改进的自适应Kalman滤波在SINS/GPS组合导航中的应用

全球定位系统（GPS）因信号受到遮挡和干扰而产生观测量突然失准,使捷联式惯性导航系统（SINS）/GPS组合导航的卡尔曼滤波器性能急剧下降。针对上述问题,提出了一种改进的自适应卡尔曼滤波的方法,通过失准时的新息对先验状态均方误差阵进行自适应调节,解决了新息协方差与实际严重不符的问题。仿真实验中,对比了传统的卡尔曼滤波、自适应卡尔曼滤波、自适应抗差卡尔曼滤波和改进的自适应卡尔曼滤波的估计性能,证明了所提出算法的有效性。     

基于新息自适应卡尔曼滤波的加速度计信号降噪

针对加速度计信号降噪中精度有限、噪声统计规律不能完全确定等实际问题,提出了一种新的自适应卡尔曼滤波算法.利用新息自适应估计出了系统噪声和量测噪声的协方差阵,采用不同滑动窗口宽度设计了一组并行滤波器,通过加权优化获得了一个综合白适应滤波器,从而使该降噪方法不仅具有噪声白适应估计能力,而且对新息方差估计所需的滑动窗口宽度的选取进行了优化.理论上推导了该降噪算法的基本过程,并进行了加速度计实测数据降噪试验.试验结果表明,该降噪方法能获得较好的滤波效果,降噪后加速度计信号的噪声方差强度减少了5个数量级.     

多新息抗差-自适应卡尔曼滤波定位算法研究

针对移动机器人在定位过程中,由传感器测量误差和机器人模型引起的位姿误差导致系统定位精度急剧下降的问题,提出了一种多新息卡尔曼滤波算法.在标准卡尔曼滤波的基础上,当传感器测量值存在误差时,引入抗差权因子,通过改变误差测量值的权值提高滤波器的估计精度;当机器人位姿存在误差时,引入自适应因子,通过调整状态协方差矩阵的大小抵制位姿误差引起的滤波发散.同时,引入了多新息,即多个时刻的新息向量,进一步提高此非线性系统的精度.实验表明:当存在测量误差和位姿误差时,该滤波算法能有效提高定位精度.     

基于多新息Kalman滤波的TLD改进算法

针对跟踪检测学习(tracking learning detection,TLD)跟 踪算法中目标被遮挡后跟踪失败以及跟踪精度不高的问题,本文提出基于多新息Klaman滤波的TLD改进算法,在原始TLD跟踪算法的基础上加入了多新息Klaman滤波算法。改进算法对跟踪目标建模,将TLD跟踪算法 的结果作为系统当前状态的观测值,结合多新息Kalman滤波算法的预测值,最优化检测结果 ,作为当前帧中目标的跟踪位置。通过实验对原始TLD和改进后的TLD算法进行比较,通过在标准测试序列的实验验证,加入多新息Kalman滤波的TLD改进算法与原始TLD算法相比,其跟踪误差更小,而且实现了对跟踪目标被遮挡后 的位置预测。     

基于新息偏差的自适应机动目标跟踪算法

证明了在某一目标机动输入下为保持跟踪最优,标准卡尔曼滤波器的过程噪声协 方差阵的调整公式,并利用观测新息在目标机动中发生正偏或负偏的信息,设计了一种自适 应跟踪算法．仿真说明该算法具有运算量小,跟踪精度高的特点．     

基于极大似然估计的新息自适应滤波算法

针对噪声统计信息未知或时变情况下常规卡尔曼滤波估计精度下降甚至发散的问题,提出了一种基于极大似然估计的新息自适应滤波算法.算法对基于极大似然估计的常规新息协方差估值器进行限定记忆指数衰减加权修正,增加滑动窗口内新近新息协方差序列的利用权重;根据新息自适应原理,利用新息协方差估计值直接计算滤波增益矩阵,加快滤波器收敛速度的同时提高了滤波算法的估计精度.算法应用于捷联惯性导航系统/全球定位系统(SINS/GPS)组合导航系统,仿真实验表明:在噪声统计信息未知或时变情况下,算法具有更强的鲁棒性以及更高的滤波精度.     

自适应Kalman滤波器及其应用

对于带未知噪声统计的单输出系统,本文提出了一种新的自适应Kalman滤波器.应用现代时间序列分析方法,基于ARMA新息模型的滑动平均(MA)参数的在线辨识,提出了稳态最优Kalman滤波器增益估计的一种新算法,比Mehra的算法简单.同时还提出了辨识滑动平均(MA)模型参数的一种新的自适应Kalman滤波算法.此外,给出了在雷达跟踪系统中的应用,且仿真结果说明了本文算法的有效性.     

基于多新息理论的 EKF 改进算法

针对标准的扩展卡尔曼滤波算法（EKF）在强非线性系统中估计精度较低的问题,提出了一种改进的扩展卡尔曼滤波算法（MI-EKF）,使得滤波精度得到很大的提高。MI-EKF 是在标准 EKF 基础上,结合多新息理论,不仅考虑了系统当前的测量值,而且也充分考虑了之前时刻的有用信息,从而使得 MI-EKF 的滤波精度和稳定性得到改善。最后,讨论了新息数量对改进算法精度的影响,仿真结果表明包含两个新息的 MI-EKF 算法滤波效果最佳。     

混合式自适应Kalman滤波算法

采用虚拟噪声补偿模型误差和有偏的噪声方差估值器、滤波器收敛性判据相结合的方法来解决自适应Kalman滤波发散的问题。首先若模型不准确,则引入虚拟噪声对模型误差进行虚拟补偿,然后采用有偏的噪声方差估值器、滤波器收敛性判据对噪声方差估计值进行监控,阻止滤波器发散。采用混合式自适应Kalman滤波算法对Gill公司的风向风速仪实时采集的数据进行处理,实验结果表明,该方法能有效的提高性能、抑制滤波发散,具有较强的实用性、自适应能力。     

基于新息偏差的观测奇异值自适应滤波算法

针对在机动目标跟踪过程中出现的观测奇异值,提出了一种对观测奇异值进行自适应滤波的算法。依据多传感器数据形成的对目标状态参数的正确描述和测量数据集合主体的变化趋势,利用新息偏差(innovation deviation)实时、准确地识别测量数据中的奇异点,并进行自适应滤波。仿真结果表明,在分布式多传感器目标跟踪的航迹融合处理中,该算法可以快速、有效地解决奇异值自适应滤波的问题。     

一种基于卡尔曼滤波的队列长度自适应算法

传统主动队列管理(AQM)算法在处理传感器网络突发流时具有响应速度慢、抗网络突变性能弱的缺点.针对此问题,提出了一种新的AQM算法,算法首先将队列长度作为早期拥塞检测参量,运用卡尔曼滤波理论预测队列长度;其次根据队列长度在缓冲区的占用比来划分网络状态;最后根据不同占用比采取相应的丢包策略,自适应地调整丢包率,当出现网络突变时,加大调整幅度,使队列长度保持在理想区间.仿真实验表明:新算法能够较好地适应网络波动,提高网络服务质量(QoS),算法综合性能优于主流AQM算法.     

On the application of a hybrid ellipsoidal-rectangular interval arithmetic algorithm to interval Kalman filtering for state estimation of uncertain systems

Modelling uncertainty is a key limitation to the applicability of the classical Kalman filter for state estimation of dynamic systems. For such systems with bounded modelling uncertainty, the interval Kalman filter (IKF) is a direct extension of the former to interval systems. However, its usage is not yet widespread owing to the over-conservatism of interval arithmetic bounds. In this paper, the IKF equations are adapted to use an ellipsoidal arithmetic that, in some cases, provides tighter bounds than direct, rectangular interval arithmetic. In order for the IKF to be useful, it must be able to provide reasonable enclosures under all circumstances. To this end, a hybrid ellipsoidal-rectangular enclosure algorithm is proposed, and its robustness is evidenced by its application to two characteristically different systems for which it provides stable estimate bounds, whereas the rectangular and ellipsoidal approaches fail to accomplish this in either one or the other case.     

A majorization inequality and its application to distributed Kalman filtering

In the analysis of a recently proposed distributed estimation algorithm based on the Kalman filtering and on gossip iterations, we needed to apply a new inequality which is valid for i.i.d. matrix valued random processes. This inequality can be useful in the analysis of the convergence rate of general jump Markov linear systems.In this paper, we present this inequality. This is based on the theory of majorization and on its use in the analysis of the singular values. Finally we will show the impact of this inequality on the performance analysis of gossip based distributed Kalman filters.     

A mixture-of-experts framework for adaptive Kalman filtering

This paper proposes a modular and flexible approach to adaptive Kalman filtering using the framework of a mixture-of-experts regulated by a gating network. Each expert is a Kalman filter modeled with a different realization of the unknown system parameters such as process and measurement noise. The gating network performs on-line adaptation of the weights given to individual filter estimates based on performance. This scheme compares very favorably with the classical Magill filter bank, which is based on a Bayesian technique, in terms of: estimation accuracy; quicker response to changing environments; and numerical stability and computational demands. The proposed filter bank is further enhanced by periodically using a search algorithm in a feedback loop. Two search algorithms are considered. The first algorithm uses a recursive quadratic programming approach which extremizes a modified maximum likelihood function to update the parameters of the best performing filter in the bank. This particular approach to parameter adaptation allows a real-time implementation. The second algorithm uses a genetic algorithm to search for the parameter vector and is suited for post-processed data type applications. The workings and power of the overall filter bank and the suggested adaptation schemes are illustrated by a number of examples.     

An optimization approach to adaptive Kalman filtering

In this paper, an optimization-based adaptive Kalman filtering method is proposed. The method produces an estimate of the process noise covariance matrix Q by solving an optimization problem over a short window of data. The algorithm recovers the observations h(x) from a system without a priori knowledge of system dynamics. Potential applications include target tracking using a network of nonlinear sensors, servoing, mapping, and localization. The algorithm is demonstrated in simulations on a tracking example for a target with coupled and nonlinear kinematics. Simulations indicate superiority over a standard MMAE algorithm for a large class of systems.     

基于自适应Kalman滤波的改进RTQC算法

为对原始雷达数据进行空间配准,对原始的实时质量控制方法(real time quality control,RTQC)进行了研究.在传统的RTQC算法的基础上,加入Kalman滤波,用于对随机误差进行处理,得出雷达测量的固定误差.对Kalman算法进行改进,改进了传统的Kalman滤波算法,通过对量测噪声方差矩阵和模型噪声方差矩阵进行实时估计,减少了Kalman滤波对于方差矩阵先验知识的要求,提高了Kalman滤波的自适应性,并初步分析了算法的有效性.与实际数据的对比实验结果表明了该方法的有效性.     

On unscented Kalman filtering with state interval constraints

This paper addresses the state-estimation problem for nonlinear systems for the case in which prior knowledge is available in the form of interval constraints on the states. Approximate solutions to this problem are reviewed and compared with new algorithms. All the algorithms investigated are based on the unscented Kalman filter. Two illustrative examples of chemical processes are discussed. Numerical results suggest that the use of constrained unscented filtering algorithms improves the accuracy of the state estimates compared to the unconstrained unscented filter, especially when a poor initialization is set. Moreover, it is shown that the constrained filters that enforce the state interval constraint on both the state estimate and error covariance yield more accurate state estimates than the methods that enforce such constraint only on the state estimates.     

On the identification of variances and adaptive Kalman filtering

A Kalman filter requires an exact knowledge of the process noise covariance matrixQand the measurement noise covariance matrixR. Here we consider the case in which the true values ofQandRare unknown. The system is assumed to be constant, and the random inputs are stationary. First, a correlation test is given which checks whether a particular Kalman filter is working optimally or not. If the filter is suboptimal, a technique is given to obtain asymptotically normal, unbiased, and consistent estimates ofQandR. This technique works only for the case in which the form ofQis known and the number of unknown elements inQis less thann times rwherenis the dimension of the state vector andris the dimension of the measurement vector. For other cases, the optimal steady-state gain Kopis obtained directly by an iterative procedure without identifyingQ. As a corollary, it is shown that the steady-state optimal Kalman filter gain Kopdepends only onn times rlinear functionals ofQ. The results are first derived for discrete systems. They are then extended to continuous systems. A numerical example is given to show the usefulness of the approach.     

改进的自适应中值滤波算法

中值滤波窗口大小影响滤波器性能,3×3滤波窗口可以很好地保持图像细节。提出一种新的自适应中值滤波方法。将3×3窗口中心的极值点作为候选噪声点,若候选噪声点仍然是7×7窗口的极值点,则该点即是噪声点。若以噪声点为中心的3×3滤波窗口的中值不是噪声,则噪声用中值替换。重复以上过程,直到没有噪声点被替换。如果图像中仍然存在大的噪声团块,则噪声用相邻的三个信号点的灰度均值替换。实验结果表明,该方法能够有效去除脉冲噪声,并在抑制噪声的同时很好地保护图像的细节。