals, which were recovered using the proposed ANN-based demultiplexing, showed strong correlation with ground truth (e.g., R2=0.98 for 125 MHz digitizer sampling rate). Moreover, both the single-step and two-stage estimation methods achieved high accuracy in DOI estimation, with an average DOI estimation accuracy of 72.9% and 74.0% at 125 MHz sampling rate when considering an error range of ± 1 DOI position. 
Significance: This novel signal multiplexing technique significantly reduces the number of required readout channels, making continuous DOI PET more cost-effective. &#xD.

Research on a Hybrid Correction Method for SINS/DVL Kalman Filter Integrated Navigation Based on Convergence Factor Judgment

Abstract The strapdown inertial navigation system (SINS)/doppler velocity log (DVL) integrated navigation is one of the primary methods for underwater navigation, providing autonomous underwater vehicles with precise information on position, velocity, and attitude. The core idea of the SINS/DVL integrated navigation using the Kalman filter is to establish the state and observation equations to estimate the SINS navigation error using DVL velocity. The optimal state estimation and correction strategies are critical to the accuracy of integrated navigation. To address this, this paper proposes a hybrid correction method for SINS/DVL Kalman filter integrated navigation based on a convergence factor judgment (HCKF-CFJ). First, the convergence factor for the Kalman filter is constructed using the diagonal elements of the estimated error covariance matrix corresponding to the velocity error state. Then, the filter's stability is assessed based on this convergence factor. When stability is confirmed, direct feedback correction is applied to the SINS velocity, and indirect feedback correction is applied to the SINS position. SINS velocity and position undergo feedforward correction if the stability condition is not met. Finally, ship experiments validate the effectiveness of the proposed method.

A Novel Finite Position Set Phase-Locked Loop Strategy for PMSM Position Estimation With High-Precision and Low Computational Burden

A Novel Finite Position Set Phase-Locked Loop Strategy for PMSM Position Estimation With High-Precision and Low Computational Burden

Efficient and robust deformation measurement based on unsupervised learning

Digital image correlation (DIC) as a non-contact deformation measurement technique is widely used in various engineering fields. Recently, satisfactory results have been achieved by deep learning-based DIC methods, which rely on synthetic datasets for supervised training due to the difficulty in obtaining the ground truth of deformation fields. However, generalization to real scenes remains challenging due to the inherent differences between synthetic and real images and the limited variability of synthetic datasets. To solve this problem, this paper proposes an unsupervised learning DIC method, referred as UnDIC-Net. UnDIC-Net proposes a Patch-based Zero-Normalized Sum of Squared Differences (Patch-ZNSSD) similarity measure as the loss function for unsupervised training, thereby eliminating the dependency on hard-to-obtain labeled data. Meanwhile, we also collected speckle image from real scenes and built an unlabeled dataset to train UnDIC-Net. For network structure, UnDIC-Net adopts an incremental deformation estimation strategy to compute the deformation field, and interestingly, this network can be applied to large deformation measurements. Adequate experiments show that UnDIC-Net performs well in small deformation measurements, and for larger deformation measurements (>100 pixels), some traditional methods fail while UnDIC-Net still performs well. The code and data of this paper is released at: https://github.com/LianpoWang/UnDICnet-master.

Structural wheat trait estimation using UAV-based laser scanning data: Analysis of critical aspects and recommendations based on a case study

PurposeThe use of UAVs (Unmanned Aerial Vehicles) equipped with sensors such as laser scanners offers an alternative to conventional, labor-intensive manual measurements in agriculture, as they enable precise and non-destructive field surveys.MethodsThis paper evaluates the use of UAV-based laser scanning (RIEGL miniVUX-SYS) for estimating the crop height and the plant area index (PAI) of winter wheat. (Methods) It further introduces a novel ground classification method, enhancing early growth stage classification through sensor attributes like intensity and pulse shape deviation.ResultsThe crop height estimation shows a high R2 score with 99.69% but a systematically lower estimate with a mean absolute error of 7.4 cm. The potential of PAI derivation is analyzed with three different estimation strategies and provides an overview and limitations of the approach. Additional weighting based on the scan angle and the adaptation of the extinction coefficient present results with R2 of 97.66% and a mean absolute error of 0.25.ConclusionThe investigation discusses further the impact of the calculated gap fraction, which describes the ratio of laser beams penetrating through the crop canopy in comparison to the total number of measurements.

Quantifying methane emissions in major rice growing areas of Tamil Nadu using remote sensing and land surface temperature model

Rice (Oryza sativa L.) is cultivated in diverse environments, contributing significantly to global methane (CH4) emissions, accounting for approximately 12% of total methane emissions worldwide. With the demand for increasing rice production, methane emissions from rice fields continue to rise. This underscores the need for reliable strategies for estimation and mitigation strategies. This study aims to estimate methane emissions from rice fields of the Cauvery Delta region of Tamil Nadu using remote sensing data. Sentinel 1A Synthetic Aperture Radar (SAR) data was used to delineate rice areas and assess agronomic flooding, while Land Surface Temperature (LST) derived from MODIS satellite data was used to estimate methane emissions. The semi-empirical methane emission model was employed to estimate methane flux based on temperature-related factors and rice area. The spatial methane estimates derived from the LST-based method were compared with field observations. The results showed that during the Kharif and Rabi seasons of 2023, a total of 169679 and 356270 ha of rice area were delineated, respectively. The total methane emissions of 7.16 and 17.09 Gg were estimated for both seasons, respectively. The agreement between estimated and observed methane for both seasons was 84.74 % and 87.52 %, respectively. This study provides an efficient empirical method for estimating methane emissions across large areas and highlights the need for continued monitoring and the development of mitigation strategies to reduce methane emissions from rice cultivation.

Correlation of serum cotinine with fatty liver index in adults: data from the NHANES March 2017 and 2018

BackgroundThe correlation between serum cotinine and fatty liver index (FLI) needs further investigation for the early identification, prevention, and treatment of metabolic dysfunction-associated steatotic liver disease (MASLD).MethodsData from the NHANES database spanning from March 2017 to 2018 was used to perform the population-based study to assess the relationship between serum cotinine and FLI. A variance estimation strategy was applied to address the data volatility. To examine the correlation between serum cotinine and FLI, a weighted multivariate logistic regression model was used. Initial normality assessment through the Kolmogorov-Smirnov test indicated non-normal distribution. Median and interquartile range were employed for description of non-normally distributed measurement data, and group comparisons were made using the Kruskal-Wallis H test. Proportions were used for ordinal data description and comparisons, with the chi-square test used for categorical data. Smooth curve fittings and generalized additive models were used to explore the non-linear relationship between serum cotinine and FLI.ResultsFinally, 2350 subjects (mean age: 49.83 ± 18.30, 1135 males and 1215 females) were selected for analysis. After adjusting for confounders, serum cotinine showed positive correlation with FLI in adults (β = 0.009, 95% CI: 0.003 to 0.014, P = 0.001). Additionally, individuals in the unexposed and passively exposed groups had lower FLI compared to those in the actively exposed group (β = -3.041, 95% CI: -4.728 to -1.353, P < 0.001; β = -2.159, 95% CI: -4.231 to -0.087, P = 0.041; respectively). Subgroup analyses by gender revealed positive associations between serum cotinine and FLI in both males (β = 0.007, 95% CI: 0.000 to 0.014, P = 0.048) and females (β = 0.012, 95% CI: 0.003 to 0.021, P = 0.007). Additionally, a positive correlation was found in “other races” subgroup (β = 0.017, 95% CI: 0.004 to 0.029, P = 0.008) rather than the subgroups of “Mexican American, Other Hispanic, Non-Hispanic White, and Non-Hispanic Black”. The relationship between serum cotinine and FLI exhibited an inverted U-shaped curve with the turning point occurring at 521 ng/mL.ConclusionThis study of a nationally representative sample demonstrates a positive association between serum cotinine and FLI, characterized by an inverted U-shaped curve. Both active and passive smoking emerge as a risk factor for the development and progression of MASLD. Smoking cessation is recommended to manage MASLD and support liver and cardiovascular health.

Stochastic modeling with time-inhomogeneous Jacobi diffusions: applications to bounded and seasonal environmental processes

AbstractIn this paper we explore stochastic modeling of bounded processes in continuous time using time-inhomogeneous Jacobi diffusions. We present some basic general results and introduce a subclass of models with seasonal time variation. In the seasonal models we derive the conditional mean and variance in closed form and propose a strategy for estimation based on quasi maximum likelihood. An empirical application is carried out to daily time series data on relative humidity. Simulation methods are used to investigate properties of the resulting parameter estimators. The results show that the proposed seasonal Jacobi model gives a very satisfactory fit to data and that the estimation procedure works well.

User velocity estimation in HetNets utilizing vertical and horizontal handover counts via maximum likelihood estimation

In this paper, we propose a maximum likelihood estimation (MLE) of user velocity based on Rayleigh distributed handover counts in heterogeneous networks (HetNets). The high density of base stations (BSs) in HetNets leads to mobile users experiencing unnecessary handovers and service outages, emphasizing the importance of mobility management in HetNets. Since user velocity plays an important role in the handover process, its knowledge is necessary for efficient mobility management. Our proposed velocity estimation strategy involves tracking of both vertical and horizontal handovers over a defined period. We utilized mathematical modeling to estimate that the probability mass function (PMF) of the handover counts in the HetNets scenario follows a Rayleigh distribution. We determined the scale parameter by analyzing the close deployment of small cells and macro-cell BSs using stochastic geometry. Our approach assumes a random distribution of BSs based on a homogeneous Poisson point process and investigates the handover-count PMF considering factors such as user velocity, BS density, and the duration of handover-count measurements. Using these statistics, we derived the Cramer–Rao lower bound (CRLB) and applied a MLE technique to estimate user velocity. The MATLAB simulation method is used to validate our approach for the velocity estimation, and the results show the tight closeness of MLE asymptotic variance with CRLB. The numerical analysis indicates that as user velocity increases, the variance of the estimation also increases. Conversely, the simulation results demonstrate that as BS density and the time span for handover-count measurements increase, the velocity estimation error decreases.

Crime Prediction Using Advanced Deep Learning Techniques: A Systematic Review

Over the last couple of decades, researchers have searched for patterns in the occurrence of crimes to predict crimes using intelligent methods based on Deep Learning accurately. To find out the variety of Deep Learning algorithms applied to the tasks of anticipating crime, this current review paper analyzes more than 150 studies. Researchers are given access to datasets that they use to predict crime; the paper discusses the techniques often implemented in algorithms as well as various patterns and components related to criminality that employ Deep Learning. The review also outlines strategies and potential challenges that must be addressed to improve the efficiency of crime prediction. To finish, the detailed literature review on the application of Deep Learning for crime prediction presented in this paper should be useful to scholars interested in this field. Crime estimation strategies can be raised among authorities to deter and prevent criminal actions even more effectively.

Exploring the Dynamics of Remittances and their Socioeconomic Impacts on Pakistan

Aim of the Study: The prime objective of this study is to examine the association between remittances and the socio-economic development of Pakistan spanning the period from 1991 to 2022. Methodology: The examination applies a robust estimation strategy, including the unit root test of Augmented Dickey-Fuller, the Autoregressive Distributed Lag bound testing approach, and the Error Correction Model. To ensure the validity and reliability of the estimated results, diagnostic tests, including the White test for heteroscedasticity and the LM test for autocorrelation, are applied. Findings: The findings of the study unveil compelling evidence of the statistically significant and positive impact of remittances on socio-economic development in Pakistan. Furthermore, foreign investments emerge as a significant contributor to development while inflation exhibits a significant albeit negative association with socioeconomic progress. On the contrary, foreign aid is found to have a negative but insignificant impact on the socio-economic development of Pakistan. Conclusion: In response to the findings, this study recommends optimizing foreign investment policies to harness their positive impact. Simultaneously, a careful reevaluation of the role of foreign aid in the socio-economic development paradigm is directed.

Statistical Inference on the Shape Parameter of Inverse Generalized Weibull Distribution

In this paper, we propose statistical inference methodologies for estimating the shape parameter α of inverse generalized Weibull (IGW) distribution. Specifically, we develop two approaches: (1) a bounded-risk point estimation strategy for α and (2) a fixed-accuracy confidence interval estimation method for α. For (1), we introduce a purely sequential estimation strategy, which is theoretically shown to possess desirable first-order efficiency properties. For (2), we present a method that allows for the precise determination of sample size without requiring prior knowledge of the other two parameters of the IGW distribution. To validate the proposed methods, we conduct extensive simulation studies that demonstrate their effectiveness and consistency with the theoretical results. Additionally, real-world data applications are provided to further illustrate the practical applicability of the proposed procedures.

Multiple-model-based adaptive state fusion estimation of IWMD-EV using SSUKF

Abstract To improve the preciseness degree and adaptive adjustment ability of the state acquisition system under multiple operating conditions, a vehicle state fusion estimation strategy using strong-tracking suboptimal unscented Kalman filtering (SSUKF) algorithm and adaptive weights is proposed. The vehicle models were established by comprehensively characterizing the multi-parameter mapping relationship under the kinematic, dynamic, and electromechanical coupling driving relationship of in-wheel motor drive electric vehicle (IWMD-EV). To improve estimation performance of vehicle state estimation system in face of nonlinear factors such as noise and interference, a vehicle kinematic-based observer (KO) and a dynamic-based observer (DO) were developed based on the SSUKF algorithm. In addition, to enhance the overall preciseness degree and its adaptive adjustment ability for large-scale road conditions, a state fusion estimation method with adaptive weight is presented by combining redundant information of multiple model observers. The results demonstrated that the proposed method can significantly improve the estimation preciseness degree and the adaptive ability for complex working conditions.&amp;#xD;

Vehicle Mass Estimation via Practical Supervisory Artificial Neural Networks Using Perturbed Engine Torque and Acceleration Inputs

Various model-based mass estimation approaches have been discussed for a long time. However, estimation performance often deteriorates in some driving situations and, in particular, slow convergence and excessive overshoot of estimates are a major issue for model-based approaches. Meanwhile, mass estimation approaches using ANN models have recently emerged to propose better solutions, but their usefulness has not been fully investigated. Therefore, this paper presents a vehicle mass estimation strategy using practical supervisory artificial neural networks to achieve more accurate results with better convergence. Here, the perturbed engine torque and vehicle longitudinal acceleration are selected as the inputs of the ANN (instead of the original engine torque and vehicle acceleration), which allows for the faster convergence of estimates with high accuracy; these inputs are existing sensor values already available in the vehicle system. The effectiveness of the proposed ANN approach was verified using simulation and software-in-the-loop simulation (SILS) with field test data, and it was found that the convergence speed of the proposed ANN is almost twice as fast as that of the model-based approach, the accuracy is much better, and the estimation quality is constantly stable without any excessive transient responses. This study will provide further insights into mass estimation using the ANN approach.

The Effect of Sample Size on the Interpolation Algorithm of Frequency Estimation

Fast and accurate frequency estimation is essential in various engineering applications, including control systems, communications, and resonance sensing systems. This study investigates the effect of sample size on the interpolation algorithm of frequency estimation. In order to enhance the accuracy of frequency estimation and performance, we describe a novel method that provides a number of approaches for calculating and defending the sample size for of the window function designs, whereas, the correct choice of the type and the size of the window function makes it possible to reduce the error. Computer simulation using Matlab / Simulink environment is performed to investigate the proposed procedure's performance and feasibility. This study performs the comparison of the interpolation algorithm of frequency estimation strategies that can be applied to improve the accuracy of the frequency estimation. Simulation results shown that the proposed strategy with the Parzen and Flat-top gave remarkable change in the maximum error of frequency estimation. They perform better than the conventional windows at a sample size equal to 64 samples, where the maximum error of frequency estimation is 2.13e-2, and 2.15e-2 for Parzen and Flat-top windows, respectively. Moreover, the efficiency and performance of the Nuttall window also perform better than other windows, where the maximum error is 7.76×10-5 at a sample size equal to 8192. The analysis of simulation result showed that when using the proposed strategy to improve the accuracy of the frequency estimation, it is first essential to evaluate what is the maximum number of samples that can be obtained, how many spectral lines should be used in the calculations, and only after that choose a suitable window.

Effect of Trade Facilitation on Economic Complexity in Selected African Countries

AbstractRecent research on international trade suggests that economic development and growth occur as countries gain knowledge and learn to produce and export more sophisticated products. This study examines the effect of trade facilitation on economic complexity in selected African countries over the period 2004–2017. It uses four indicators of trade facilitation capturing physical infrastructure, information, and communication technology (ICT), business, and regulatory environment as well as border, and transport efficiency. The empirical analysis using ordinary least square, and the two‐stage least square estimators shows that effective trade facilitation reform targeted particularly at improving ICT as well as border, and transport efficiency enhances economic complexity. The results remain consistent when an alternative estimation strategy is used. These findings offer scope for a reminder that reforms aimed at simplifying border procedures should be implemented and accelerated in African countries.

A Biomimetic Pose Estimation and Target Perception Strategy for Transmission Line Maintenance UAVs.

High-voltage overhead power lines serve as the carrier of power transmission and are crucial to the stable operation of the power system. Therefore, it is particularly important to detect and remove foreign objects attached to transmission lines, as soon as possible. In this context, the widespread promotion and application of smart robots in the power industry can help address the increasingly complex challenges faced by the industry and ensure the efficient, economical, and safe operation of the power grid system. This article proposes a bionic-based UAV pose estimation and target perception strategy, which aims to address the lack of pattern recognition and automatic tracking capabilities of traditional power line inspection UAVs, as well as the poor robustness of visual odometry. Compared with the existing UAV environmental perception solutions, the bionic target perception algorithm proposed in this article can efficiently extract point and line features from infrared images and realize the target detection and automatic tracking function of small multi-rotor drones in the power line scenario, with low power consumption.

A New Discrete Generator with Mathematical Characterization, Properties, Count Statistical Modeling and Inference with Applications to Reliability, Medicine, Agriculture, and Biology Data

In this piece of work, we examine and present a completely new discrete family of distributions that we have created. Our investigation into the relevant mathematical properties and characterizations of the system makes use of both analytical and numerical methods. We focus on a particular member of this family so that we can study its theoretical foundations as well as its graphical and numerical representations. This new model contains a few different hazard rate functions, some of which are referred to as "increasing constant", "decreasing-constant-increasing (U)", "constant", "U-constant", "decreasing", and "J-shape" In a similar vein, the model's probability mass function provides a variety of forms, all of which are helpful and practical. These forms include "asymmetric left skewed," "right skewed with wide peak," "right skewed," "bimodal," "symmetric," and "right skewed," amongst others. Each of these forms is valuable and applicable in their own way. These forms might be discovered in the probability mass function that the model generates. In this investigation, in addition to the Bayesian estimating technique under the traditional loss function of squared errors, we investigate and make use of a total of eight estimate strategies that are not founded on Bayesian theory (classical methods). Simulations employing the Markov Chain Monte-Carlo method are run for comparing the Bayesian way of estimation with the more traditional approach of estimating values. According to the findings that we've compiled, the estimation strategy that is referred to as maximum likelihood yields the most accurate results across the board and for all different types of sample sizes. In addition, we evaluate and contrast the various methods of estimation by making use of six distinct real dataset sets; this indicates the versatility of the unique model that we have developed.