Improving the Position Accuracy and Computational Efficiency of UAV Terrain Aided Navigation Using a Two-Stage Hybrid Fuzzy Particle Filtering Method

Terrain Aided Navigation(TAN)technology has become increasingly important due to its effectiveness in environments where Global Positioning System(GPS)is unavailable.In recent years,TAN systems have been extensively researched for both aerial and underwater navigation applications.However,many TAN systems that rely on recursive Unmanned Aerial Vehicle(UAV)position estimation methods,such as Extended Kalman Filters(EKF),often face challenges with divergence and instability,particularly in highly non-linear systems.To address these issues,this paper proposes and investigates a hybrid two-stage TAN positioning system for UAVs that utilizes Particle Filter.To enhance the system’s robustness against uncertainties caused by noise and to estimate additional system states,a Fuzzy Particle Filter(FPF)is employed in the first stage.This approach introduces a novel terrain composite feature that enables a fuzzy expert system to analyze terrain non-linearities and dynamically adjust the number of particles in real-time.This design allows the UAV to be efficiently localized in GPS-denied environments while also reducing the computational complexity of the particle filter in real-time applications.In the second stage,an Error State Kalman Filter(ESKF)is implemented to estimate the UAV’s altitude.The ESKF is chosen over the conventional EKF method because it is more suitable for non-linear systems.Simulation results demonstrate that the proposed fuzzy-based terrain composite method achieves high positional accuracy while reducing computational time and memory usage.

MRI IDEAL-IQ在2型糖尿病患者腹部脂肪量化中的应用价值

目的采用磁共振(MRI)非对称回波的最小二程估算法迭代水脂分离(IDEAL-IQ)技术评价2型糖尿病对腹部脂肪分布及内脏脂肪沉积的影响。方法前瞻性纳入温州医科大学附属第二医院29例健康志愿者(对照组)和39例2型糖尿病患者(2型糖尿病组),两组均进行3.0T磁共振IDEAL-IQ序列扫描,得到脂肪分数图像。在脂肪分数图像上测量两组右肝、椎体、两肾实质和肾窦以及胰腺的质子密度脂肪分数(PDFF)、腹腔内脂肪组织(VAT)面积和皮下脂肪组织(SAT)面积。比较两组PDFF、VAT面积和SAT面积。结果2型糖尿病组的右肝、胰腺、左肾窦、右肾窦和椎体的PDFF值均高于对照组,差异有统计学意义(均P<0.05)。2型糖尿病组的VAT面积显著大于对照组[(105.47±7.12)cm^(2)vs(81.36±6.94)cm^(2),P=0.043],而两组SAT面积差异无统计学意义(P>0.05)。在所有参与者中,VAT面积分别与右肝、胰腺、椎体、左肾窦和右肾窦PDFF呈显著正相关。结论MRI IDEAL-IQ技术可以量化成人腹部脏器及结构的脂肪情况,2型糖尿病影响患者腹部脂肪分布并导致腹腔内脏脂肪沉积。

On Rough Intuitionistic Fuzzy Ideals（Filters） in Lattices

In this paper, we introduce a new algebraic structure, called a rough intuitionistic fuzzy ideal(filter) which is a generalized intuitionistic fuzzy ideal(filter) of a lattice and study some related properties of such ideals(filters).

磁共振IDEAL-IQ序列定量分析腹腔内脏脂肪含量与2型糖尿病的关系

目的探讨腹腔内脏脂肪含量与糖尿病发生的关系,进而探讨其预测T2DM发病的可行性。方法收集行腹部3.0T MRI检查并有T2DM患者45例(T2DM组),对照组80例,应用IDEAL软件进行图像自动重组,获得脂肪分数(FF图)、弛豫率(R2*)、脂相、水相、同反相位图像。将FF图传至AW 4.6工作站,由两名观察者采用双盲法对FF图进行测量。应用ITK-SNAP软件采用阈值分割法半自动勾画腹腔内脏脂肪(VAT)及皮下脂肪组织(SAT),并计算体积。采用SPSS 26.0软件对数据进行统计学分析。结果(1)T2DM组的腹腔内脏脂肪组织体积(VATV)、肝脏脂肪分数(HFF)、胰腺脂肪分数(PFF)、甘油三酯(TG)及空腹血糖(FBG)均高于对照组,高密度脂蛋白(HDL)低于对照组,差异有统计学意义(P<0.05);(2)ROC曲线分析示:PFF判断是否患T2DM的曲线下面积(AUC)为0.656(P<0.01,95%CI:0.534~0.777),截断值为8.44%,敏感度为50%,特异度为82.5%;HFF判断是否患T2DM的AUC为0.744(P<0.05,95%CI:0.637~0.851),截断值、敏感度、特异度分别为2.99%、97.5%、42.5%。男性患者VATV判断T2DM的最佳阈值为3466 cm^(3),敏感度为63.2%,特异度为89.5%,AUC为0.78(P<0.01),联合SATV、PFF及HFF时,敏感度为78.9%,特异度为84.2%,AUC为0.839(P<0.01);女性患者利用VATV诊断T2DM的最佳阈值为2103 cm^(3),利用该阈值判断T2DM的敏感度为66.7%,特异度为66.7%,AUC为0.68(P<0.05),联合SATV、PFF及HFF时,敏感度为90.5%,特异度为76.2%,AUC为0.909(P<0.01)。(3)TG和FBG每增加1个单位,患T2DM的风险就分别提升了3986.3%及417.1%,即TG和FBG是T2DM发病的独立预测因子。结论PFF、HFF及VATV单独在预测T2DM发病方面,检验效能较低。VATV联合SATV、PFF、HFF预测T2DM发生的检验效能显著提高,其敏感度及特异度亦均明显提高。IDEAL-IQ技术是评估T2DM患者腹腔内异位脂肪组织沉积定量的便捷的方法,具有简单、稳定性好及重复性高的优势。

A novel digital control strategy of three-phase shunt active power filter under non-ideal mains voltages

A novel control strategy for three-phase shunt active power filter (SAPF) was proposed to improve its performance under non-ideal mains voltages. The approach was inspired by our finding that the classic instantaneous reactive power theory based algorithm was unsatisfactory in terms of isolating positive sequence fundamental active components exactly under non-ideal mains voltages. So, a modified ip-iq reference current calculation method was presented. With usage of the new method, not only the positive sequence but also the fundamental active current components can be accurately isolated from load current. A deadbeat closed-loop control model is built in order to eliminate both delay error and tracking error between reference voltages and compensation voltages under unbalanced and distorted mains voltages. Computer simulation results show that the proposed strategy is effective with better tracking ability and lower total harmonic distortion (THD). The strategy is also applied to a 10 kV substation with a local electrolysis manganese plant injecting a large amount of harmonics into the power system, and is proved to be more practical and efficient.

Underwater four-quadrant dual-beam circumferential scanning laser fuze using nonlinear adaptive backscatter filter based on pauseable SAF-LMS algorithm

The phenomenon of a target echo peak overlapping with the backscattered echo peak significantly undermines the detection range and precision of underwater laser fuzes.To overcome this issue,we propose a four-quadrant dual-beam circumferential scanning laser fuze to distinguish various interference signals and provide more real-time data for the backscatter filtering algorithm.This enhances the algorithm loading capability of the fuze.In order to address the problem of insufficient filtering capacity in existing linear backscatter filtering algorithms,we develop a nonlinear backscattering adaptive filter based on the spline adaptive filter least mean square(SAF-LMS)algorithm.We also designed an algorithm pause module to retain the original trend of the target echo peak,improving the time discrimination accuracy and anti-interference capability of the fuze.Finally,experiments are conducted with varying signal-to-noise ratios of the original underwater target echo signals.The experimental results show that the average signal-to-noise ratio before and after filtering can be improved by more than31 d B,with an increase of up to 76%in extreme detection distance.

一种基于Filter Faster R-CNN的数字PCR液滴检测技术

目的研究液滴数字聚合酶链式反应(ddPCR)液滴检测技术,去除图像中灰尘、气泡、芯片表面的划痕以及微小凹陷等因素产生的异常点对结果的影响,实现高通量、稳定和准确的ddPCR液滴的自动检测。方法提出Filter Faster R-CNN ddPCR液滴检测模型。使用Faster R-CNN生成液滴预测框,之后使用异常点过滤模块(Filter)去除阳性液滴预测框中的异常点。以诺如病毒片段的质粒为模板进行ddPCR实验,建立一个ddPCR数据集,用于模型的训练(2462例,约占78.56%)和测试(672例,约占21.44%)。对异常点过滤模块的3个过滤支路在验证集上进行消融实验,通过与其他ddPCR液滴检测模型进行比较的对比实验以及进行ddPCR的绝对定量实验。结果在少尘和多尘的环境中,Filter Faster R-CNN阳性液滴准确率为98.23%和88.35%,综合指标F1分数分别达到了99.15%和99.14%,高于其他相比较的模型。独立样本T检验的结果证明,相比未添加过滤模块的网络,添加过滤模块后能够显著提示模型在多尘环境中的阳性准确率。在ddPCR绝对定量实验中,将商业化流式检测设备的结果作为标准浓度,绘制了回归线。结果显示,回归线斜率为1.0005,截距为-0.025,决定系数达到了0.9997,二者结果高度一致。结论本文提出了一种基于Filter Faster R-CNN的ddPCR液滴检测技术,为在多种环境条件下的ddPCR实验提供了鲁棒的液滴检测方法。

Disturbance Observer Design with a Bode’s Ideal Filter for Sigma-Delta Modulators

It is usually difficult to design a high performance Sigma⁃Delta(ΣΔ)modulator due to system noises.In this paper,a disturbance observer(DOB)is utilized to estimate the system noises and eliminate their effects on ΣΔ modulators.The applied DOB is introduced with a Bode's ideal cut⁃off(BICO)filter used for the Q⁃filter.The proposed DOB with the BICO filter used in ΣΔ modulators can achieve better noise⁃shaping ability,resulting from the less phase loss of the BICO filter.Finally,the simulation results show that the proposed BICO filter scheme is a useful additional tool for improving the performance of ΣΔ modulators.

Estimating the subsolar magnetopause position from soft X-ray images using a low-pass image filter

The Lunar Environment heliospheric X-ray Imager(LEXI)and Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)missions will image the Earth’s dayside magneto pause and cusps in soft X-rays after their respective launches in the near future,to specify glo bal magnetic reconnection modes for varying solar wind conditions.To suppo rt the success of these scientific missions,it is critical to develop techniques that extract the magnetopause locations from the observed soft X-ray images.In this research,we introduce a new geometric equation that calculates the subsolar magnetopause position(RS)from a satellite position,the look direction of the instrument,and the angle at which the X-ray emission is maximized.Two assumptions are used in this method:(1)The look direction where soft X-ray emissions are maximized lies tangent to the magnetopause,and(2)the magnetopause surface near the subsolar point is almost spherical and thus RSis nea rly equal to the radius of the magneto pause curvature.We create synthetic soft X-ray images by using the Open Geospace General Circulation Model(OpenGGCM)global magnetohydrodynamic model,the galactic background,the instrument point spread function,and Poisson noise.We then apply the fast Fourier transform and Gaussian low-pass filte rs to the synthetic images to re move noise and obtain accurate look angles for the soft X-ray pea ks.From the filte red images,we calculate RS and its accuracy for different LEXI locations,look directions,and solar wind densities by using the OpenGGCM subsolar magnetopause location as ground truth.Our method estimates RS with an accuracy of<0.3 RE when the solar wind density exceeds>10 cm-3.The accuracy improves for greater solar wind densities and during southward interplanetary magnetic fields.The method ca ptures the magnetopause motion during southwa rd interplaneta ry magnetic field turnings.Consequently,the technique will enable quantitative analysis of the magnetopause motion and help reveal the dayside reconnection modes for dynamic solar wind conditions.This technique will suppo rt the LEXI and SMILE missions in achieving their scientific o bjectives.

Uncertainties of landslide susceptibility prediction: Influences of random errors in landslide conditioning factors and errors reduction by low pass filter method

In the existing landslide susceptibility prediction(LSP)models,the influences of random errors in landslide conditioning factors on LSP are not considered,instead the original conditioning factors are directly taken as the model inputs,which brings uncertainties to LSP results.This study aims to reveal the influence rules of the different proportional random errors in conditioning factors on the LSP un-certainties,and further explore a method which can effectively reduce the random errors in conditioning factors.The original conditioning factors are firstly used to construct original factors-based LSP models,and then different random errors of 5%,10%,15% and 20%are added to these original factors for con-structing relevant errors-based LSP models.Secondly,low-pass filter-based LSP models are constructed by eliminating the random errors using low-pass filter method.Thirdly,the Ruijin County of China with 370 landslides and 16 conditioning factors are used as study case.Three typical machine learning models,i.e.multilayer perceptron(MLP),support vector machine(SVM)and random forest(RF),are selected as LSP models.Finally,the LSP uncertainties are discussed and results show that:(1)The low-pass filter can effectively reduce the random errors in conditioning factors to decrease the LSP uncertainties.(2)With the proportions of random errors increasing from 5%to 20%,the LSP uncertainty increases continuously.(3)The original factors-based models are feasible for LSP in the absence of more accurate conditioning factors.(4)The influence degrees of two uncertainty issues,machine learning models and different proportions of random errors,on the LSP modeling are large and basically the same.(5)The Shapley values effectively explain the internal mechanism of machine learning model predicting landslide sus-ceptibility.In conclusion,greater proportion of random errors in conditioning factors results in higher LSP uncertainty,and low-pass filter can effectively reduce these random errors.

A Novel Clutter Suppression Algorithm for Low-Slow-Small Targets Detecting Based on Sparse Adaptive Filtering

Passive detection of low-slow-small(LSS)targets is easily interfered by direct signal and multipath clutter,and the traditional clutter suppression method has the contradiction between step size and convergence rate.In this paper,a frequency domain clutter suppression algorithm based on sparse adaptive filtering is proposed.The pulse compression operation between the error signal and the input reference signal is added to the cost function as a sparsity constraint,and the criterion for filter weight updating is improved to obtain a purer echo signal.At the same time,the step size and penalty factor are brought into the adaptive iteration process,and the input data is used to drive the adaptive changes of parameters such as step size.The proposed algorithm has a small amount of calculation,which improves the robustness to parameters such as step size,reduces the weight error of the filter and has a good clutter suppression performance.

An improved particle filter indoor fusion positioning approach based on Wi-Fi/PDR/geomagnetic field

The existing indoor fusion positioning methods based on Pedestrian Dead Reckoning(PDR)and geomagnetic technology have the problems of large initial position error,low sensor accuracy,and geomagnetic mismatch.In this study,a novel indoor fusion positioning approach based on the improved particle filter algorithm by geomagnetic iterative matching is proposed,where Wi-Fi,PDR,and geomagnetic signals are integrated to improve indoor positioning performances.One important contribution is that geomagnetic iterative matching is firstly proposed based on the particle filter algorithm.During the positioning process,an iterative window and a constraint window are introduced to limit the particle generation range and the geomagnetic matching range respectively.The position is corrected several times based on geomagnetic iterative matching in the location correction stage when the pedestrian movement is detected,which made up for the shortage of only one time of geomagnetic correction in the existing particle filter algorithm.In addition,this study also proposes a real-time step detection algorithm based on multi-threshold constraints to judge whether pedestrians are moving,which satisfies the real-time requirement of our fusion positioning approach.Through experimental verification,the average positioning accuracy of the proposed approach reaches 1.59 m,which improves 33.2%compared with the existing particle filter fusion positioning algorithms.

Detection of functional diversity gradients and their geoclimatic filters is sensitive to data types(occurrence vs.abundance)and spatial scales(sites vs.regions)

Functional diversity(FD)reflects within-and between-site variation of species traits(α-and β-FD,respectively).Understanding how much data types(occurrence-based vs.abundance-weighted)and spatial scales(sites vs.regions)change FD and ultimately interfere with the detection of underlying geoclimatic filters is still debated.To contribute to this debate,we explored the occurrence of 1690 species in 690 sites,abundances of 1198 species in 343 sites,and seven functional traits of the Atlantic Forest woody flora in South America.All FD indices were sensitive and dependent on the data type at both scales,with occurrence particularly increasing a richness and dispersion(occurrence>abundance in 80%of the sites)while abundance increased β total,β replacement,and α evenness(abundance>occurrence in 60%of the sites).Furthermore,detecting the effect of geoclimatic filters depended on the data type and was scale-dependent.At the site scale,precipitation seasonality and soil depth had weak effects on α-and β-FD(max.R^(2)=0.11).However,regional-scale patterns of a richness,dispersion,and evenness strongly mirrored the variation in precipitation seasonality,soil depth,forest stability over the last 120 kyr,and cation exchange capacity(correlations>0.80),suggesting that geoclimatic filters manifest stronger effects at the regional scale.Also,the role of edaphic gradients expands the idea of biogeographical filters beyond climate.Our findings caution functional biogeographic studies to consider the effect of data type and spatial scale before designing and reaching ecological conclusions about the complex nature of FD.

Ideal Case Study of Adaptive Localization in Storm-scale Ensemble Kalman Filter Assimilation

This study explores the use of the hierarchical ensemble filter to determine the localized influence of observations in the Weather Research and Forecasting ensemble square root filtering(WRF-EnSRF)assimilation system.With error correlations between observations and background field state variables considered,the adaptive localization approach is applied to conduct a series of ideal storm-scale data assimilation experiments using simulated Doppler radar data.Comparisons between adaptive and empirical localization methods are made,and the feasibility of adaptive localization for storm-scale ensemble Kalman filter assimilation is demonstrated.Unlike empirical localization,which relies on prior knowledge of distance between observations and background field,the hierarchical ensemble filter provides continuously updating localization influence weights adaptively.The adaptive scheme improves assimilation quality during rapid storm development and enhances assimilation of reflectivity observations.The characteristics of both the observation type and the storm development stage should be considered when identifying the most appropriate localization method.Ultimately,combining empirical and adaptive methods can optimize assimilation quality.

Nonlinear Filtering With Sample-Based Approximation Under Constrained Communication:Progress, Insights and Trends

The nonlinear filtering problem has enduringly been an active research topic in both academia and industry due to its ever-growing theoretical importance and practical significance.The main objective of nonlinear filtering is to infer the states of a nonlinear dynamical system of interest based on the available noisy measurements. In recent years, the advance of network communication technology has not only popularized the networked systems with apparent advantages in terms of installation,cost and maintenance, but also brought about a series of challenges to the design of nonlinear filtering algorithms, among which the communication constraint has been recognized as a dominating concern. In this context, a great number of investigations have been launched towards the networked nonlinear filtering problem with communication constraints, and many samplebased nonlinear filters have been developed to deal with the highly nonlinear and/or non-Gaussian scenarios. The aim of this paper is to provide a timely survey about the recent advances on the sample-based networked nonlinear filtering problem from the perspective of communication constraints. More specifically, we first review three important families of sample-based filtering methods known as the unscented Kalman filter, particle filter,and maximum correntropy filter. Then, the latest developments are surveyed with stress on the topics regarding incomplete/imperfect information, limited resources and cyber security.Finally, several challenges and open problems are highlighted to shed some lights on the possible trends of future research in this realm.

基于多端交互协同的“IDeal”智能社区终端开发与设计

智慧社区是通过各种智能技术和方式整合社区现有的各类服务资源,为社区业主提供便捷服务的新模式、新平台。为迎合市场需求和填补商场空缺,实现社区主要服务项目的数字化管理,“IDeal”智能社区终端采取多端交互协同的设计方案,从业主和社区管理人员2个方面进行设计,通过采取TCP/IP协议、部署云端服务器等,轻松实现了业主移动终端和管理人员PC端的实时交互。整体终端系统设计从实际出发,紧密贴合了物业管理服务和社区业主的需要,应用效果较好,克服了同类软件的弊端,提升了社区管理服务的数字化、智能化水平。

On the Optimal Initial Inner-Core Size for Tropical Cyclone Intensification: An Idealized Numerical Study

Recent observational and numerical studies have revealed the dependence of the intensification rate on the inner-core size of tropical cyclones(TCs). In this study, with the initial inner-core size(i.e., the radius of maximum wind—RMW)varied from 20–180 km in idealized simulations using two different numerical models, we found a nonmonotonic dependence of the lifetime maximum intensification rate(LMIR) on the inner-core size. Namely, there is an optimal innercore size for the LMIR of a TC. Tangential wind budget analysis shows that, compared to large TCs, small TCs have large inward flux of absolute vorticity due to large absolute vorticity inside the RMW. However, small TCs also suffer from strong lateral diffusion across the eyewall, which partly offsets the positive contribution from large inward flux of absolute vorticity. These two competing processes ultimately lead to the TC with an intermediate initial inner-core size having the largest LMIR. Results from sensitivity experiments show that the optimal size varies in the range of 40–120 km and increases with higher sea surface temperature, lower latitude, larger horizontal mixing length, and weaker initial TC intensity. The 40–120 km RMW corresponds to the inner-core size most commonly found for intensifying TCs in observations, suggesting the natural selection of initial TC size for intensification. This study highlights the importance of accurate representation of TC inner-core size to TC intensity forecasts by numerical weather prediction models.

A dual adaptive unscented Kalman filter algorithm for SINS-based integrated navigation system

In this study, the problem of measuring noise pollution distribution by the intertial-based integrated navigation system is effectively suppressed. Based on nonlinear inertial navigation error modeling, a nested dual Kalman filter framework structure is developed. It consists of unscented Kalman filter (UKF)master filter and Kalman filter slave filter. This method uses nonlinear UKF for integrated navigation state estimation. At the same time, the exact noise measurement covariance is estimated by the Kalman filter dependency filter. The algorithm based on dual adaptive UKF (Dual-AUKF) has high accuracy and robustness, especially in the case of measurement information interference. Finally, vehicle-mounted and ship-mounted integrated navigation tests are conducted. Compared with traditional UKF and the Sage-Husa adaptive UKF (SH-AUKF), this method has comparable filtering accuracy and better filtering stability. The effectiveness of the proposed algorithm is verified.

Recursive Filtering for Stochastic Systems With Filter-and-Forward Successive Relays

In this paper,the recursive filtering problem is considered for stochastic systems over filter-and-forward successive relay(FFSR)networks.An FFSR is located between the sensor and the remote filter to forward the measurement.In the successive relay,two cooperative relay nodes are adopted to forward the signals alternatively,thereby existing switching characteristics and inter-relay interferences(IRI).Since the filter-and-forward scheme is employed,the signal received by the relay is retransmitted after it passes through a linear filter.The objective of the paper is to concurrently design optimal recursive filters for FFSR and stochastic systems against switching characteristics and IRI of relays.First,a uniform measurement model is proposed by analyzing the transmission mechanism of FFSR.Then,novel filter structures with switching parameters are constructed for both FFSR and stochastic systems.With the help of the inductive method,filtering error covariances are presented in the form of coupled difference equations.Next,the desired filter gain matrices are further obtained by minimizing the trace of filtering error covariances.Moreover,the stability performance of the filtering algorithm is analyzed where the uniform bound is guaranteed on the filtering error covariance.Finally,the effectiveness of the proposed filtering method over FFSR is verified by a three-order resistance-inductance-capacitance circuit system.

Bayesian Filtering for High-Dimensional State-Space Models With State Partition and Error Compensation

In the era of exponential growth of data availability,the architecture of systems has a trend toward high dimensionality,and directly exploiting holistic information for state inference is not always computationally affordable.This paper proposes a novel Bayesian filtering algorithm that considers algorithmic computational cost and estimation accuracy for high-dimensional linear systems.The high-dimensional state vector is divided into several blocks to save computation resources by avoiding the calculation of error covariance with immense dimensions.After that,two sequential states are estimated simultaneously by introducing an auxiliary variable in the new probability space,mitigating the performance degradation caused by state segmentation.Moreover,the computational cost and error covariance of the proposed algorithm are analyzed analytically to show its distinct features compared with several existing methods.Simulation results illustrate that the proposed Bayesian filtering can maintain a higher estimation accuracy with reasonable computational cost when applied to high-dimensional linear systems.