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PF算法的锈蚀RC结构抗弯承载力预测方法

为提高锈蚀钢筋混凝土(RC)结构抗弯承载力评估精度，该文综合考虑锈蚀RC结构几何尺寸、钢筋截面积及力学性能、混凝土强度、粘结性能等因素，提出了基于改进粒子滤波(PF)算法的抗弯承载力模型参数更新及预测方法。通过生成大量的粒子以表征承载力退化过程中模型参数的不确定性，从选择不同建议密度函数的角度改进PF算法以解决传统PF算法中粒子退化的问题，分别采用PF、扩展粒子滤波(EPF)、无迹粒子滤波(UPF)算法对模型参数进行估计与更新，实现了锈蚀RC结构抗弯承载力的有效预测。结果表明：随着钢筋锈蚀率的增加，RC结构的抗弯承载力逐渐降低。基于改进PF算法的锈蚀RC结构抗弯承载力预测方法因考虑了模型参数更新使得预测结果更接近试验数据。基于EKF和UKF的改进PF算法可有效抑制粒子退化，其预测精度较PF算法更高；锈蚀RC结构抗弯承载力预测精度随着训练数据及粒子数的增加而提高。     

非线性系统的小波分频的扩展Kalman滤波

基于噪声的小波变换特点,结合量测的多尺度分解和扩展Kalman滤波(EKF),提出了一种小波“最佳”尺度分解的分频EKF滤波算法。该算法依据小波变换模功率谱选择最佳小波分解尺度,并将小波多尺度分解去噪和分频EKF滤波结合起来。对实际中含强噪声的非线性动态系统进行状态估计效果较好。Monte-Carlo仿真表明,与普通EKF滤波相比,本文算法的滤波精度平均提高约10%。     

基于改进扩展卡尔曼滤波算法的静电目标跟踪

针对静电探测的数学模型结构复杂、强非线性以及实验测量数据存在极大不确定性的特点和 ExtendedKalman Filter(EKF)在处理强非线性的测量方程时会出现滤波发散的现象.为了提高滤波精度和减少计算复杂度,采用中心差分的方法计算EKF中的非线性函数的一阶导数,并结合球形静电探测器实际探测的特点形成一种新的改进的EKF算法.将改进后的EKF应用于静电目标的跟踪,建立目标跟踪滤波器.理论分析和仿真表明,采用改进后的EKF与EKF和Unscented Kalman Filter(UKF)相比较,虽然计算时间比EKF稍有增加,但比UKF的计算时间少;而计算精度比EKF有显著提高,与UKF的计算精度相当.     

基于修正时延粒子滤波的水声传感器网络目标跟踪

在水声传感器网络中,利用多个传感器节点探测到的方位信息进行目标跟踪是水下目标跟踪领域的一种新思路。由于水中声速的限制,信号到达各个节点的时间不是同步的,提出了一种修正时间延迟的方法,并将其与粒子滤波(PF)、扩展卡尔曼粒子滤波(EKPF)结合来解决该非线性跟踪问题。仿真分析表明修正时延后,算法的跟踪性能有较大提高;并且在相同条件下,EKPF的跟踪性能比PF好。     

基于高斯粒子滤波的当前统计模型跟踪算法

对于非线性系统估计问题,高斯粒子滤波器可以获得近似最优解,与粒子滤波器相比其优点是不需要重采样步骤和不存在粒子退化现象.采用高斯粒子滤波代替当前模型自适应跟踪算法中的卡尔曼滤波,将高斯粒子滤波与当前统计模型的优点相结合,提出了一种新的当前统计模型自适应跟踪算法,用于非线性非高斯系统的机动目标跟踪.MonteCarlo仿真表明,该算法跟踪精度优于标准的交互多模型算法和当前统计模型自适应跟踪算法,实时性好于交互多模型粒子滤波算法.     

基于粒子滤波的多枚声呐浮标联合跟踪定位算法

由于被动声呐浮标目标测量源的不确定性以及位置解算方程的非线性,声呐浮标联合跟踪定位面临着非线性非高斯问题,提出一种基于粒子滤波的多枚声呐浮标联合跟踪定位算法。该算法将最优贝叶斯滤波与蒙特卡洛随机采样方法相结合,在更广义的条件下实现了目标最优状态估计。算法仿真结果表明,可以较大程度的提高目标位置估计精度。     

一种自适应EPF算法及其在光纤水听器中的应用

基于椭圆拟合的相位生成载波(Phase Generated Carrier,PGC)解调方法是消除非线性因素对光纤水听器PGC解调结果影响的一种有效手段,椭圆曲线参数的最优估计问题是实现该方法的关键。扩展卡尔曼粒子滤波(Extended Kalman Particle Filter,EPF)是解决此类非线性估计问题的一种常用的最优估计算法。但传统的EPF算法在用于常参数过程方程的参数或状态估计问题时,过程噪声的方差通常设置为一个常量,这使得算法难以兼顾收敛速度和估计精度,一定程度上限制了算法的整体性能。为了解决这个问题,文章对现有的EPF进行了改进,提出了一种自适应扩展卡尔曼粒子滤波(Adaptive Extended Kalman Particle Filter,AEPF)算法。模拟仿真和实验结果表明,文中所提出的AEPF算法能根据基于椭圆拟合的PGC解调方法有效地解调出待测声信号,相比EKF算法和EPF算法,AEPF算法的收敛速度和估计精度都得到了提升。此外,文章所提出的AEPF算法也适用于其他具有常参数过程方程的参数或状态估计问题,具有一定的通用性。     

多传感器顺序统计量不敏概率数据互联算法

针对非线性系统中杂波环境下的多传感器多目标跟踪问题,提出了一种多传感器顺序统计量不敏概率数据互联算法(MSOSUPDA).算法首先根据顺序结构多传感器系统实现方法将研究问题转化为顺序处理多个非线性单传感器多目标跟踪问题,然后结合顺序统计量概率数据互联(OSPDA)的思想将单个传感器的量测点迹与多个舷迹互联,在此基础上采用不敏卡尔曼滤波(UKF)实现非线性条件下目标状态估计与协方差的递推.与MSJPDA/EKF算法相比,算法具有更高的跟踪精度和稳定性,计算量明显减小.仿真结果表明,该算该发散率与耗时分别为MsJPDA/EKF算法的19%与70%,算法综合性能明显好于MSJPDA/EKF算法.     

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.     

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。     

水声传感器网络移动节点定位技术

针对水声传感器网络的移动节点定位问题,首先研究了基于距离测量值的多边定位方法(Multilateral Localization,ML);然后利用节点运动信息,提出采用扩展卡尔曼滤波(Extended Kalman Filter,EKF)进行跟踪的方法;最后针对水下移动节点的测量值不同步问题,提出了修正扩展卡尔曼滤波(Modified Extend Kalman Filter,MEKF)以改进EKF的精度。仿真分析结果表明,MEKF的定位精度要好于EKF,而EKF和MEKF由于其用到了节点的运动信息,因此其定位精度要远好于ML。     

Kernel Entropy Based Extended Kalman Filter for GPS Navigation Processing

This paper investigates the kernel entropy based extended Kalman filter (EKF) as the navigation processor for the Global Navigation Satellite Systems (GNSS), such as the Global Positioning System (GPS). The algorithm is effective for dealing with non-Gaussian errors or heavy-tailed (or impulsive) interference errors, such as the multipath. The kernel minimum error entropy (MEE) and maximum correntropy criterion (MCC) based filtering for satellite navigation system is involved for dealing with non-Gaussian errors or heavy-tailed interference errors or outliers of the GPS. The standard EKF method is derived based on minimization of mean square error (MSE) and is optimal only under Gaussian assumption in case the system models are precisely established. The GPS navigation algorithm based on kernel entropy related principles, including the MEE criterion and the MCC will be performed, which is utilized not only for the time-varying adaptation but the outlier type of interference errors. The kernel entropy based design is a new approach using information from higher-order signal statistics. In information theoretic learning (ITL), the entropy principle based measure uses information from higher-order signal statistics and captures more statistical information as compared to MSE. To improve the performance under non-Gaussian environments, the proposed filter which adopts the MEE/MCC as the optimization criterion instead of using the minimum mean square error (MMSE) is utilized for mitigation of the heavy-tailed type of multipath errors. Performance assessment will be carried out to show the effectiveness of the proposed approach for positioning improvement in GPS navigation processing.     

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.     

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).     

Estimate-Merge-Technique-based algorithms to track an underwater moving target using towed array bearing-only measurements

Bearing-only passive target tracking is a well-known underwater defence issue dealt in the recent past with the conventional nonlinear estimators like extended Kalman filter (EKF) and unscented Kalman filter (UKF). It is being treated now-a-days with the derivatives of EKF, UKF and a highly sophisticated particle filter (PF). In this paper, two novel methods based on the Estimate Merge Technique are proposed. The Estimate Merge Technique involves a process of getting a final estimate by the fusion of a posteriori estimates given by different nonlinear estimates, which are in turn driven by the towed array bearing-only measurements. The fusion of the estimates is done with the weighted least squares estimator (WLSE). The two novel methods, one named as Pre-Merge UKF and the other Post-Merge UKF, differ in the way the feedback to the individual UKFs is applied. These novel methods have an advantage of less root mean square estimation error in position and velocity compared with the EKF and UKF and at the same time require much lesser number of computations than that of the PF, showing that these filters can serve as an optimal estimator. A testimony of the afore-mentioned advantages of the proposed novel methods is shown by carrying out Monte Carlo simulation in MATLAB R2009a for a typical war time scenario.     

基于前向神经网络的非线性时变系统辨识改进EKF算法

为了克服传统扩展卡尔曼滤波算法进行参数估计时可能产生的新数据失效问题,提出了一种改进的扩展卡尔曼滤波(EKF)步骤,然后将改进步骤做为人工神经网络的学习算法用于基于前向神经网络的非线性时变系统辨识。与传统的扩展卡尔曼滤波步骤相比克服了新数据的饱和现象,可以更好地反映系统时变特征。通过一个单变量一般时变非线性系统和一个三自由度非线性时变刚度结构系统算例,仿真验证了新算法在辨识精度和计算量方面的改进特性。