一种用于雷达量测的滤波算法

在雷达跟踪系统中,广泛使用混合坐标系下扩展卡尔曼滤波器（EKF）和转换卡尔曼滤波器（CMKF）两种算法,但是当目标距离较远时,这两种滤波器由于量测方程非线性的影响,误差较大,甚至导致滤波发射。本文计算了传统EKF滤波器引入的线性化误差,并在此基础上提出一种新的滤波算法（NDRKF）。计算机仿具结果表明,该算法减小了线性化误差的影响,较明显地改善了滤波顺的性能。     

UKF在GPS/INS伪距、伪距率组合导航中的应用

扩展卡尔曼滤波（EKF）是GPS／INS组合导航系统中常用的数据融合方式。但是EKF的线性化会带来截断误差,从而影响系统定位精度。不敏卡尔曼滤波（UKF）是一种新的非线性滤波的方法,它能减少线性化截断误差对系统定位精度的影响。文中在线性状态方程的条件下,主要研究了伪距、伪距率的非线性对系统定位性能的影响。UKF采用非线性观测方程,EKF采用线性观测方程。仿真结果表明UKF能明显改善位置项的定位精度。     

UPF算法在惯导非线性初始对准中的应用

惯性导航系统（INS）的初始对准误差模型通常为非线性的,对于估计惯导误差普遍采用的是扩展卡尔曼滤波算法（EKF）,该方法是在一阶泰勒展开的基础上近似得到的,因而误差较大。粒子滤波算法一种新颖的非线性滤波算法,它较传统的EKF算法具有稳定性好,适用范围广的优点。该文首先介绍了作为粒子滤波理论基础的递推贝叶斯估计的基本概念,说明了重要性函数对于粒子滤波器的设计是至关重要的。随后,给出了一种将不敏卡尔曼滤波（UKF）算法作为重要性函数的UPF算法,并提出将其用于静基座条件下的惯导系统非线性初始对准,通过计算机仿真对比了UPF和EKF的估计效果。仿真结果表明,UPF算法较传统的EKF算法对准时间更快,对准精度更高。     

Speed estimation of an induction motor drive using an optimizedextended Kalman filter

This paper presents a novel method to achieve good performance of an extended Kalman filter (EKF) for speed estimation of an induction motor drive. A real-coded genetic algorithm (GA) is used to optimize the noise covariance and weight matrices of the EKF, thereby ensuring filter stability and accuracy in speed estimation. Simulation studies on a constant V/Hz controller and a field-oriented controller (FOC) under various operating conditions demonstrate the efficacy of the proposed method. The experimental system consists of a prototype digital-signal-processor-based FOC induction motor drive with hardware facilities for acquiring the speed, voltage, and current signals to a PC. Experiments comprising offline GA training and verification phases are presented to validate the performance of the optimized EKF     

CDKF在GPS/SINS组合导航系统非线性模型中的应用

GPS/SINS组合导航系统模型的非线性会导致扩展卡尔曼滤波（EKF）的估计精度降低。而中心差分卡尔曼滤波（CDKF）的新型非线性滤波方法,则利用插值公式对非线性系统的状态估计进行逼近,从而减小线性化误差对系统精度的影响。针对GPS/SINS导航系统的特点,建立了一种非线性误差模型,并将EKF与CDKF分别应用于组合导航系统模型中进行仿真比较。仿真结果表明,该算法简单易实现,且能满足系统在非线性模型下的导航要求,并具有较高的精度和收敛性。     

基于扩展卡尔曼滤波器的捷联导引头刻度尺参数辨识

基于扩展卡尔曼滤波器,提出一种捷联导引头刻度尺参数辨识方法。首先,简化了在比例导引制导律作用下捷联导引头系统的非线性模型。之后,根据该非线性模型,推导扩展卡尔曼滤波方程组,并在参数估计处利用泰勒展式将其线性化。最后,在以上条件下对制导系统的稳定性进行理论分析与研究。通过数学仿真对该刻度尺参数辨识方法加以验证,仿真结果表明:应用该方法,可以快速、准确的估计捷联导引头刻度尺参数,并且有效提高了在稳定性方面制导系统对导引头刻度尺系数误差的容忍度,使系统更具鲁棒性。     

高精度惯导速度信息辅助的导引头量测误差抑制方法

 针对利用惯导信息抑制末制导导引头量测随机误差的问题,提出了一种高精度惯导速度信息辅助的扩展卡尔曼滤波方法.利用高精度惯导速度信息描述导弹自身运动,采用一阶马尔科夫过程描述目标机动,构建基于弹目信息状态变量系统的弹目相对运动模型,通过扩展卡尔曼滤波方法实现对导引头测量随机误差的抑制.新方法实现了惯导信息、导引头量测信息的融合,克服了已有滤波方法运动模型建模时需考虑导弹制导控制因素的难点.仿真实验结果验证了该方法在导弹末制导过程中的有效性.     

Joint estimation of PN code delay and multipath using the extendedKalman filter

The problem of delay estimation in the presence of multipath is considered. It is shown that the extended Kalman filter (EKF) can be used to obtain joint estimates of time-of-arrival and multipath coefficients for deterministic signals when the channel can be modeled as a tapped-delay line. Simulation results are presented for the EKF joint estimator used for synchronization in a direct-sequence spread-spectrum system operating over a frequency-selective fading channel. A simplified model of the EKF joint estimator is considered for analysis purposes. The evolution in time of the tracking error probability density function and the nonlinear tracking error variance are examined through numerical solution of the Chapman-Kolmogorov equation. The nonlinear tracking error variance is compared to both the linear error variance estimate directly provided by the EKF and the Cramer-Rao lower bound     

基于MPF的惯性导航系统快速对准技术

针对目前应用于惯性导航系统初始对准中的扩展卡尔曼滤波存在对准精度低、时间长的缺点,提出一种基于模型预测滤波的快速对准技术。该方法将惯性器件测量误差视为模型误差使用MPF进行实时预测,并以此来修正惯性导航系统的平台误差角。这样,MPF不仅有效提高了平台误差角的估计精度,而且降低了系统状态变量的维数,大大缩短了初始对准时间。仿真结果表明,MPF在平台对准精度和快速性方面均明显优于EKF,初时对准时间仅是EKF的10%,而对准精度却高于EKF。     

Quaternion-based extended Kalman filter for determining orientation by inertial and magnetic sensing

In this paper, a quaternion based extended Kalman filter (EKF) is developed for determining the orientation of a rigid body from the outputs of a sensor which is configured as the integration of a tri-axis gyro and an aiding system mechanized using a tri-axis accelerometer and a tri-axis magnetometer. The suggested applications are for studies in the field of human movement. In the proposed EKF, the quaternion associated with the body rotation is included in the state vector together with the bias of the aiding system sensors. Moreover, in addition to the in-line procedure of sensor bias compensation, the measurement noise covariance matrix is adapted, to guard against the effects which body motion and temporary magnetic disturbance may have on the reliability of measurements of gravity and earth's magnetic field, respectively. By computer simulations and experimental validation with human hand orientation motion signals, improvements in the accuracy of orientation estimates are demonstrated for the proposed EKF, as compared with filter implementations where either the in-line calibration procedure, the adaptive mechanism for weighting the measurements of the aiding system sensors, or both are not implemented.     

基于改进扩展卡尔曼滤波算法的空中目标跟踪

采用扩展卡尔曼滤波方法建立了雷达跟踪模型,对空中目标航迹进行滤波,为了减少雷达量测噪声的不稳定变化对系统跟踪性能的影响,对扩展卡尔曼滤波算法进行了改进,利用新息方差的计算来调整卡尔曼滤波器的增益。仿真结果表明,采用改进扩展卡尔曼滤波算法后,在雷达量测噪声发生大幅变化的情况下,经过滤波后的位置和速度误差仍然趋于稳定。表明该方法具有很好的滤波性能及跟踪精度,并可以提高空中目标航迹预测的精确性。     

双被动雷达交会跟踪的精度分析与跟踪算法

多平台的被动传感器交会(融合)跟踪系统的功能就是将呈网状分布的多被动传感器测量到的目标的角度量测信息进行融合,以得出目标的运动状态.本文基于无偏估计的Cramer-Rao 不等式,分析了运动平台上的双被动雷达交会跟踪目标时不同通信带宽情况下的估计精度问题,并且建立了对机动目标交会跟踪的扩展卡尔曼滤波(EKF)跟踪算法.     

Solution of large dense complex matrix equations using a fastFourier transform (FFT)-based wavelet-like methodology

Wavelet-like transformations have been used in the past to compress dense large matrices into a sparse system. However, they generally are implemented through a finite impulse response filter realized through the formulation of Daubechies (1992). A method is proposed to use a very high order filter (namely an ideal one) and use the computationally efficient fast Fourier transform (FFT) to carry out the multiresolution analysis. The goal here is to reduce the redundancy in the system and also guarantee that the wavelet coefficients drop off much faster. Hence, the efficiency of the new procedure becomes clear for very high order filters. The advantage of the FFT-based procedure utilizing ideal filters is that it can be computationally efficient and for very large matrices may yield a sparse matrix. However, this is achieved, as well known in the literature, at the expense of robustness, which may lead to a larger reconstruction error due to the presence of the Gibb's phenomenon. Numerical examples are presented to illustrate the efficiency of this procedure as conjectured in the literature     

Location prediction algorithm for a nonlinear vehicular movement in VANET using extended Kalman filter

Vehicular ad-hoc network (VANET) is an essential component of the intelligent transportation system, that facilitates the road transportation by giving a prior alert on traffic condition, collision detection warning, automatic parking and cruise control using vehicle to vehicle (V2V) and vehicle to roadside unit (V2R) communication. The accuracy of location prediction of the vehicle is a prime concern in VANET which enhances the application performance such as automatic parking, cooperative driving, routing etc. to give some examples. Generally, in a developed country, vehicle speed varies between 0 and 60 km/h in a city due to traffic rules, driving skills and traffic density. Likewise, the movement of the vehicle with steady speed is highly impractical. Subsequently, the relationship between time and speed to reach the destination is nonlinear. With reference to the previous work on location prediction in VANET, nonlinear movement of the vehicle was not considered. Thus, a location prediction algorithm should be designed by considering nonlinear movement. This paper proposes a location prediction algorithm for a nonlinear vehicular movement using extended Kalman filter (EKF). EKF is more appropriate contrasted with the Kalman filter (KF), as it is designed to work with the nonlinear system. The proposed prediction algorithm performance is measured with the real and model based mobility traces for the city and highway scenarios. Also, EKF based prediction performance is compared with KF based prediction on average Euclidean distance error (AEDE), distance error (DE), root mean square error (RMSE) and velocity error (VE).     

扩展卡尔曼滤波在无源定位中的应用研究

阐述了卡尔曼滤波(KF)和扩展卡尔曼滤波(EKF)的原理和方法,建立了无源定位系统的状态模型和观测模型,推导了将非线性观测模型线性化,并利用EKF进行递推滤波估计的步骤和公式。通过计算机仿真,验证了运用EKF算法解决基于方位角及其变化率测量信息的无源定位方法,结果表明,运用EKF滤波算法,可以实现单观测站对运动目标的无源定位,初始状态估计误差对定位收敛的性能有较大影响。     

基于UT的混合粒子滤波单站无源定位算法

为利用无源固定单站对运动辐射源快速定位,将粒子滤波和UT(unscented transformation)应用于单站无源定位,给出了一种基于UT的角度约束采样混合粒子滤波无源定位算法,该算法从UKF滤波得到建议分布,从该建议分布采样时引入角度测量对状态变量的约束,可以减少粒子滤波用于高维情况时所需的粒子数目,改善滤波性能.与EKF、UKF(unscented kalman filter)以及基于EKF的混合粒子滤波算法的仿真比较表明,本文算法在滤波收敛速度、跟踪精度以及稳定性方面优于其它算法,估计误差可以接近Cramer-Rao下界.     

A digital DS spread-spectrum receiver with joint channel andDoppler shift estimation

A digital spread-spectrum receiver design is presented for communication over multipath channels with severe Doppler shifts. The characteristics of the underwater channel relevant to spread-spectrum system design are discussed, and a channel model for short-range communications (less than 10 km) is defined. The receiver considered uses a digital coherent RAKE combiner, coupled with an extended Kalman filter (EKF)-based estimator for channel parameters and pseudonoise code delay. Receiver performance is evaluated by computing average bit-error rate (BER) versus iterations of the EKF joint estimator, using both fixed and time-varying channels. It is shown that the BER obtained using the EKF joint estimator closely tracks the optimum BER obtained when the channel, delay, and Doppler parameters are known exactly. Finally, the Cramer-Rao lower bound for time-invariant joint channel, delay, and Doppler estimation is derived, and compared with the ensemble averaged mean-squared error of the EKF estimator     

Mobility Tracking in Cellular Networks Using Particle Filtering

Mobility tracking based on data from wireless cellular networks is a key challenge that has been recently investigated both from a theoretical and practical point of view. This paper proposes Monte Carlo techniques for mobility tracking in wireless communication networks by means of received signal strength indications. These techniques allow for accurate estimation of mobile station's (MS) position and speed. The command process of the MS is represented by a first-order Markov model which can take values from a finite set of acceleration levels. The wide range of acceleration changes is covered by a set of preliminary determined acceleration values. A particle filter and a Rao-Blackwellised particle filter are proposed and their performance is evaluated both over synthetic and real data. A comparison with an extended Kalman filter (EKF) is performed with respect to accuracy and computational complexity. With a small number of particles the RBPF gives more accurate results than the PF and the EKF. A posterior Cramer Rao lower bound (PCRLB) is calculated and it is compared with the filters' root- mean-square error performance.     

压电陶瓷定位系统电容传感器容错控制

针对压电陶瓷定位系统中电容传感器故障对定位精度的影响,对使用扩展卡尔曼滤波(EKF)进行容错控制的方法进行了研究。以传感器采样电路故障和掉电故障为对象,对三阶轨迹规划算法下电容传感器的EKF滤波公式进行了分析,提出以离散化迭代计算的EKF代替传统的将非线性系统线性化的方法。在压电陶瓷定位系统实验平台上,使用激光干涉仪作为测量基准,在传感器采样电路故障和掉电故障的情况下,实现了500μm行程,绝对精度小于3.5μm,误差小于0.7%的定位控制。结果表明,基于EKF的电容传感器容错控制可以有效减小传感器故障引起的控制误差,增加压电陶瓷定位系统的鲁棒性。     

Performance Analysis of Nonlinear-Predictive-Filter-Based Chaotic Synchronization

Synchronization of chaotic systems is essential for chaotic communication schemes. If the communication environment is modeled stochastic, the extended-Kalman-filter-(EKF)-based synchronization scheme possesses minimum error in estimating the states for certain chaotic systems/maps. However, its intrinsic nonlinear approximation error can cause divergence and, hence, desynchronization. In this brief, a nonlinear predictive filter (NPF) is proposed for chaotic synchronization. The condition for stability and an approximate expression for the total normalized mean square error are derived. Numerical evaluations reveal that the proposed NPF-based synchronization scheme has better performance compared with the EKF-based scheme.