State Estimation of Drive-by-Wire Chassis Vehicle Based on Dual Unscented Particle Filter Algorithm

Accurate vehicle dynamic information plays an important role in vehicle driving safety.However,due to the characteristics of high mobility and multiple controllable degrees of freedom of drive-by-wire chassis vehicles,the current mature application of traditional vehicle state estimation algorithms can not meet the requirements of drive-by-wire chassis vehicle state estimation.This paper proposes a state estimation method for drive-by-wire chassis vehicle based on the dual unscented particle filter algorithm,which make full use of the known advantages of the four-wheel drive torque and steer angle parameters of the drive-by-wire chassis vehicle.In the dual unscented particle filter algorithm,two unscented particle filter transfer information to each other,observe the vehicle state information and the tire force parameter information of the four wheels respectively,which reduce the influence of parameter uncertainty and model parameter changes on the estimation accuracy during driving.The performance with the dual unscented particle filter algorithm,which is analyzed in terms of the time-average square error,is superior of the unscented Kalman filter algorithm.The effectiveness of the algorithm is further verified by driving simulator test.In this paper,a vehicle state estimator based on dual unscented particle filter algorithm was proposed for the first time to improve the estimation accuracy of vehicle parameters and states.

Unscented Kalman filter for a low-cost GNSS/IMU-based mobile mapping application under demanding conditions

For the last two decades,low-cost Global Navigation Satellite System(GNSS)receivers have been used in various applications.These receivers are mini-size,less expensive than geodetic-grade receivers,and in high demand.Irrespective of these outstanding features,low-cost GNSS receivers are potentially poorer hardwares with internal signal processing,resulting in lower quality.They typically come with low-cost GNSS antenna that has lower performance than their counterparts,particularly for multipath mitigation.Therefore,this research evaluated the low-cost GNSS device performance using a high-rate kinematic survey.For this purpose,these receivers were assembled with an Inertial Measurement Unit(IMU)sensor,which actively transmited data on acceleration and orientation rate during the observation.The position and navigation parameter data were obtained from the IMU readings,even without GNSS signals via the U-blox F9R GNSS/IMU device mounted on a vehicle.This research was conducted in an area with demanding conditions,such as an open sky area,an urban environment,and a shopping mall basement,to examine the device’s performance.The data were processed by two approaches:the Single Point Positioning-IMU(SPP/IMU)and the Differential GNSS-IMU(DGNSS/IMU).The Unscented Kalman Filter(UKF)was selected as a filtering algorithm due to its excellent performance in handling nonlinear system models.The result showed that integrating GNSS/IMU in SPP processing mode could increase the accuracy in eastward and northward components up to 68.28%and 66.64%.Integration of DGNSS/IMU increased the accuracy in eastward and northward components to 93.02%and 93.03%compared to the positioning of standalone GNSS.In addition,the positioning accuracy can be improved by reducing the IMU noise using low-pass and high-pass filters.This application could still not gain the expected position accuracy under signal outage conditions.

Time Delay Estimation in Radar System using Fuzzy Based Iterative Unscented Kalman Filter

RSs(Radar Systems)identify and trace targets and are commonly employed in applications like air traffic control and remote sensing.They are necessary for monitoring precise target trajectories.Estimations of RSs are non-linear as the parameters TDEs(time delay Estimations)and Doppler shifts are computed on receipt of echoes where EKFs(Extended Kalman Filters)and UKFs(Unscented Kalman Filters)have not been examined for computations.RSs,certain times result in poor accuracies and SNRs(low signal to noise ratios)especially,while encountering complicated environments.This work proposes IUKFs(Iterated UKFs)to track onlinefilter performances while using optimization techniques to enhance outcomes.The use of cost functions can assist state corrections while lowering costs.A new parameter is optimized using MCEHOs(Mutation Chaotic Elephant Herding Optimizations)by linearly approximating system non-linearity where OIUKFs(Optimized Iterative UKFs)predict a target's unknown parameters.To obtain optimal solutions theoretically,OIUKFs take less iteration,resulting in shorter execution times.The proposed OIUKFs provide numerical approximations which are derivative-free implementations.Simulation evaluation results with estimators show better performances in terms of reduced NMSEs(Normalized Mean Square Errors),RMSEs(Root Mean Squared Errors),SNRs,variances,and better accuracies than current approaches.

Combining unscented Kalman filter and wavelet neural network for anti-slug

The stability of the subsea oil and gas production system is heavily influenced by slug flow. One successful method of managing slug flow is to use top valve control based on subsea pipeline pressure. However, the complexity of production makes it difficult to measure the pressure of subsea pipelines, and measured values are not always accessible in real-time. The research introduces a technique for integrating Unscented Kalman Filter (UKF) and Wavelet Neural Network (WNN) to estimate the state of subsea pipeline pressure using historical data and a state model. The proposed method treats multiphase flow transport as a nonlinear model, with a dynamic WNN serving as the state observer. To achieve real-time state estimation, the WNN is included into the UKF algorithm to create a WNN-based UKF state equation. Integrate WNN and UKF in a novel way to predict system state accurately. The simulated results show that the approach can efficiently predict the inlet pressure and manage the slug flow in real-time using the riser's top pressure, outlet flow and valve opening. This method of estimate can significantly increase the control effect.

Performance of GPS and IMU sensor fusion using unscented Kalman filter for precise i-Boat navigation in infinite wide waters

The Unmanned Surface Vehicle(USV)navigation system needs an accurate,firm,and reliable performance to avoid obstacles,as well as carry out automatic movements during missions.The Global Positioning System(GPS)is often used in these systems to provide absolute position information.However,the GPS measurements are affected by external conditions such as atmospheric bias and multipath effects.This leads to the inability of the stand-alone GPS to provide accurate positioning for the USV systems.One of the solutions to correct the errors of this sensor is by conducting GPS and Inertial Measurement Unit(IMU)fusion.The IMU sensor is complementary to the GPS and not affected by external conditions.However,it accumulates noise as time elapses.Therefore,this study aims to determine the fusion of the GPS and IMU sensors for the i-Boat navigation system,which is a USV developed by Institut Teknologi Sepuluh Nopember(ITS)Surabaya.Using the Unscented Kalman filter(UKF),sensor fusion was carried out based on the state equation defined by the dynamic and kinematic mathematical model of ship motion in 6 degrees of freedom.Then the performance of this model was tested through several simulations using different combinations of attitude measurement data.Two scenarios were conducted in the simulations:attitude measurement inclusion and exclusion(Scenarios I and II,respectively).The results showed that the position estimation in Scenario II was better than in Scenario I,with the Root Mean Square Error(RMSE)value of 0.062 m.Further simulations showed that the presence of attitude measurement data caused a decrease in the fusion accuracy.The UKF simulation with eight measurement parameters(Scenarios A,B and C)and seven measurement parameters(Scenarios D,E and F),as well as analytical attitude movement,indicated that yaw data had the largest noise accumulation compared to roll and pitch.

带有色量测噪声的非线性系统Unscented卡尔曼滤波器

传统Unscented卡尔曼滤波器(Unscented Kalman filter,UKF)要求噪声必须为高斯白噪声,无法解决带有色噪声的非线性系统滤波问题.为此,本文提出了一种带有色量测噪声的UKF滤波新算法.首先,基于量测信息增广和最小方差估计,推导出一类带有色量测噪声的非线性离散系统状态的最优滤波框架,接着采用Unscented变换(Unscented transformation,UT)来计算最优框架中的非线性状态后验均值和协方差,进而得到有色量测噪声下UKF滤波递推公式.所设计的UKF新方法能有效地解决传统UKF在量测噪声有色情况下非线性滤波失效的问题,数值仿真实例验证了其可行性和有效性.

渐消自适应Unscented粒子滤波及其在组合导航中的应用

提出一种新的渐消自适应Unscented粒子滤波算法,通过Sigma点来获取状态估值和协方差阵,利用渐消因子自适应的调节权值大小,得到一种参数可调节的重要性密度函数。该重要性密度函数考虑了最新量测的影响,更合理地利用有效信息,保证了粒子多样性,使滤波性能明显改善,能更好地解决非线性非高斯系统模型的滤波问题。将提出的算法应用于SINS/SAR组合导航系统中,与扩展Kalman滤波和渐消自适应扩展Kalman滤波比较,仿真结果表明,提出的滤波算法能提高导航解算的精度,其性能明显优于扩展Kalman滤波和渐消自适应扩展Kalman滤波。

分数阶Unscented卡尔曼滤波器研究

分数阶微积分在控制系统中的应用日益广泛,随着分数阶动态系统模型的引入,需要求解分数阶状态估计问题的方法。该文从分数阶非线性动态系统模型出发,以概率论为基础,导出分数阶的Unscented卡尔曼滤波器,得到其递推模型并应用于典型的非线性系统,UNGM(Univariate Nonstationary Growth Model)模型和再入飞行器跟踪模型。实验结果证明在合理设置分数阶Unscented卡尔曼滤波器阶次的情况下,能够取得优于Unscented卡尔曼滤波器的效果。

基于Unscented卡尔曼滤波的超视距目标跟踪

针对大地坐标系下,超视距目标状态方程和量测方程复杂的非线性,提出了一种基于Unscented卡尔曼滤波的超视距目标跟踪。Unscented卡尔曼滤波通过设计少量的σ点,并计算这些σ点经由非线性函数的传播,从而获得滤波值基于非线性系统方程的更新。由于Unscented卡尔曼滤波无需像Extended卡尔曼滤波那样求状态方程和量测方程的雅可比矩阵,给计算带来了极大的方便。数值仿真结果表明,所给出的方法是可行而有效的。

Unscented粒子滤波在静基座捷联惯导系统大方位失准角初始对准中的应用研究

静基座大方位失准角的捷联惯导系统误差方程是非线性的,Unscented粒子滤波从非线性系统状态向量概率分布出发,结合Unscented卡尔曼滤波和粒子滤波的特点,无需对非线性系统模型进行处理而能达到较高的滤波精度。本文对Unscented粒子滤波进行了研究,并结合重采样算法,运用于捷联惯导系统静基座大方位失准角初始对准中,计算结果验证了该方法的有效性与优越性。

有色噪声下基于Unscented粒子滤波的语音增强方法

针对含有色噪声的语音,提出了一种基于Unscented粒子滤波的单通道语音增强方法。采用时变自回归模型(TVAR)对干净语音建模,通过Unscented粒子滤波器估计AR模型的参数并滤除有色噪声。与大多数常用的粒子滤波选择的建议分布不同,Unscented粒子滤波器采用Unscented卡尔曼滤波器生成粒子滤波的建议分布。由于在粒子的更新过程中考虑了最近的观测值,Unscented粒子滤波器能够在粒子数少于传统粒子滤波算法所需粒子数目的基础上改善估计的性能。仿真实验结果表明,在有色噪声背景下该算法具有良好的语音增强效果。

自适应平方根Unscented粒子滤波算法研究

针对粒子滤波存在的重要性密度函数难以选取和可能出现粒子退化的问题,在吸收平方根滤波、自适应滤波和粒子滤波优点的基础上,提出了一种新的UPF算法。该算法由UKF算法得到重要性密度函数,通过自适应因子实时控制动力学模型误差,采用平方根分解法抑制系统状态协方差矩阵的负定性。仿真结果证明,文中所提出的自适应平方根UPF算法,不但适用于非线性、非高斯动态系统的滤波计算,而且能有效地改善滤波性能,提高SINS/SAR组合导航系统的定位精度。

强跟踪Unscented滤波器及其在无源跟踪中的应用

本文研究用递推算法实现只测量到达角和多普勒频移的无源双基地雷达的目标运动分析。基于Unscented卡尔曼滤波器和强跟踪滤波器,提出了一种新的强跟踪Unscented滤波器算法。新算法在无源双基地雷达目标跟踪的仿真实验中显示了高精度鲁棒滤波的性能。

Unscented卡尔曼滤波-卡尔曼滤波算法

针对条件线性高斯状态空间模型,提出unscented卡尔曼滤波-卡尔曼滤波unscented Kalman filte-ring-Kalman filtering,UKF-KF算法,该方法用UKF估计条件线性高斯状态空间模型中的非线性状态,用KF估计线性状态。为了有效地融合UKF和KF估计的后验状态分布,将蒙特卡罗方法应用于KF估计的线性状态均值和方差,获得了与UKF sigma点相同数量的后验线性状态估计分布的样本,然后将这些样本与UKF中sigma点进行合成去获得系统中非线性状态的估计。该算法应用于机动目标跟踪的仿真结果表明:与Rao-Blackwellized粒子滤波器(Rao-Blackwellized particle filter,RBPF)相比,该算法虽在估计精度上略有下降,然而计算时间明显降低,有效提高了实时性。

Unscented卡尔曼滤波在飞航导弹地磁导航中的应用

对飞航导弹巡航段的地磁导航问题进行了讨论,导航算法为Unscented卡尔曼滤波,导航信息为地磁异常强度。首先通过对比不同飞行轨迹下的滤波效果,得到了导弹射前路径规划的一些原则。由于Unscented变换参数对滤波效果存在一定影响,提出了利用遗传算法优化Unscented变换参数的方法,优化参数的鲁棒性分析进一步验证了该方法的有效性。最后对Unscented卡尔曼滤波和扩展卡尔曼滤波的导航性能进行了比较,仿真结果表明前者具有更高的导航精度。

一种基于Unscented卡尔曼滤波的多平台多传感器配准算法

首先给出传感器偏差配准模型,然后将目标的运动模型和传感器偏差组合在同一个状态方程中,利用Unscented卡尔曼滤波(UKF)方法进行状态和偏差联合估计,最后理论分析了配准偏差对状态估计的影响.Monte-Carlo仿真表明,该方法能同时有效地估计目标状态和传感器配准偏差.

非高斯噪声下基于Unscented粒子滤波器的非线性系统故障诊断方法

非高斯噪声下非线性系统的故障诊断中,一般是基于粒子滤波器的方法,但普通粒子滤波器通常会发生“退化”现象,严重影响故障的检测和诊断品质。本文通过引入Unscented粒子滤波器方法,利用Unscented变换对随机分布的非线性概率传递能力来产生建议分布,能明显地改善普通粒子滤波器的性能;然后,提出了基于该滤波器的序贯式故障诊断策略,采用负对数似然比方法监控系统的运行状态,故障发后利用状态联合估计器进行故障隔离。计算实例表明,该新方法能实时检测诊断出非线性系统的故障,同时能抑制非高斯噪声的影响。

SVD-Unscented卡尔曼滤波的非线性结构系统识别

提出一种基于奇异值分解的unscented卡尔曼滤波(SVD-UKF)非线性滞回结构系统识别方法。SVD-UKF可被看成改进的unscented卡尔曼滤波(UKF)方法,相对UKF而言,SVD-UKF具有更好的鲁棒性和灵活性。此方法不仅避免象扩展卡尔曼滤波(EKF)为了计算Jacobians矩阵的所需的导数运算,并且可以克服常规UKF方法在计算协方差时经常遇到的病态条件的缺点。对非线性系统参数的识别和突然变化的识别的数值模拟结果显示了所提出方法的鲁棒性和灵活性。

基于unscented粒子滤波的红外弱小目标跟踪

为有效解决非线性环境中的红外弱小目标跟踪问题,提出基于unscented粒子滤波的目标跟踪算法。状态转移先验概率中未考虑当前测量对状态估计的作用,为克服传统粒子滤波算法采用状态转移先验概率作为粒子滤波建议分布的缺点,采用UKF生成粒子滤波的建议分布(UPF),并从中抽样粒子。由于考虑到当前观测值在状态后验估计中产生的影响,改善了目标状态估计的性能,且实验所需粒子数目大大少于传统粒子滤波算法所需粒子数目。用实际红外图像对所提算法做了仿真实验,结果表明,用该方法得到的状态估计结果优于用传统粒子滤波算法和用扩展卡尔曼滤波作为建议分布的粒子滤波算法获得的结果。

权值自适应调整Unscented粒子滤波及其在组合导航中的应用

针对粒子滤波存在的重要性密度函数难以选取和粒子退化问题,提出了一种新的权值自适应调整Unscented粒子滤波算法。该算法在Unscented粒子滤波的采样过程中吸收权值自适应调整的优点,考虑最新量测影响,通过欧氏距离和反映量测噪声统计特性的精度因子来自适应的调整粒子对应权值分布,增加有用粒子的权值,降低粒子退化程度,保持粒子多样性。同时Unscented变换提高了滤波精度,使该算法能更好地适用于非线性、非高斯系统模型的计算。将提出的算法应用于GPS/DR组合导航系统进行仿真验证,结果表明,提出的权值自适应调整Unscented粒子滤波算法得到的东向定位误差控制在±5.5 m附近,北向定位误差则在±5.2 m附近,滤波性能明显优于扩展卡尔曼滤波和Unscented粒子滤波,能提高GPS/DR组合导航系统解算精度。