大失准角下SINS的KF/EKF2混合滤波对准

针对捷联惯导系统(SINS)大失准角下滤波对准过程中非线性滤波器状态维数过大的问题,提出了一种基于模型分解的卡尔曼滤波/二阶扩展卡尔曼滤波(KF/EKF2)混合滤波方法,将基于欧拉平台误差角的非线性滤波模型分解为线性部分和非线性部分,分别采用线性KF滤波和非线性EKF2滤波处理,并且设计了混合滤波的滤波步骤。实验结果表明,KF/EKF2混合滤波算法在计算量、实时性及精度等方面优于最常用的无迹卡尔曼滤波(UKF)和EKF2滤波。     

基于最优导频的自适应Kalman滤波信道估计方法

针对时变信道中的子载波间干扰(ICI)和噪声的统计模型不准确引起的滤波发散问题,介绍了一种基于最优导频预滤波的自适应Kalman联合算法。该算法通过使用最优导频滤除ICI,获得理想信道初始状态,然后将其作为Kalman滤波初始信息在时域上进行自适应Kalman信道估计。最后仿真实验表明,和传统的基于导频的Kalman滤波(KF)算法相比,该方法能有效抑制KF发散和改善信道估计精度。     

Particle filtering based channel estimation in OFDM power line communication

Particle filtering （PF） algorithm has the powerful potential for coping with difficult non-linear and non-Gaussian problems. Aiming at non-linear, non-Gaussian and time-varying characteristics of power line channel, a time-varying channel estimation scheme combined PF algorithm with decision feedback method is proposed. In the proposed scheme, firstly the indoor power line channel is measured using the pseudo-noise （PN） correlation method, and a first-order dynamic autoregressive （AR） model is set up to describe the measured channel, then, the channel states are estimated dynamically from the received signals by exploiting the proposed scheme. Meanwhile, due to the complex noise distribution of power line channel, the performance of channel estimation based on the proposed scheme under the Middleton class A impulsive noise environment is analyzed. Comparisons are made with the channel estimation scheme respectively based on least square （LS）, Kalman filtering （KF） and the proposed algorithm. Simulation indicates that PF algorithm dealing with this power line channel estimation difficult non-linear and non-Gaussian problems performance is superior to those of LS and KF respectively, so the proposed scheme achieves higher estimation accuracy. Therefore, it is confirmed that PF algorithm has its own unique advantage for power line channel estimation.     

EKF、UKF、PF目标跟踪性能的比较

雷达系统的非线性目标跟踪已被人们广泛重视。扩展卡尔曼滤波器（EKF）是将卡尔曼滤波器（KF）局部线性化，其算法简单、计算量小，适用于弱非线性、高斯环境下。不敏卡尔曼滤波器（UKF）是用一系列确定样本来逼近状态的后验概率密度，在高斯环境中，对任何非线性系统都有较好的跟踪性能。粒子滤波器（PF）是用随机样本来近似状态后验概率密度函数，适用于任何非线性非高斯系统。文中通过仿真实验，对三者的性能进行了仿真比较，结果证明在复杂的非高斯非线性环境中，粒子滤波器的性能明显优于另外两种滤波器，但计算复杂，耗时长。     

Stochastic noise tolerance: Enhanced full state observer vs. Kalman filter from video tracking perspective

A control-based full state observer scheme is explored for video target tracking application, and is enhanced with a lowpass filter for improving the tracking precision, thus forming an Enhanced Full State Observer (EFSO). The whole design is based on the given lab-generated video sequence with motion of an articulate target. To evaluate the EFSO’s stochastic noise tolerance, a Kalman Filter (KF) is intentionally employed in tracking the same target with the given Gaussian white noises. The comparison results indicate that, for system noises of certain statistics, the proposed EFSO has its own noise resistance capacity that is superior to that of KF and is more advantageous for implementation.     

SINS静基座初始对准的超球体采样STFUKF算法

初始对准是实现惯性导航高精度的一项关键技术。无迹滤波(UKF)在SINS系统静基座大方位失准角初始对准中计算量大,在不精确或错误的噪声统计情况下,收敛速度变慢,估计精度下降,甚至滤波发散。针对这一问题,将超球体采样与强跟踪无迹滤波(STFUKF)算法相结合,提高了运算速度和对准精度。利用SINS的非线性误差模型,通过数字仿真将卡尔曼滤波、UKF和STFUKF的性能进行比较,证明该方法具有精度高、抗干扰性好、跟踪能力强的特点。     

AKF与EFRLS在动态目标跟踪性能上的比较

卡尔曼滤波是具有递推估计形式的最优滤波,但最优性的获得是在过程噪声和观测噪声统计特性已知的前提下得到的。然而,在大量的动态目标跟踪实际问题中噪声具有不确定性,因而有必要研究在噪声不确定下动态目标的跟踪算法以满足实际问题的需要。文中介绍自适应Kalman滤波对过程噪声方差的估计以及推广的遗忘因子最小二乘法对状态估计的递推公式,并且在平均误差最小准则下通过计算机仿真比较两种方法对动态目标的跟踪性能.仿真结果表明,在不确定噪声下自适应Kalman滤波能够取得比推广的遗忘因子递推最小二乘法更好的跟踪性能。     

Robust state estimator design for a solar battery charging system

This article describes a robust state estimator design for a solar battery charger where there is significant noise in the output measurements of the solar array voltage that causes degradation in the performance of the maximum power point tracking. The application of the extended Kalman filter, to the photovoltaic system, can lead to enhanced state estimation results so that a recursive solution can be obtained to achieve the most accurate estimate from the noisy signals. Additionally, as a consequence of applying the Kalman filter (KF), the immeasurable state of the inductor current can be estimated without a current sensor. The proposed controller uses the estimated solar array voltage for maximum power point tracker, and the estimated inductor current for determining the battery current controller. The methods for system modeling and design of the extended KF are presented, and the experimental results verify the validity of the proposed system.     

粒子滤波方法在GPS/DR组合导航系统中的应用

成功地将粒子滤波方法应用干GPS／DR组合导航系统中。如果GPS信号受到干扰或者车辆做大幅度机动时，卡尔曼滤波会有较大的误差。粒子滤波不仅考虑了客观样本信息，还考虑了主观因素，能很好地处理这种观测样本出现异常的情况，具有鲁棒性。实验证明，当GPS信号被遮挡时，粒子滤波优于卡尔曼滤波。     

Tracking application about singer model based on marginalized particle filter

This article deals with the problem of maneuvering target tracking which results in a mixed linear/non-linear model estimation problem.For maneuvering tracking system,extended Kalman filter (EKF) or particle filter (PF) is traditionally used to estimate the states.In this article,marginalized particle filter (MPF) is presented for application in a mixed linear/non-linear model estimation problem.MPF is a combination of Kalman filter (KF) and PF.So it holds both advantage of them and can be used for mixed linear/non-linear substructure,where the conditionally linear states are estimated using KF and the nonlinear states are estimated using PF.Simulation results show that MPF guarantees the estimation accuracy and alleviates the potential computational burden problem compared with PF and EKF in maneuvering target tracking application.     

基于自适应卡尔曼滤波的侧滑移动机器人运动模型估计

精确实时在线的运动模型对于侧滑移动机器人的运动控制和轨迹规划至关重要,相比于离线模型估计,该文在基于速度瞬心(ICRs)的侧滑移动机器人运动学模型基础上,采用扩展卡尔曼滤波(EKF),在同一特定地形下在线准确得到ICRs的参数值;并针对不同的地形情况,采用k-近邻法对地形进行分类,实时判别机器人当前运行的路面,采用自适应的卡尔曼滤波器(AKF)调整滤波器参数。仿真和实验对比表明,该方法在同一地形和变化地形下均能快速估计出侧滑移动机器人的运动学模型,收敛时间均为3 s以内,可以满足实际使用的需要。     

扩展容积卡尔曼滤波-卡尔曼滤波组合算法

传统单一线性或非线性滤波方法往往难以获得最优线性/非线性混合动态系统状态估计,针对这一问题,结合卡尔曼滤波（KF）方法可获得线性状态估计最优解、计算量小等优势,提出了一种基于KF和扩展容积卡尔曼滤波（A-CKF）的组合滤波方法。该方法将系统状态分解为线性状态与非线性状态两部分,分别采用KF和简化两次扩展容积卡尔曼滤波（STA-CKF）方法进行系统状态估计。机动目标跟踪和捷联惯性导航系统非线性对准仿真结果表明,相比于Rao-Blackwellized粒子滤波方法,新方法在保证滤波精度的前提下,使得计算成本有效降低;相比于STA-CKF方法,新方法在滤波精度和滤波实时性方面均得到明显提高。     

Estimation of hidden state variables of the Intracranial system using constrained nonlinear Kalman filters

Impeded by the rigid skull, assessment of physiological variables of the intracranial system is difficult. A hidden state estimation approach is used in the present work to facilitate the estimation of unobserved variables from available clinical measurements including intracranial pressure (ICP) and cerebral blood flow velocity (CBFV). The estimation algorithm is based on a modified nonlinear intracranial mathematical model, whose parameters are first identified in an offline stage using a nonlinear optimization paradigm. Following the offline stage, an online filtering process is performed using a nonlinear Kalman filter (KF)-like state estimator that is equipped with a new way of deriving the Kalman gain satisfying the physiological constraints on the state variables. The proposed method is then validated by comparing different state estimation methods and input/output (I/O) configurations using simulated data. It is also applied to a set of CBFV, ICP and arterial blood pressure (ABP) signal segments from brain injury patients. The results indicated that the proposed constrained nonlinear KF achieved the best performance among the evaluated state estimators and that the state estimator combined with the I/O configuration that has ICP as the measured output can potentially be used to estimate CBFV continuously. Finally, the state estimator combined with the I/O configuration that has both ICP and CBFV as outputs can potentially estimate the lumped cerebral arterial radii, which are not measurable in a typical clinical environment.     

空间目标卡尔曼滤波稀疏成像方法

鉴于卡尔曼滤波器(KF)具有优良的信号估计性能,将KF与贪婪算法相结合,该文给出稀疏约束下的基于KF的空间目标逆合成孔径雷达(ISAR)成像方法。考虑到有些空间目标尺寸较大或包含大尺寸部件,或成像积累时间较长,会引入越分辨单元走动(MTRC)和方位向2次相位调制,首先对回波进行MTRC校正,然后构建包含2次相位的观测矩阵,通过使图像锐度最大化,估计目标转动角速度,获得聚焦目标图像,并将估计转速用于方位向图像定标。卫星仿真ISAR数据处理验证了上述成像处理方法的有效性。成像效果优于传统距离多普勒(RD)和正交匹配追踪(OMP)方法。     

Angular acceleration estimation and feedback control: An experimental investigation

Angular acceleration estimation and its application in acceleration feedback control are investigated experimentally in the paper. In combination of Newton Predictor (NP) with Kalman Filter (KF), a new predictive estimator for angular acceleration, called Newton Predictor Enhanced Kalman Filter (NPEKF) is proposed. This estimator provides a wide bandwidth and a small phase lag of the estimated acceleration while attenuating noises. Based on the estimated acceleration an acceleration feedback control (AFC) is presented for multiple degree-of-freedom (DOF) mechatronic system. The design of AFC is specified in terms of its stability and ability in suppressing dynamic disturbances. Experiments are conducted on a 2-DOF direct-drive manipulator. The frequency responses of the acceleration estimated by NP, KF and NPEKF are compared with those of the measured acceleration via linear accelerometer. The performance of AFC using the estimated acceleration is assessed against that using the measured acceleration. This study has shown that the proposed NPEKF estimator is able to supply the AFC with reliable required acceleration.     

新型自适应Kalman滤波算法及其应用

为防止滤波发散和提高系统的实时性,提出了一种新的自适应Kalman滤波算法.该算法利用滤波异常判据获得一个滤波状态因子,通过滤波状态因子确定量测噪声协方差阵的值,在线调整噪声的统计特性实现自适应滤波.将该算法应用到惯导/双星组合导航系统中,并和常规Kalman滤波和简化的Sage-Husa自适应滤波算法进行仿真比较.仿真结果表明,在滤波精度与简化Sage-Husa自适应滤波相当的情况下,新算法简化了运算,提高了实时性.     

Iterative Soft-Kalman Filter-based Data Detection and Channel Estimation for Turbo Coded MIMO–OFDM Systems

MIMO channels are often assumed to be constant over a block or packet. This assumption of block stationarity is valid for many fixed wireless scenarios. However, for communications in a mobile environment, the stationarity assumption will result in considerable performance degradation. In this paper, we focus on a new channel estimation technique for Turbo coded MIMO systems using OFDM. In the proposed MIMO–OFDM system, pilots are placed on selected subcarriers and used by a pair of Kalman filter (KF) channel estimators at the receiver. The KF channel estimates are then utilized by a MIMO–OFDM soft data detector based on the computationally efficient QRD-M algorithm. The soft detector output is fed back to the Kalman filters to iteratively improve the channel estimates. The extrinsic information generated by the Turbo decoder is also used as a priori information for the soft data detector. The overall receiver thus combines MIMO data detection, KF-based channel estimation, and Turbo decoding in a joint iterative structure yielding computational efficiency and improved bit-error rate (BER) performance. Parts of this paper were presented at ICC’2005, Seoul, Korea. This work was supported in part by NSF Grant No. CCF-0429596. This work was done when he was with the Nokia Research Center in Dallas, USA.     

On the use of first-order autoregressive modeling for Rayleigh flat fading channel estimation with Kalman filter

This letter deals with the estimation of a flat fading Rayleigh channel with Jakes's spectrum. The channel is approximated by a first-order autoregressive (AR(1)) model and tracked by a Kalman filter (KF). The common method used in the literature to estimate the parameter of the AR(1) model is based on a correlation matching (CM) criterion. However, for slow fading variations, another criterion based on the minimization of the asymptotic variance (MAV) of the KF is more appropriate, as already observed in few works (Barbieri et al., 2009 [1]). This letter gives analytic justification by providing approximated closed-form expressions of the estimation variance for the CM and MAV criteria, and of the optimal AR(1) parameter.     

SOI-KF: Distributed Kalman Filtering With Low-Cost Communications Using the Sign of Innovations

When dealing with decentralized estimation, it is important to reduce the cost of communicating the distributed observations-a problem receiving revived interest in the context of wireless sensor networks. In this paper, we derive and analyze distributed state estimators of dynamical stochastic processes, whereby the low communication cost is effected by requiring the transmission of a single bit per observation. Following a Kalman filtering (KF) approach, we develop recursive algorithms for distributed state estimation based on the sign of innovations (SOI). Even though SOI-KF can afford minimal communication overhead, we prove that in terms of performance and complexity it comes very close to the clairvoyant KF which is based on the analog-amplitude observations. Reinforcing our conclusions, we show that the SOI-KF applied to distributed target tracking based on distance-only observations yields accurate estimates at low communication cost     

Effects of combined additional indium deposition and potassium fluoride post‐deposition treatments on Cu(In,Ga)Se2 thin film solar cells

The effect of additional indium on copper indium gallium selenide (CIGS) thin films and solar cells was investigated with respect to potassium fluoride post‐deposition treatment (KF‐PDT) using current‐voltage, external quantum efficiency, scanning electron microscopy, X‐ray photoelectron spectroscopy, time‐resolved photoluminescence and capacitance‐voltage measurements. The cell performance, particularly open‐circuit voltage (V _oc) improved drastically by the combined treatments of additional indium deposition after CIGS growth and subsequent KF‐PDT. A Cu deficient layer at the CIGS surface increased after both treatments rather than only KF‐PDT. Photoluminescence intensity, lifetime and net carrier concentration of KF‐untreated CIGS solar cells did not change significantly by only additional indium deposition. However, they improved because of the combined treatments. Copyright © 2017 John Wiley & Sons, Ltd.