一种信标水声定位的改进扩展卡尔曼滤波算法

水声测距误差通常偏离高斯分布,纯距离扩展卡尔曼滤波(Extended Kalman Filter,EKF)定位跟踪算法误差较大。在将测距噪声分为高斯分量和非高斯缓变分量的基础上,提出了一种改进的扩展卡尔曼滤波EKF算法(Improved Extended Kalman Filter,IEKF)和初值选取方法。利用仿真实验和湖试对IEKF算法进行了验证,结果表明IEKF算法能够对测距偏差进行跟踪补偿,定位精度明显优于常规EKF算法。     

修正时延扩展卡尔曼滤波MIMO声纳目标跟踪方法研究

分布式多输入多输k~（Multiple．InputMultiple—Output,MIMO）声纳是一种通过MIMO技术规划时空信道来提高声纳探测性能的新型主动探测声纳体制。由于分布式MIMO声纳节点分布间隔大,水中声速较小,由各发射节点同步发射的测距信号将经过不同的时延到达目标,因此各接收节点测得的距离值分别对应于目标不同时刻的状态。常规的定位方法并没有考虑传播时延对测量值的影响,因而定位精度受到限制。提出了一种修正时延的扩展卡尔曼滤波方法（ModifiedExtendedKalmanFilter,MEKF）对分布式MIMO声纳系统中的移动目标进行跟踪。仿真结果表明,与常规的目标定位跟踪方法相比,该方法有定位精度高、收敛速度快、跟踪性能稳定的特点。     

EKF和PF算法及其在目标跟踪中的应用

基于贝叶斯滤波的目标跟踪原理,介绍了扩展卡尔曼滤波(Extended Kalman Filter,EKF)和粒子滤波(ParticleFilter,PF)的基本思想和算法实现步骤。在非线性环境下对比分析了EKF算法和PF算法的估计精度,并给出两种方法的适用条件。EKF算法采用Taylor展开的线性变换来近似非线性模型,而PF算法采用一些带有权值的随机样本来表示所需要的后验概率密度。仿真结果表明,在强非线性非高斯环境下,PF算法的跟踪性能远优于EKF算法,当系统非线性强度不大时,EKF算法和PF算法的估计精度相差不大,但PF算法计算复杂,跟踪时间长,实时性差。     

基于部分加速度测量的结构Bouc-Wen非线性恢复力及质量识别

结构非线性行为识别是结构灾后损伤评估的关键。扩展卡尔曼滤波(Extended Kalman Filter,EKF)有助于解决结构动力响应测量不完备的问题,但一般要求结构质量已知。针对仅部分加速度响应已知和结构质量未知情况下结构非线性恢复力的识别问题,提出一种结合EKF和最小二乘算法的结构非线性恢复力及质量识别的迭代算法。该方法基于质量估计值和部分自由度上的加速度响应测量,通过EKF预测完整响应时程,再利用最小二乘法识别修正质量分布,循环迭代至收敛,最后基于质量收敛值实现物理参数(刚度、阻尼、非线性)的识别,进而得到非线性恢复力。以一个含Bouc-Wen磁流变阻尼器的多自由度体系的数值模型为例,考虑4种不同的质量初始误差,通过数值模拟验证该方法识别结构质量及非线性恢复力的有效性。同时考虑加速度测量噪声的影响,证明了该方法的鲁棒性。     

基于UKF的简化交互多模型视频图像机动目标跟踪算法

在视频图像运动目标的状态估计与跟踪问题中,常用的扩展卡尔曼(EKF)算法简单、计算量小,但仅适用于弱非线性和弱高斯环境下.本文提出一种基于无迹卡尔曼滤波(UKF)与简化交互多模型(IMM算法相结合的视频图像运动目标跟踪算法,有效地克服了EKF算法在强非线性状态下或对小运动目标跟踪时精度低,容易发散的问题.仿真结果表明,该算法估计和跟踪非线性目标的性能明显优于基于EKF算法,其跟踪精度可达到三阶(泰勒级数展开)精度.     

基于改进扩展卡尔曼滤波算法的移动机器人定位方法研究

针对移动机器人在位姿跟踪过程中存在单一传感器或多传感器测量系统对环境信息处理能力有限的问题,结合扩展卡尔曼滤波算法,对传感器测量信息进行融合分析.对于单个传感器测得的n个观测值,扩展观测矩阵至大于n的m个目标测量值,将预测空间到测量空间的映射设计为一个具有n个非零变量、维数为nm、秩为n的变换矩阵,实现传感器对状态向量的局部更新.在建立的传感器及机器人运动模型基础上,通过地面移动机器人进行实验验证.理论分析和实验结果表明,该方法能在保证定位精度的前提下,提高算法对不同传感器类型和传感器数量的泛化能力,增强测量系统的准确性和灵活性.     

地震动反演及结构参数识别的EKF算法

讨论了信息不完备的情况下剪切型结构的参数识别问题,利用不完备的测量信息,在系统载荷未知的条件下,采用最小二乘方法反演得到地震动响应时程,用扩展卡尔曼滤波(EKF)算法识别结构的动态物理参数,解决了扩展卡尔曼滤波算法需要输入的问题,取得了满意的参数识别结果。系统仿真算例表明,在引入20%的白噪声条件下,识别结果仍具有较高的计算精度,说明EKF算法用于剪切型结构参数识别时有较强的鲁棒性。     

基于扩展卡尔曼滤波的结构参数和荷载识别研究

经典的扩展卡尔曼滤波（Extend Kalman Filter,EKF）方法可有效识别结构参数,但却需要已知外部激励,然而,在工程实际中,有些外激励往往难以实时获取。为此,该文提出了一种基于EKF的未知激励下的结构参数和荷载识别方法。通过在观测方程中引入投影矩阵,实现了结构参数的识别,同时,利用最小二乘估计实时识别了未知的外激励。为了验证该方法的有效性和鲁棒性,文中采用了三个数值算例:四层的Benchmark模型、分段线性系统和非线性Duffing系统。数值分析的结果表明,该方法不仅能够准确识别线性和非线性结构的参数,还能有效识别作用于这些结构的外激励。     

被动定向浮标阵卡尔曼滤波目标跟踪研究

文章研究利用被动定向浮标阵定位跟踪水下机动目标的方法,基于卡尔曼滤波(Kalman Filter, KF)原理提出一种定位跟踪滤波器的具体实现方法。该方法能够整合多枚浮标现在及过去有误差的测量数据,提高定位精度,同时连续输出水下目标运动参数估计从而锁定目标运动轨迹。该方法实现的关键在于建立水下目标与浮标阵的数学迭代运算模型,包括状态空间的动态与观测过程。由于被动定向浮标阵目标跟踪是一个非线性估计问题,而卡尔曼滤波器是线性的,因此文章设计了近似的线性观测方程以利用卡尔曼滤波来解决这个问题。通过计算机仿真研究该滤波器的跟踪效果并与最小二乘法进行比较,估计精度明显高于最小二乘法。同时通过仿真验证该滤波器可以自适应跟踪目标的非稳态运动过程。该方法在工程实践上具有一定应用前景与指导意义。     

基于UKF、EKF飞行器气动参数辨识方法比较研究

在航空航天工业中,气动参数辨识广泛应用于飞机气动性能测试,随着飞机性能、操纵要求的提高,以及在线辨识实时性的需要,对气动参数辨识的精度、速度有了越来越高的要求。该文对扩展卡尔曼滤波模型(EKF)、无损卡尔曼滤波模型(UKF)、飞行器气动参数辨识模型进行理论分析。而后依据固定翼飞机飞行数据,结合二维飞行器运动模型,分别应用EKF算法、UFK算法对气动参数进行辨识,对两者的辨识过程和结果进行比较,为飞行器气动参数辨识中滤波算法的选择提供借鉴。     

基于二阶转换测量Kalman滤波的目标跟踪算法(英文)

为了减小传统跟踪滤波算法线性化误差,提高光电跟踪系统的跟踪速度和跟踪精度,本文在三维空间中,提出了二阶去偏转换测量卡尔曼滤波算法.该算法利用二阶泰勒展开的方法,推导出了光电跟踪系统观测方程的转换测量值误差的均值和协方差矩阵表达式,并对测量误差进行去偏差补偿处理,再经过转换测量卡尔曼滤波,可显著减小传统滤波算法的线性化误差.仿真结果表明,二阶去偏转换测量卡尔曼滤波(SCMKF)算法的跟踪精度优于非去偏转换测量卡尔曼滤波(CMKF)和扩展卡尔曼滤波(EKF),以及unscented卡尔曼滤波(UKF)算法,并且具 有更快的收敛速度,和采用统计方法的去偏转换测量卡尔曼滤波(DCMKF)的跟踪精度相当,但计算简单,提高了跟踪速度.     

Estimation Performance for the Cubature Particle Filter under Nonlinear/Non-Gaussian Environments

This paper evaluates the state estimation performance for processing nonlinear/non-Gaussian systems using the cubature particle filter (CPF), which is an estimation algorithm that combines the cubature Kalman filter (CKF) and the particle filter (PF). The CPF is essentially a realization of PF where the third-degree cubature rule based on numerical integration method is adopted to approximate the proposal distribution. It is beneficial where the CKF is used to generate the importance density function in the PF framework for effectively resolving the nonlinear/non-Gaussian problems. Based on the spherical-radial transformation to generate an even number of equally weighted cubature points, the CKF uses cubature points with the same weights through the spherical-radial integration rule and employs an analytical probability density function (pdf) to capture the mean and covariance of the posterior distribution using the total probability theorem and subsequently uses the measurement to update with Bayes’ rule. It is capable of acquiring a maximum a posteriori probability estimate of the nonlinear system, and thus the importance density function can be used to approximate the true posterior density distribution. In Bayesian filtering, the nonlinear filter performs well when all conditional densities are assumed Gaussian. When applied to the nonlinear/non-Gaussian distribution systems, the CPF algorithm can remarkably improve the estimation accuracy as compared to the other particle filter-based approaches, such as the extended particle filter (EPF), and unscented particle filter (UPF), and also the Kalman filter (KF)-type approaches, such as the extended Kalman filter (EKF), unscented Kalman filter (UKF) and CKF. Two illustrative examples are presented showing that the CPF achieves better performance as compared to the other approaches.     

Minimum Error Entropy Based EKF for GPS Code Tracking Loop

This paper investigates the minimum error entropy based extended Kalman filter (MEEKF) for multipath parameter estimation of the Global Positioning System (GPS). The extended Kalman filter (EKF) is designed to give a preliminary estimation of the state. The scheme is designed by introducing an additional term, which is tuned according to the higher order moment of the estimation error. The minimum error entropy criterion is introduced for updating the entropy of the innovation at each time step. According to the stochastic information gradient method, an optimal filer gain matrix is obtained. The mean square error criterion is limited to the assumption of linearity and Gaussianity. However, non-Gaussian noise is often encountered in many practical environments and their performances degrade dramatically in non-Gaussian cases. Most of the existing multipath estimation algorithms are usually designed for Gaussian noise. The I (in-phase) and Q (quadrature) accumulator outputs from the GPS correlators are used as the observational measurements of the EKF to estimate the multipath parameters such as amplitude, code delay, phase, and carrier Doppler. One reasonable way to obtain an optimal estimation is based on the minimum error entropy criterion. The MEEKF algorithm provides better estimation accuracy since the error entropy involved can characterize all the randomness of the residual. Performance assessment is presented to evaluate the effectivity of the system designs for GPS code tracking loop with multipath parameter estimation using the minimum error entropy based extended Kalman filter.     

基于UKF的室内移动机器人定位技术研究

为提高室内轮式机器人定位精度,采用多传感器信息融合的机器人自主定位方法,根据室内轮式移动机器人的运动模型,建立了定位系统的状态方程;基于传感器的工作原理和数学模型,建立了各自的观测方程。鉴于二者的非线性,利用无味卡尔曼滤波算法对传感器信息进行融合。实验中开发了基于FPGA的主控平台,降低了数据处理系统的冗余度,提高了系统的稳定性。另外,设计了多传感器并行采集与快速处理的算法,提高了传感器信息融合的实时性和有效性。通过进行的机器人行走实验,结果表明该算法明显减小了定位误差,有效地提高了定位精度。     

Modeling and state of charge estimation of lithium-ion battery

Modeling and state of charge(SOC) estimation of lithium-ion(Li-ion) battery are the key techniques of battery pack management system(BMS) and critical to its reliability and safety operation.An auto-regressive with exogenous input(ARX) model is derived from RC equivalent circuit model(ECM) due to the discrete-time characteristics of BMS.For the time-varying environmental factors and the actual battery operating conditions,a variable forgetting factor recursive least square(VFFRLS)algorithm is adopted as an adaptive parameter identification method.Based on the designed model,an SOC estimator using cubature Kalman filter(CKF) algorithm is then employed to improve estimation performance and guarantee numerical stability in the computational procedure.In the battery tests,experimental results show that CKF SOC estimator has a more accuracy estimation than extended Kalman filter(EKF) algorithm,which is widely used for Li-ion battery SOC estimation,and the maximum estimation error is about 2.3%.     

A New Optimized Algorithm with Nonlinear Filter for Ultra-Tightly Coupled Integrated Navigation System of Land Vehicle

The extended particle filter (EPF) assisted by the Takagi-Sugeno (T-S) fuzzy logic adaptive system (FLAS) is used to design the ultra-tightly coupled GPS/INS (inertial navigation system) integrated navigation, which can maneuver the vehicle environment and the GPS outages scenario. The traditional integrated navigation designs adopt a loosely or tightly coupled architecture, for which the GPS receiver may lose the lock due to the interference/jamming scenarios, high dynamic environments, and the periods of partial GPS shading. An ultra-tight GPS/INS architecture involves the integration of I (in-phase) and Q (quadrature) components from the correlator of a GPS receiver with the INS data. The EPF is a particle filter (PF) which uses the extended Kalman filter (EKF) to generate the proposal distribution. The PF depends mostly on the number of particles in order to achieve a better performance during the high dynamic environments and GPS outages. The T-S FLAS is one of these approaches that can prevent the divergence problem of the filter when the precise knowledge on the system models is not available. The results show that the proposed fuzzy adaptive EPF (FAEPF) can effectively improve the navigation estimation accuracy and reduce the computational load as compared with the EPF and the unscented Kalman filter (UKF).     

基于MEMS的分散式低阶AHPRS系统设计

设计一种基于MEMS陀螺、加速度计、磁强计以及GPS模块姿态航向位置参考系统(AHPRS).首先,姿态航向参考系统主要由姿态估计卡尔曼滤波器与补偿卡尔曼滤波器构成,通过补偿滤波器周期修正姿态估计滤波器,从而弥补了由于机体的刚体运动而导致姿态角的估计误差;其次,采用分散式卡尔曼滤波器的设计思路,以估计的误差姿态角作为导航系统卡尔曼滤波器的输入量,有效降低了导航滤波方程的阶次,减小了对姿态解算计算机的性能要求;最后,通过仿真与飞行试验验证该AHPRS有效地克服了动态环境下对系统姿态估计偏差大的缺点,提高了系统的姿态航向与速度位置估计精度.