粒子滤波在机动目标跟踪中的应用

机动目标跟踪问题一直是数据融合领域的难点.本文提出将粒子滤波应用于跟踪机动目标.该算法不仅可解决非线性问题,且对机动目标有很好的跟踪效果.仿真实验结果表明了该滤波器的有效性.     

一种改进的迭代无迹卡尔曼滤波算法

针对非线性系统目标跟踪中状态估计的线性问题,在滤波过程的不同部分,利用统计和分析原理对状态估计进行线性化,提出一种改进的迭代无迹卡尔曼滤波(Improved Iterated Unscented Kalman Filter, IIUKF)。在系统方程和测量方程都具有较严重的非线性条件下,与无迹卡尔曼滤波器(UKF)和迭代扩展卡尔曼滤波(IEKF)进行仿真验证比较。结果显示该方法的跟踪性能优于UKF和IEKF,提高了系统的跟踪效果。     

卡尔曼粒子滤波的视频车辆跟踪算法研究

近年来,视频车辆跟踪作为城市智能交通系统（ITS）的一个关键技术受到关注。本文针对传统粒子滤波的非线性、非高斯性可能导致跟踪过程的不稳健性,提出一种基于卡尔曼粒子滤波的视频车辆跟踪算法,该算法利用基于重要区域的目标颜色直方图统计模型对视频车辆目标进行建模,并将其应用于卡尔曼滤波更新中,通过采用Mean Shift算法将卡尔曼滤波器引用到粒子滤波器当中,对车辆的运行轨迹进行校正,实现了局部线性滤波,实现了在保持跟踪系统整体上的非线性、非高斯性的同时,兼顾其局部的线性高斯特性。实验结果表明,本文所提出的方法与传统粒子滤波方法相比,能够更准确地对车辆进行跟踪,同时保证了在复杂环境下性能的稳健性。     

双基阵纯方位目标跟踪无偏最小二乘估计算法

纯方位目标跟踪是非线性估计问题,伪线性估计算法是主要的纯方位目标跟踪算法之一,其计算简单,但对测量方程进行线性化处理后其运动要素的估计会出现发散或偏置现象.为了克服伪线性估计算法的估计有偏性,本文提出一种带约束条件的最小二乘估计算法,使得伪线性化后的均方等效误差最小来实现目标运动要素的无偏估计.通过仿真说明了利用该方法所得到的位置、速度跟踪误差曲线能很好快速地逼近CRLB,比双基阵EKF滤波器及伪线性估计算法有着更好的收敛速度和跟踪精度,证明了该方法的有效性.     

高斯混合交互式多模型容积信息滤波算法

针对机动目标跟踪中由于目标机动使系统的非线性强度增大,导致系统的线性误差增大和跟踪精度明显下降、甚至发散的问题,提出了基于高斯混合的交互式多模型容积信息滤波（ GMIMM-CIF）算法,实现对机动目标的精确跟踪。新算法在每次输入交互之后,保留概率较大的几个假设,并利用一个高斯混合项替换最优多模型算法中剩余的假设,从而使算法中假设的数量保持恒定;用容积信息滤波器（ CIF）代替传统的非线性滤波器,通过估计信息状态向量和信息矩阵而不是估计状态向量和协方差,可以减小系统的非线性误差。通过仿真对比实验,验证了该算法可以提高机动目标的跟踪精度。     

分布式伪线性卡尔曼滤波纯方位跟踪北大核心CSCD

针对纯方位跟踪(BOT)的非线性滤波和距离可观测性较差问题,提出了一种新的分布式多传感器辅助变量伪线性卡尔曼滤波器(DM-IVPLKF)。该滤波器利用辅助变量伪线性卡尔曼滤波器(IVPLKF)独立处理目标测量值,通过偏差补偿伪线性卡尔曼滤波器(偏差补偿PLKF)解决由于量测向量与伪线性噪声相关而产生的偏差,将递归辅助变量估计方法嵌入偏差补偿PLKF中,对目标状态估计和协方差进行修正。所提算法利用多传感器最优信息融合准则,对目标状态进行融合估计。然后,推导了多传感器BOT的克拉默-拉奥下界(CRLB)。通过仿真实验,将所提算法与传统算法进行对比,仿真结果证明了所提算法具有较高的跟踪精度。     

Cubature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cubature卡尔曼滤波-卡尔曼滤波算法(CKF-KF),分别应用CKF和KF估计模型中的非线性和线性状态.该算法对非线性与线性状态均进行cubature采样,并将两种样本通过线性方程和量测方程进行传播,以获得非线性状态估计.机动目标跟踪仿真结果表明,CKF-KF的估计精度比Rao-Blackwellized粒子滤波器(RBPF)略低,但算法运行时间不到其1％;与无迹卡尔曼滤波器(UKF-KF)相比,估计精度相当,但算法运行时间降低了22％,有效地提高了实时性.     

永磁同步电动机的平滑跟踪伺服控制及实现

针对永磁同步电动机位置伺服控制的最小时间响应和平滑轨迹跟踪问题,提出一种改进的二阶离散非线性平滑轨迹跟踪滤波器,该滤波器的输出适合于伺服系统的前馈控制.结合永磁同步电动机的(d,q)数学模型,采用线性控制实现电流闭环使机电运动系统等效为典型的二阶系统,利用非线性平滑轨迹跟踪滤波器为电流环提供前馈输入,并设计一个降阶的扰动观测器保证了系统具有较强的鲁棒性.利用DSP(TM S320LF2407A)实现算法并与传统的PID算法进行了比较,通过仿真和实验结果证明了所提方法的正确性和有效性.     

Rao-Blackwellized粒子势均衡多目标多伯努利滤波器

由于多伯努利滤波器直接近似递推了多目标状态的后验概率密度,使得多目标跟踪问题在基于随机有限集理论框架下的求解及目标状态的估计显得更为直观.本文针对一个状态可分解(线性/非线性)的状态空间模型,分析基于Rao-Blackwell定理的滤波估计方法,结合噪声的去相关构造线性状态的滤波方程.文中详细推导并提出Rao-Blackwellized粒子势均衡多目标多伯努利滤波器的一般实现形式,包括给出多伯努利非线性状态粒子滤波的实现形式,并结合非线性滤波结果给出多伯努利线性状态的递推滤波公式.本文提出的滤波器实现方法能够在更低维的状态空间上进行采样,滤波器的整体跟踪性能得到提高.多目标跟踪的仿真实验结果验证了该算法的有效性.     

一种带多步随机延迟量测高斯滤波器的一般框架解

提出了一种适用于线性和非线性系统的带多步随机延迟量测高斯滤波器的一般框架解. 为了完成状态的递归更新估计, 噪声向量和先前时刻状态向量被扩展到当前时刻状态向量中. 然后基于贝叶斯方法推导了扩展后状态向量的一般框架解. 对于非线性系统, 通过利用不同的数值计算方法计算贝叶斯解中的高斯加权积分可以推导获得不同的高斯近似滤波器. 最后本文利用三阶球径容积准则来实施提出的方法, 并通过量测被随机延迟多步的目标跟踪模型对所提出的方法进行了仿真, 仿真结果验证了提出方法的有效性和优点.     

Target tracking based on the extended H-infinity filter in wireless sensor networks

In this paper, we propose a new target tracking approach for wireless sensor networks (WSNs) by using the extended H-infinity filter. First, the extended H-infinity filter for nonlinear discrete-time systems is deduced through the Krein space analysis scheme. Then, the proposed extended H-infinity filtering algorithm is applied to target tracking in wireless sensor networks. Finally, experiments are conducted through a small wireless sensor network test-bed. Both experimental and simulation results illustrate that the extended H-infinity filtering algorithm is more accurate to track a moving target in wireless sensor networks than using the extended Kalman filter in the case of having no knowledge of the statistics of the environment and the target to be tracked.     

磁偶极子跟踪的渐进贝叶斯滤波方法

提出一种新的非线性滤波器应用于磁偶极子目标跟踪问题.建立了跟踪问题的状态空间模型, 在此基础上, 从高斯矩近似误差的角度分析了现有卡尔曼滤波更新在磁偶极子跟踪中的问题.对此, 将贝叶斯更新过程等效为求解连续时间上的渐进贝叶斯问题, 在线性高斯条件下推导了其解析解, 表明渐进贝叶斯更新可等效为定常系统的Kalman-Bucy滤波器; 进一步采用一阶Taylor展开得到非线性近似解表达式, 导出一种渐进贝叶斯滤波器, 从理论上分析了与现有方法的异同.仿真与实测磁目标跟踪试验结果表明, 渐进贝叶斯滤波器具有良好的精度和收敛性, 能够有效抑制磁目标跟踪中由于大初始误差导致的性能下降和滤波发散, 且计算效率与扩展卡尔曼滤波器相当, 适于实际应用.     

WSNs下一种自适应多传感器协同目标跟踪策略*

对WSNs中机动目标跟踪问题提出一种自适应多传感器协同跟踪策略.该策略能根据目标的移动位置,动态地唤醒无线传感器网络中部分传感器节点形成分簇,并选择合适的簇首和采样间隔进行目标跟踪.簇内节点通过协作感知以及测量信息融合,提高了跟踪精度,同时自适应可变采样间隔节约了通信能量和计算资源,满足了跟踪系统的实时性要求.提出了传感器网络能量均衡分配的指标,提高了网络的可靠性.由于模型的非线性和目标运动的机动性,采用IMM滤波器进行目标状态估计.仿真结果表明,与NSSS和DGSS相比,跟踪精度明显提高;与DCSS相比,在保证一定跟踪精度的同时,节约了能量消耗.     

WSNs下一种自适应多传感器协同目标跟踪策略*

对WSNs中机动目标跟踪问题提出一种自适应多传感器协同跟踪策略。该策略能根据目标的移动位置,动态地唤醒无线传感器网络中部分传感器节点形成分簇,并选择合适的簇首和采样间隔进行目标跟踪。簇内节点通过协作感知以及测量信息融合,提高了跟踪精度,同时自适应可变采样间隔节约了通信能量和计算资源,满足了跟踪系统的实时性要求。提出了传感器网络能量均衡分配的指标,提高了网络的可靠性。由于模型的非线性和目标运动的机动性,采用IMM滤波器进行目标状态估计。仿真结果表明,与NSSS和DGSS相比,跟踪精度明显提高;与DCSS相比     

一类非线性系统输出偏差的伪分离估计算法

首先把Friedland的用于线性时不变系统定常偏差的分离估计算法推广到一类存有输出偏差的非线性系统,得到了一种处理定常偏差的伪分离估计算法,随后,应用作者提出的正交性原理,又提出一种可估计大范围时变偏差的伪分离估计算法,进一步发展了Friedland的偏差分离估计理论,使得扩展Kalman滤波器更便于实际应用,仿真实例证实了本文算法的有效性。     

An enhanced linear Kalman filter (EnLKF) algorithm for parameter estimation of nonlinear rational models

In this study, an enhanced Kalman Filter formulation for linear in the parameters models with inherent correlated errors is proposed to build up a new framework for nonlinear rational model parameter estimation. The mechanism of linear Kalman filter (LKF) with point data processing is adopted to develop a new recursive algorithm. The novelty of the enhanced linear Kalman filter (EnLKF in short and distinguished from extended Kalman filter (EKF)) is that it is not formulated from the routes of extended Kalman Filters (to approximate nonlinear models by linear approximation around operating points through Taylor expansion) and also it includes LKF as its subset while linear models have no correlated errors in regressor terms. No matter linear or nonlinear models in representing a system from measured data, it is very common to have correlated errors between measurement noise and regression terms, the EnLKF provides a general solution for unbiased model parameter estimation without extra cost to convert model structure. The associated convergence is analysed to provide a quantitative indicator for applications and reference for further research. Three simulated examples are selected to bench-test the performance of the algorithm. In addition, the style of conducting numerical simulation studies provides a user-friendly step by step procedure for the readers/users with interest in their ad hoc applications. It should be noted that this approach is fundamentally different from those using linearisation to approximate nonlinear models and then conduct state/parameter estimate.     

非线性系统的多扩展目标跟踪算法

目前扩展目标跟踪算法大都假设其系统为线性高斯系统，针对非线性系统的多扩展目标跟踪问题，提出了采用粒子滤波技术对目标状态和关联假设进行联合估计的多扩展目标跟踪算法。首先，提出了将多扩展目标状态和关联假设进行联合估计的思想，解决了在估计目标状态和数据关联时相互牵制的问题；其次，根据扩展目标演化模型、量测模型建立多扩展目标状态和关联假设的联合建议分布函数，并利用粒子滤波技术实现联合估计的Bayes框架；最后，为解决直接采用粒子滤波实现时存在的维数灾难问题，将目标联合状态粒子的产生和演化分解为各个目标状态粒子的产生和演化，对每个目标的粒子集根据与其相关的权重单独进行重抽样，这样在抑制目标状态估计较差部分的同时使每个目标都保留了对其状态估计较好的粒子。仿真实验结果表明，与扩展目标概率假设密度滤波器的高斯混合实现方式和序贯蒙特卡洛实现方式相比，所提算法的状态估计精度较高，形状估计的Jaccard距离分别降低了30%、20%左右，更适合于非线性系统的多扩展目标跟踪。     

Adaptive tracking of multiple hot-spot target IR images

In the recent past, the capability of tracking dynamic targets from forward-looking infrared (FLIR) measurements has been improved substantially, by replacing standard correlation trackers with adaptive extended Kalman filters. This research investigates a tracker able to handle "multiple hot-spot" targets, in which digital (or optical) signal processing is employed on the FLIR data to identify the underlying target shape. This identified shape is then used in the measurement model portion of the filter as it estimates target offset from the center of the field-of-view. In this algorithm, an extended Kalman filter processes the raw intensity measurements from the FLIR to produce target estimates. An alternative algorithm uses a linear Kalman filter to process the position indications of an enhanced correlator in order to generate tracking estimates; the enhancement is accomplished not only by thresholding to eliminate poor correlation information, but also by incorporating the dynamics information from the Kalman filter and the on-line identification of the target shape as a template instead of merely using previous frames of data. The performance capabilities of these two algorithms are evaluated under various tracking environment conditions and for a range of choices of design parameters.     

General two-stage extended Kalman filters

A general two-stage extended Kalman filter (GTSEKF), which extends the linear general two-stage Kalman filter to nonlinear systems, is further proposed. A new nonlinear two-stage transformation is introduced to facilitate achieving this extension. As in the linear one, the GTSEKF is derived mainly by applying the nonlinear two-stage transformation to the well-known extended Kalman filter (EKF), and is shown to be equivalent to the EKF with a decoupled computing structure. A nonlinear filter for estimating constant parameters in dynamic systems is presented to illustrate one application of the proposed GTSEKF. A literature example is also given to demonstrate the correctness and usefulness of the proposed results.     

Gaussian approximate filter for stochastic dynamic systems with randomly delayed measurements and colored measurement noises带随机延迟量测和有色量测噪声随机动态系统的高斯近似滤波器

In this paper, a new Gaussian approximate (GA) filter for stochastic dynamic systems with both one-step randomly delayed measurements and colored measurement noises is presented. For linear systems, a Kalman filter can be obtained to include one-step randomly delayed measurements and colored measurement noises. On the other hand, for nonlinear stochastic dynamic systems, different GA filters can be developed which exploit numerical methods to compute Gaussian weighted integrals involved in the proposed Bayesian solution. Existing GA filter with one-step randomly delayed measurements and existing GA filter with colored measurement noises are special cases of the proposed GA filter. The efficiency and superiority of the proposed method are illustrated in a numerical example concerning a target tracking problem.