Missile guidance law design based on free-time convergent error dynamics

To solve the finite-time error-tracking problem in mis-sile guidance,this paper presents a unified design approach through error dynamics and free-time convergence theory.The proposed approach is initiated by establishing a desired model for free-time convergent error dynamics,characterized by its independence from initial conditions and guidance parameters,and adjustable convergence time.This foundation facilitates the derivation of specific guidance laws that integrate constraints such as leading angle,impact angle,and impact time.The theoretical framework of this study elucidates the nuances and synergies between the proposed guidance laws and existing methodologies.Empirical evaluations through simulation comparisons underscore the enhanced accuracy and adaptability of the proposed laws.

Robust and Reusable Fuzzy Extractors from Non-Uniform Learning with Errors Problem

Afuzzy extractor can extract an almost uniformrandom string from a noisy source with enough entropy such as biometric data.To reproduce an identical key from repeated readings of biometric data,the fuzzy extractor generates a helper data and a random string from biometric data and uses the helper data to reproduce the random string from the second reading.In 2013,Fuller et al.proposed a computational fuzzy extractor based on the learning with errors problem.Their construction,however,can tolerate a sub-linear fraction of errors and has an inefficient decoding algorithm,which causes the reproducing time to increase significantly.In 2016,Canetti et al.proposed a fuzzy extractor with inputs from low-entropy distributions based on a strong primitive,which is called digital locker.However,their construction necessitates an excessive amount of storage space for the helper data,which is stored in authentication server.Based on these observations,we propose a new efficient computational fuzzy extractorwith small size of helper data.Our scheme supports reusability and robustness,which are security notions that must be satisfied in order to use a fuzzy extractor as a secure authentication method in real life.Also,it conceals no information about the biometric data and thanks to the new decoding algorithm can tolerate linear errors.Based on the non-uniform learning with errors problem,we present a formal security proof for the proposed fuzzy extractor.Furthermore,we analyze the performance of our fuzzy extractor scheme and provide parameter sets that meet the security requirements.As a result of our implementation and analysis,we show that our scheme outperforms previous fuzzy extractor schemes in terms of the efficiency of the generation and reproduction algorithms,as well as the size of helper data.

Gross errors identification and correction of in-vehicle MEMS gyroscope based on time series analysis

This paper presents a novel approach to identify and correct the gross errors in the microelectromechanical system （MEMS） gyroscope used in ground vehicles by means of time series analysis. According to the characteristics of autocorrelation function （ACF） and partial autocorrelation function （PACF）, an autoregressive integrated moving average （ARIMA） model is roughly constructed. The rough model is optimized by combining with Akaike＇s information criterion （A/C）, and the parameters are estimated based on the least squares algorithm. After validation testing, the model is utilized to forecast the next output on the basis of the previous measurement. When the difference between the measurement and its prediction exceeds the defined threshold, the measurement is identified as a gross error and remedied by its prediction. A case study on the yaw rate is performed to illustrate the developed algorithm. Experimental results demonstrate that the proposed approach can effectively distinguish gross errors and make some reasonable remedies.

Bayesian Joint Modelling of Survival Time and Longitudinal CD4 Cell Counts Using Accelerated Failure Time and Generalized Error Distributions

Survival of HIV/AIDS patients is crucially dependent on comprehensive and targeted medical interventions such as supply of antiretroviral therapy and monitoring disease progression with CD4 T-cell counts. Statistical modelling approaches are helpful towards this goal. This study aims at developing Bayesian joint models with assumed generalized error distribution (GED) for the longitudinal CD4 data and two accelerated failure time distributions, Lognormal and loglogistic, for the survival time of HIV/AIDS patients. Data are obtained from patients under antiretroviral therapy follow-up at Shashemene referral hospital during January 2006-January 2012 and at Bale Robe general hospital during January 2008-March 2015. The Bayesian joint models are defined through latent variables and association parameters and with specified non-informative prior distributions for the model parameters. Simulations are conducted using Gibbs sampler algorithm implemented in the WinBUGS software. The results of the analyses of the two different data sets show that distributions of measurement errors of the longitudinal CD4 variable follow the generalized error distribution with fatter tails than the normal distribution. The Bayesian joint GED loglogistic models fit better to the data sets compared to the lognormal cases. Findings reveal that patients’ health can be improved over time. Compared to the males, female patients gain more CD4 counts. Survival time of a patient is negatively affected by TB infection. Moreover, increase in number of opportunistic infection implies decline of CD4 counts. Patients’ age negatively affects the disease marker with no effects on survival time. Improving weight may improve survival time of patients. Bayesian joint models with GED and AFT distributions are found to be useful in modelling the longitudinal and survival processes. Thus we recommend the generalized error distributions for measurement errors of the longitudinal data under the Bayesian joint modelling. Further studies may investigate the models with various types of shared random effects and more covariates with predictions.

Iterative identification of output error model for industrial processes with time delay subject to colored noise

To deal with colored noise and unexpected load disturbance in identification of industrial processes with time delay, a bias-eliminated iterative least-squares(ILS) identification method is proposed in this paper to estimate the output error model parameters and time delay simultaneously. An extended observation vector is constructed to establish an ILS identification algorithm. Moreover, a variable forgetting factor is introduced to enhance the convergence rate of parameter estimation. For consistent estimation, an instrumental variable method is given to deal with the colored noise. The convergence and upper bound error of parameter estimation are analyzed. Two illustrative examples are used to show the effectiveness and merits of the proposed method.

Time Series Correlated Error's Simulation Scheme with the Application to Simulate the Ephemerides Error of CHAMP

Computer simulation experiment is very important in the phase of project design, the availability of simulated result highly depends on the scheme of error simulation. Time series observations are normally correlated. This paper first discusses the formula of correlated error propagation, then derives the formula of simulating time series correlated errors. This formula is then used to simulate correlated ephemerides errors of CHAMP, then the ephemerides are used to recover the gravity vector at satellite altitude with finite differential formula. The formulae derived in this paper are verified with the difference between the recovered gravity vectors and the `true values’ which are directly computed with the same gravity model as that generating the ephemerides.

Time Synchronized Velocity Error for Trajectory Compression

Nowadays,distance is usually used to evaluate the error of trajectory compression.These methods can effectively indicate the level of geometric similarity between the compressed and the raw trajectory,but it ignores the velocity error in the compression.To fill the gap of these methods,assuming the velocity changes linearly,a mathematical model called SVE(Time Synchronized Velocity Error)for evaluating compression error is designed,which can evaluate the velocity error effectively,conveniently and accurately.Based on this model,an innovative algorithm called SW-MSVE(Minimum Time Synchronized Velocity Error Based on Sliding Window)is proposed,which can minimize the velocity error in trajectory compression under the premise of local optimization.Two elaborate experiments are designed to demonstrate the advancements of the SVE and the SW-MSVE respectively.In the first experiment,we use the PED,the SED and the SVE to evaluate the error under four compression algorithms,one of which is the SW-MSVE algorithm.The results show that the SVE is less influenced by noise with stronger performance and more applicability.In the second experiment,by marking the raw trajectory,we compare the SW-MSVE algorithm with three others algorithms at information retention.The results show that the SW-MSVE algorithm can take into account both velocity and geometric structure constraints and retains more information of the raw trajectory at the same compression ratio.

Impacts of space-time-frequency synchronization errors onwide-band target echo characteristics of bistatic/multistatic radar

Bistatic/multistatic radar has great potential advantages over its monostatic counterpart. However, the separation of a transmitter and a receiver leads to difficulties in locating the target position accurately and guaranteeing space-timefrequency synchronization of the transmitter and the receiver.The error model of space-time-frequency synchronization in a motion platform of bistatic/multistatic radar is studied. The relationship between the space synchronization error and the transmitter platform position, receiver platform position, moving state, and beam pointing error, is analyzed. The effect of space synchronization error on target echo power is studied. The target scattering characteristics are restructured by many separate scattering centers of the target in high frequency regions. Based on the scattering centers model of the radar target, this radar target echo model and the simulation method are discussed. The algorithm of bistatic/multistatic radar target echo accurately reflects the scattering characteristics of the radar target, pulse modulation speciality of radar transmitting signals, and spacetime-frequency synchronization error characteristics between the transmitter station and the receiver station. The simulation of bistatic radar is completed in computer, and the results of the simulation validate the feasibility of the method.

Performance Analysis for Orthogonal Time Frequency Space Modulation Systems with Generalized Waveform

Orthogonal time-frequency space(OTFS),which exhibits beneficial advantages in high-mobility scenarios,has been considered as a promising technology in future wireless communication systems.In this paper,a universal model for OTFS systems with generalized waveform has been developed.Furthermore,the average bit error probability(ABEP)upper bounds of the optimal maximum likelihood(ML)detector are first derived for OTFS systems with generalized waveforms.Specifically,for OTFS systems with the ideal waveform,we elicit the ABEP bound by recombining the transmitted signal and the received signal.For OTFS systems with practical waveforms,a universal ABEP upper bound expression is derived using moment-generating function(MGF),which is further extended to MIMO-OTFS systems.Numerical results validate that our theoretical ABEP upper bounds are concur with the simulation performance achieved by ML detectors.

Time Series Analysis and Prediction of COVID-19 Pandemic Using Dynamic Harmonic Regression Models

Rapidly spreading COVID-19 virus and its variants, especially in metropolitan areas around the world, became a major health public concern. The tendency of COVID-19 pandemic and statistical modelling represents an urgent challenge in the United States for which there are few solutions. In this paper, we demonstrate combining Fourier terms for capturing seasonality with ARIMA errors and other dynamics in the data. Therefore, we have analyzed 156 weeks COVID-19 dataset on national level using Dynamic Harmonic Regression model, including simulation analysis and accuracy improvement from 2020 to 2023. Most importantly, we provide new advanced pathways which may serve as targets for developing new solutions and approaches.

Mitigating Time Interval Error (TIE) in High-Speed Baseband Digital Transports: Design for Delay Compensation at Baseband Infrastructure of Smart-Phones Using Fractal Dispersive Delay-Lines

A major concern in modern smart-phones and hand-held devices is a way of mitigating the time interval error (TIE) perceived at high-speed digital transits along the traces of the circuit-board (rigid and or flexible) used in baseband infrastructures. Indicated here is a way of adopting a planar fractal inductor configuration to improvise the necessary time-delay in the transits of digital signal phase jitter and reduce the TIE. This paper addresses systematic design considerations on fractal inductor geometry commensurate with practical aspects of its implementation as delaylines in the high-speed digital transports at the baseband operations of smart-phone infrastructures. Experimental results obtained from a test module are presented to illustrate the efficacy of the design and acceptable delay performance of the test structure commensurate with the digital transports of interest.

An Output-error-based Iterative Learning Control Algorithm for Linear Discrete-time Dynamic System

This paper improves the iterative learning control algo-rithm for nonlinear discrete-time dynamic systemswhich proposed by D.-H.Hwang et.al.,and make itpossible to use in the system which can give output erroronly.Then a sufficient condition for asymptotical conve-rgence of iterative learning algorithm is proposed.Thealgotithm can be used to a class of nonlinear systems withunknown but periodic parameters.

有限视场下的攻击时间和角度多约束制导律

针对精确打击任务存在导引头视场受限这一过程约束的制导问题,提出了一种考虑视场限制以及攻击角度和时间多种约束的制导律.首先,在以比例导引项实现精确命中的基础上,利用最优控制理论,采用弹目距离加权控制能耗形式的指标函数,设计了攻击角度控制项,并推导了相应的剩余飞行时间解析预测表达式.其次,基于最优误差动力学方法,设计了攻击时间相关的控制项,保证了攻击时间误差的有限时间收敛.同时,针对导引头视场有限的问题,分析了各控制项对前置角收敛性的影响,利用特定的限制函数确保前置角在整个制导过程中始终处于有效视场内,并给出了前置角的有界性及收敛性证明.数学仿真结果验证了所提出制导律对多约束制导问题的有效性.

基于高频正交方波注入法的永磁同步电机控制研究

针对传统高频注入法解调过程复杂和观测精度受非理想因素时延影响的不足,提出一种基于高频正交方波注入法的零低速位置估计方法。首先,考虑估计旋转轴系注入的低可靠性,选择将高频信号注入静止轴系,采用简单代数运算提取出高频响应电流,通过解调正向虚拟高频响应电流初步估计转子位置;然后,针对主要非理想因素进行影响分析,在此基础上,通过解调负向虚拟高频响应电流提取出相位滞后角,完成补偿;最后,为避免启动阶段位置收敛错误,通过获取电感变化趋势判断出磁极极性。实验结果表明,所提算法在各类工况均能稳定收敛,且最大平均误差不超过1°,说明了算法的抗扰性以及准确性。

时间传递链路闭合钟差处理方法与性能分析

守时实验室和全球卫星导航系统(GNSS)维持的时间基准都通过不同溯源链路溯源至UTC(k),k为实验室代号。目前主流溯源方式包括光纤时间传递、卫星双向时间传递和GNSS共视时间传递。通过对不同溯源链路构造闭环钟差模型,利用间接平差修正各溯源链路钟差,消除了不同链路中存在在系统差和周期项等误差,提升了时间传递精度。采用5个时频站中8条独立不同溯源链路的钟差实测数据的对算法进行验证,结果表明:由于存在未消除空间误差的影响,卫星双向时间传递和GNSS共视时间传递在长基线条件下都存在明显的周期项,通过对溯源链路的平差修正,消除了溯源钟差不闭合的问题。通过对钟差的二次多项式拟合得到的残差计算,GNSS共视时间传递噪声由0.43ns降低至0.25 ns,卫星双向时间传递噪声0.21 ns降低至0.14 ns,光纤时间传递噪声由0.18 ns降低至0.12 ns。通过提升各溯源链路的时间传递精度,有效地改善了各时间基准的服务能力。

时间反转直扩通信系统及性能分析

时间反转传输技术的空时聚焦特性能够有效地提升通信系统的性能.本文设计了一种将时间反转技术应用于直扩通信系统的方案,并对多径衰落信道下单用户和多用户系统的接收信干噪比、系统容量、误比特率等性能进行了理论分析与仿真验证.理论与仿真结果表明,通过时间反转预处理,直扩通信系统的性能得到了改善,且优于复杂度较高的多通道Rake接收的系统,且多径数目越多,优势越明显.

双曲型法向圆弧齿轮传动的动力学分析

为探究啮合刚度、传动误差及侧隙对双曲型法向圆弧齿轮传动动态响应的影响,建立齿轮副的动力学方程,并在数值计算的基础上进行仿真分析。运用有限元方法,计算接触位置处的载荷及变形,并对时变啮合刚度、静态传动误差和侧隙进行量化分析。仿真结果表明:对振动位移响应影响最大的是侧隙,其次是时变啮合刚度;对动态啮合力影响最大的则是时变啮合刚度和侧隙。

基于人工智能投票算法建立识别血清钠离子随机误差的实时质量控制法

目的利用人工智能投票(voting)算法,建立一种快速识别血清钠离子随机误差的实时质量控制新方法,并评价在此基础上构建模型的相关效能。方法采用回顾性调查研究方法,通过南京鼓楼医院医学检验科实验室信息系统导出2021年1月至5月在BeckmanAU5400生化分析仪上检测的住院患者的血清钠离子结果,共计144754条,作为本研究的无偏数据。人为引入随机误差,生成相应有偏数据。随后,根据投票算法的原理建立质量控制方法(ViQC)模型。针对每种偏差,用ViQC模型与5种传统PBRTQC算法进行测试,利用分类模型评估指标评价ViQC模型的分析性能。绘制偏差检测曲线,采用误差检出所需对称修剪平均样本数(tANPed)来评价模型的临床检测效能,并与5种传统PBRTQC算法进行比较。结果ViQC模型对所有偏差检测的假阳性率均小于0.002,准确度大于0.951。当误差因子为1.5、2.5和3.0时,ViQC模型假阳性率均为0;当误差因子为2.5时,该模型的准确度高达0.979。与5种传统PBRTQC算法相比,ViQC模型对所有偏差检测的平均tANPed最多下降34%,误差检测敏感度更高。此外,ViQC模型在测试环节TEa定值偏差下的ROC曲线下面积高达0.989,tANPed仅为5。结论成功建立了基于人工智能算法的患者数据实时质量控制模型,其临床检测效能优于传统PBRTQC算法。

用于数据采集器测试的时间脉冲插值分析方法

地震数据采集器时间服务精度是评价设备性能的重要指标,一般使用标准时间源输出的时间脉冲信号采样数据进行分析计算,但受仪器采样率限制,时间偏差测试分辨率不高。设计了基于一阶差分和直方图分析的脉冲信号上升沿识别、高低电位分析和时间偏差分析算法,研究使用非线性插值分析方法对地震数据采集器采集的标准时间脉冲信号进行波形升采样恢复,并基于所提算法进行实验分析和讨论,在提高地震数据采集器时间偏差测试分辨率的同时,规范了测试数据处理方法和流程。