四频差动激光陀螺随机误差建模与滤波研究

根据四频差动激光陀螺随机误差的特性,对实验采集的某型四频差动激光陀螺的静态输出数据,使用p阶自回归q阶滑动平均模型(ARMA(p,q))进行分析,并采用BIC准则确定模型的阶数。基于建立的ARMA(p,q)模型,使用卡尔曼滤波对四频差动激光陀螺的静态输出数据进行滤波处理,检验结果表明,该方法能明显减小四频差动激光陀螺的随机误差。     

温度约束的MEMS陀螺零漂补偿模型

针对现有MEMS零位随机漂移的缺陷,本文建立关于温度约束的确定性模型MEMS陀螺零位漂移补偿模型。首先,依据MEMS陀螺信号的测量模型,将陀螺信号误差分解为确定性误差和随机性误差,针对由温度引入的确定性误差,建立温度-零偏和温度-主频率分量确定性约束模型,有效消除信号序列中的温度引入趋势项和辨识周期项;其次,利用自回归滑动平均模型(Auto-Regressive and Moving Average Model,简称为ARMA模型)逼近MEMS陀螺信号中的随机误差项,准确地预测出随机误差的变化趋势;最后,采用Kalman滤波优化ARMA模型的预测效果,进一步提高模型的状态估计精度。理论分析和实验结果验证了该模型的鲁棒性和有效性。     

光纤陀螺随机漂移的建模与滤波研究

时间序列模型是对光纤陀螺(FOG)随机漂移进行建模的一种重要方法.传统的时间序列建模方法难以应用于实时建模,且模型精度较低.因此,提出了适用于高精度FOG随机漂移的改进自回归整合移动平均模型(ARIMA),并基于该模型建立了FOG随机漂移的实时Kalman滤波器.实验结果表明,该改进ARIMA模型能较准确地描述FOG的随机漂移;Allan方差分析结果表明,与基于传统自回归移动平均模型(ARMA)的Kalman滤波结果相比,基于该模型的Kalman滤波对减小光纤陀螺的5项主要随机误差更有效.     

基于新息突变约束的自适应卡尔曼滤波研究

针对微机电系统(MEMS)陀螺仪易受影响且随机误差较大,导致建立模型不准确和测量精度低的问题,该文提出了一种改进的自适应卡尔曼滤波方法。首先建立ARMA模型,在传统卡尔曼算法中引入衰减系数以减小系统旧值的影响,同时引入基于系统新息突变的预测误差矩阵清除系统的突变值。使用Allan方差对原始陀螺仪数据和滤波后的陀螺仪数据进行分析对比。结果表明,实验所用陀螺仪的角度随机游走、零偏不稳定性和角速率随机游走至少小了1个数量级,标准差明显减小,这表明改进算法有效抑制了随机噪声,提高了MEMS的性能。     

光纤陀螺随机漂移的建模与滤波

光纤陀螺的随机漂移是捷联式惯导系统的主要误差源,为了减小光纤陀螺的随机误差并提高其精度,需要对光纤陀螺的随机误差进行精确建模,本文根据时间序列理论,采用自回归AR模型法,建立了光纤陀螺随机误差模型。根据该模型,采用卡尔曼滤波算法实现了对光纤陀螺的随机误差的滤波。滤波结果和Allan方差分析表明,滤波后光纤陀螺随机误差得到了明显地减小,光纤陀螺的精度得到了有效地提高。     

基于SHAKF滤波器的MEMS陀螺随机误差建模补偿

MEMS陀螺随机漂移误差是制约惯性导航精度的关键因素。本文针对标准kalman滤波器陀螺漂移处理方法中,随机动态系统的结构参数和噪声统计特性参数不准确的问题,采用自适应SHAKF(Sage-Husa Adaptive Kalman Filter)滤波器进行参数实时估计,提高陀螺漂移精度。基于此思想,建立了ARMA随机误差模型,搭建了MEMS陀螺组件实验系统,通过高精度三轴转台静态测试采集陀螺数据。Aallan方差分析表明,零偏不稳定性经线性KF滤波后提升17.4%,经自适应SHAKF滤波后提升26.2%。     

基于时间序列模型的雷达数据随机误差建模与补偿

针对雷达数据随机误差超差的问题,分析了非平稳时间序列自回归求和滑动平均(ARIMA)模型,并以雷达某次实测国际空间站数据的前4 000点数据建立ARIMA模型,设计了基于此模型的Kalman滤波器,利用所设计滤波器对雷达前4 000点数据和剩余数据分别进行了滤波处理,补偿后误差为原数据的13.7%和20.1%。结果表明:该方法能有效降低雷达测量数据随机误差,提高数据质量。     

基于状态扩增的MEMS陀螺随机误差实时滤波研究

针对一般民用MEMS陀螺仪精度较低的缺点以及传统MEMS随机误差时间序列建模需进行零均值化处理且不能进行在线处理建模的问题,提出一种基于状态扩增的随机误差实时滤波方法,将时间序列的均值作为未知数,给ARMA模型增加一个截距项,并利用该模型采用扩增状态的方法设计卡尔曼滤波及自适应卡尔曼滤波器,使得测量数据不需满足零均值的条件。静态及摇摆试验表明,所提方法能大幅提高MEMS陀螺仪精度。     

基于时间序列分析的弹道目标进动周期提取

利用微波暗室静态测量数据,反演了某型弹道目标在典型战情下的动态雷达散射截面积(RCS)时间序列.分析表明:该序列具有非平稳性、相关性和拟周期性的特点,直接利用谱分析方法提取进动周期的效果较差.采用B样条函数、广义自回归条件异方差(GARCH)模型和自回归-滑动平均模型(ARMA模型)构造了RCS序列的迭合滤波分解模型....     

基于改进粒子滤波和自适应融合的目标跟踪

针对复杂环境下视频目标跟踪精确度低的问题,提 出了一种基于混合迭代无迹粒子滤波(HI-UPF)和关联系数自 适应融合的目标跟踪算法。首先采用统计线性回归的方法对无迹变换进行优化,提出了HI- UPF,不 仅提升了滤波精度,而且有效降低了算法的时间消耗;其次基于关联系数,采用一种自适应 融合方法,实现了加性 融合和乘性融合的自适应切换,并根据关联系数提出一种改进的自适应加性融合方法。仿真 实验表明,本文方法对 于复杂条件下的目标跟踪具有较高的精度和较强的鲁棒性。     

半球谐振陀螺仪随机漂移数学模型研究

半球谐振陀螺仪在实际工作过程中会受到各种随机因素的干扰,存在随机误差,影响其工作精度.该文针对半球谐振陀螺仪输出中的随机漂移,结合使用小波分解、自相关分析和平稳时间序列建模等方法,对半球谐振陀螺仪的测试数据进行处理,建立了其随机漂移的AR模型,并利用残差检验法对模型的有效性进行了检验,为半球谐振陀螺仪的随机误差补偿提供了参考依据.     

MEMS陀螺的抗野值自适应滤波降噪方法

针对微机电系统(MEMS)陀螺随机漂移较大及量测信息中野值对滤波的不利影响,提出了一种抗野值自适应滤波降噪方法。该方法采用Allan方差信息估计量测噪声方差参数,避免了Kalman滤波器与量测噪声估值器之间的相互关联,能有效抑制滤波发散。在此基础上引入新息抗野值算法,通过修正新息去除野值的不利影响,增强对随机漂移的滤波效果。实测数据试验结果表明,采用该文方法滤波后的MEMS陀螺输出信号均方差及角度随机游走都比滤波前明显降低,验证了提出的滤波方法在MEMS陀螺降噪中的有效性。     

An Approximate Statistical Analysis of the Widrow LMS Algorithm with Application to Narrow-Band Interference Rejection

The Widrow LMS algorithm is considered for the implementation of an adaptive prewhitening filter in a direct-sequence (DS) spread-spectrum receiver. Exact expressions for the steady-state tapweight covariance matrix and resulting average excess mean square error are developed for the real LMS algorithm when the input contains a random binary sequence (used to model a pseudonoise spreading sequence). It is shown here that the output samples of the adaptive filter possess approximately Gaussian statistics under the conditions of slow convergence and a large number of filter taps. Using this approximation, expressions for the resulting bit error rate (BER) when the adaptive algorithm is used to suppress a fading gone jammer are developed, and numerical results obtained from these expressions are compared to simulation results for the DS receiver.     

Parametric modeling of somatosensory evoked potentials

In this paper, we examine methods of characterizing somatosensory evoked potentials (SEP's) in both the time and frequency domains. We have found that the truncated impulse response (TIR) method produced an accurate time domain model of the SEP signals at model orders greatly reduced from the original state space matrix. The TIR method was valuable for smoothing signals that were slightly corrupted by noise. In this case, the simulated data sequence was close to the original data sequence in the mean squared error sense. For signals that were greatly corrupted by noise, the TIR method was not able to perform as well. Therefore, the TIR method was not a feature extraction method but was valuable for data simulation. In the frequency domain, we have used the autoregressive moving average model (ARMA) to parameterize the SEP signal. An overdetermined set of Yule-Walker equations was created to determine the autoregressive (AR) parameters of the original data with the model order established by the singular value decomposition. From these AR parameters, a residual time series was generated which was used to find the moving average parameters. The resulting ARMA model was used to produce a simulated data sequence. The frequency domain characteristics of the simulated sequence and the corresponding power spectral density of the ARMA filter were very close to the periodogram of the original data sequence. Accurate parameterization was achieved for the SEP waveforms at low filter lengths.     

基于自适应信息融合的导航系统构成与算法研究

由于组合导航系统应用环境的不确定性,给噪声统计特性的准确描述带来困难,这将造成Kalman滤波器不稳定甚至发散,目前常用的解决办法是直接估计系统噪声与量测噪声的方差阵 Q及R ,进行自适应滤波.但方程的增加将使计算量加大、实时性不能保证.本文在对基于信息融合的INS/GPS组合导航系统进行分析和设计的基础上,探讨了通过ARMA模型自适应参数辨识求解可变增益K,从而求出状态估计值的方法,并对辨识误差协方差的防饱和算法进行了研究.计算机仿真结果表明:该算法对提高导航精度和运算速度是行之有效的,所得结论有一定的工程实用价值.     

Inherent matrix identities on ARMA lattice filter realizationalgorithm and their application

Presents the matrix identities that are inherent in the solution of the normal equations for an ARMA lattice filter. This derivation also makes clear the relationship between the recursive least squares (RLS) method and the ARMA lattice filter realization algorithm. Further, as an application of the matrix identities, a new method for model identification with frequency weighting (MIFW) is presented     

单站无源定位中的抗野值鲁棒CKF算法

单站无源定位系统的测量噪声中如果出现野值,会影响滤波器的估计精度和稳定性,严重时还会导致滤波器发散。针对这一问题,基于Bayes定理并结合归一化受污染正态模型,提出了一种抗野值鲁棒容积卡尔曼滤波算法。该算法采用球面径向积分原则直接计算非线性函数的均值和方差,并对测量误差建立一个归一化的受污染正态模型,然后根据野值出现的后验概率来自适应调整测量预测残差的方差阵。结合空频域单站无源定位模型进行仿真实验表明,该算法可以较好地抑制测量噪声中的离散或成片连续野值的不利影响,具有较强的鲁棒性。     

ARMA implementation of diffraction operators with inverse-rootsingularities

The integral of a time-domain diffraction operator which has an integrable inverse-root singularity and an infinite tail is numerically differentiated to get a truncated digital form of the operator. This truncated difference operator effectively simulates the singularity but is computationally inefficient and produces a convolutional truncation ghost. The authors therefore use a least-squares method to model an equivalent autoregressive moving-average (ARMA) filter on the difference operator. The recursive convolution of the ARMA filter with a wavelet has no truncation ghost and an error below 1% of the peak diffraction amplitude. Design and application of the ARMA filter reduces computer (CPU) time by 42% over that repaired with direct convolution. A combination of filter design at a coarse spatial sampling, angular interpolation of filter coefficients to a finer sampling, and recursive application reduces CPU time by 83% over direct convolution or 80% over Fourier convolution, which also has truncation error