枪弹头痕迹自动比对方法的研究

研究了枪弹头痕迹自动比对方法,用最优化的方法消除测量误差,通过中值滤波去除随机噪声,并根据数据的相似性度量理论确定了以横向相关性为基础的比对方案.     

中国季度GDP数据核算和修订过程的统计分析

GDP数据核算、修订程序的有效性是影响GDP数据准确性的重要因素之一,故有必要对GDP数据核算和修订程序进行统计分析。基于2003-2015年季度GDP核算和修订的动态数据,本文构建中国季度GDP数据修订状态空间模型,并以此为基础对GDP初步核算数据的准确性进行检验。此外,本文还分析了核算误差的性质和来源、数据修订的目的和方法,并进而对数据核算和修订程序的有效性进行评估。结果显示:中国季度GDP实际累计同比增长率的核算和修订程序都是有效的,修改同比增长率的核算方法能消除核算误差的“溢出效应”;而实际GDP环比增长率的初步核算数据不是真实值的有效估计,只有修订核算程序才能消除核算误差。相较于已有研究,本文对GDP数据核算、修订动态过程进行了全面研究,在澄清关于中国GDP核算数据有效性质疑同时,也指出了改善数据核算、修订程序有效性的方向。     

考虑时变状态约束与输入饱和的PMSM随机系统指令滤波控制

针对考虑时变状态约束和输入饱和的永磁同步电机随机系统的位置跟踪控制问题,提出了一种基于障碍Lyapunov函数的指令滤波反步控制方案.首先,构造障碍Lyapunov函数以保证电流、转速等状态量不违反时变约束条件.随后,利用模糊逻辑理论处理电机随机系统中的未知非线性项.此外,采用了指令滤波技术与误差补偿机制相结合的方法,不仅解决了传统反步法中出现的“计算爆炸”问题,而且消除了滤波误差的影响.仿真结果表明该控制器能有效抑制输入饱和与随机扰动的影响,提高系统的控制性能,同时能够保证电机系统所有状态在给定的约束范围内.     

反褶积中的脉冲紧缩准则

反褶积方法被广泛地用于天文学、地震勘探、雷达信号处理、遥感图象处理等领域.这类物理过程可用如下表示:x_k=w_k*P_k+n_k,(1)其中,w_k 为源信号,P_k 为反射函数,n_k 为附加噪声,x_k 是观测信号,*表示褶积运算.由于实际中仅知道 x_k,故(1)中解 w_k(或 P_k)不具有唯一性.设{x_t}为滤波输入,τ=1,2,…,n_x;{f_t}为双向预测误差滤波器,t=-n_b,…,n_a;{y_t}为预测误差(输出),t=-n_b+1,…,n_x+n_a.滤波系统可写为     

小波在股市数据分析中的应用

本文把股票日收益率时间序列看作一维时间信号 ,构造了新的小波母函数作为滤波器 ,对此时间信号做滤波处理 ,消除了数据的奇异性 ,从而可以宏观地预测股票日收益率走势 .     

样条滤波

<正> 飞机飞行,其轨迹一般是一条空间曲线.用雷达进行观察,记录下有关数据.这些数据点在图上,是一条忽上忽下、弯曲得不成样子的平面曲线(假定在水平面进行投影).这是由于观察误差造成的.我们需要的是飞机的真实飞行曲线.有了它,我们才能了解飞机执行某项任务的某些情况.在由于误差而歪曲的观察曲线中,恢复出飞机的真实飞行曲线叫滤波.飞机的真实飞行曲线叫信号.雷达实测数据叫观察.观察等于信号加干扰.因此,滤波一般是从观察出发,削弱干扰,以修复出未知的信号.观察,是被干扰污染了的信号.滤波实现的最重要方法之一是多项式回归(或称多项式滤波).即用某一阶多项式去逼近观察曲线.用最小二乘法定出此多项式的系数.该多项式就是对信号的估计.这就是滤波的最终结果.     

并行准高斯高阶递归滤波算法研究

三维变分同化系统中一个重要的问题是背景误差协方差矩阵B及其逆的求解.背景误差协方差矩阵的水平变换部分采用递归滤波运算,可以简化矩阵的求解,解决了背景误差协方差矩阵B及其逆难以求解的问题.本文对准高斯高阶递归滤波的算法原理和过程进行了深入研究.因为递归滤波并行的低可扩展性制约了高阶递归滤波算法在三维变分同化系统中的应用,所以本文提出了阶段二维区域剖分并行化方法,实现了并行准高斯高阶递归滤波算法库.数值试验表明,四阶递归滤波1次的效果明显优于一阶4次的滤波效果;并且高阶递归滤波并行算法64核时能达到大约50倍的加速,并行效率高达78%,具有良好的加速效果和较强的可扩展性.     

基于小波分解和ANFIS模型的CPI统计数据预测

介绍应用小波分析理论解决时间序列统计数据的测量误差消除问题,实例证明借助离散小波分解与重构手段,可有效地从误差干扰的统计数据序列中提取统计数据的原始特征.完成CPI经济序列数据预测,为CPI统计数据的误差消除引入一种有效方法.     

基于方根分解形式的带衰减因子UKF算法应用

UKF作为一种新的非线性滤波方法已在目标跟踪问题中得到应用,在状态的时间更新阶段直接使用非线性模型,不引入线性化误差,而且不必计算Jacobians矩阵.提出了一种基于方根分解形式的带有衰减因子的UKF算法(SRDMA-UKF),算法的方根形式增加了数字稳定性和状态协方差的半正定性.通过衰减因子的引入加强对当前测量数据的利用,减小历史数据对滤波的影响.仿真实验结果表明,该算法与UKF算法相比具有更好的滤波性能.     

多四旋翼无人机分布式固定时间自适应编队控制

针对具有外部扰动和参数不确定性的多四旋翼无人机,提出了基于指令滤波反步方法的分布式固定时间自适应编队控制算法.通过引入固定时间指令滤波及构建非光滑误差补偿机制,有效地避免了“复杂性爆炸”问题并在固定时间内移除了滤波误差的影响.此外,通过设计分段函数,巧妙地解决了分布式控制器中存在的奇异性问题.利用固定时间稳定性理论,严格证明了闭环系统是实际固定时间稳定的,且闭环系统中所有信号有界,同时编队跟踪误差在固定时间内收敛到原点附近充分小的邻域内.最后,仿真算例验证了所提出的分布式固定时间控制策略的有效性.     

Approximate filter for a two-dimensional nonlinear diffusion observed in a correlated low noise channel

The purpose of this article is to study a nonlinear filtering problem when the signal is a two-dimensional process from which only the second component is noisy and when only its first (and unnoisy) component is observed in a correlated low noise channel. We propose an approximate finite-dimensional filter and we prove that the filtering error converges to zero. The order of magnitude of the error between the approximate filter and the optimal filter, as the observation noise vanishes, is computed.     

A weighted averaging method for treating discontinuous spectral data

We introduce a weighted averaging method for improving the inevitable error induced by the Gibbs phenomenon appearing in a spectral approximation for a discontinuous function. In the result we have a family of filters generalizing the well known Fejer filter. In addition, for high resolution recovery, we propose an adaptive filter which is competitive with the existing exponentially convergent adaptive filter. Several numerical examples are included to show the applicability of the method presented.     

On the optimal design of the output transformation for discrete-time linear systems

The optimization of the output matrix for a discrete-time, single-output, linear stochastic system is approached from two different points of view. Firstly, we investigate the problem of minimizing the steady-state filter error variance with respect to a time-invariant output matrix subject to a norm constraint. Secondly, we propose a filter algorithm in which the output matrix at timek is chosen so as to maximize the difference at timek+1 between the variance of the prediction error and that of the a posteriori error. For this filter, boundedness of the covariance and asymptotic stability are investigated. Several numerical experiments are reported: they give information about the limiting behavior of the sequence of output matrices generated by the algorithm and the corresponding error covariance. They also enable us to make a comparison with the results obtained by solving the former problem.This work was supported by the Italian Ministry of Education (MPI 40%), Rome, Italy.     

A rule-based development of incremental models

In the study, we propose a concept of incremental fuzzy models in which fuzzy rules are aimed at compensating discrepancies resulting because of the use of a certain global yet simple model of general nature (such as e.g., a constant or linear regression). The structure of input data and error discovered through fuzzy clustering is captured in the form of a collection of fuzzy clusters, which helps eliminate (compensate) error produced by the global model. We discuss a detailed architecture of the proposed rule-based model and present its design based on an augmented version of Fuzzy C-Means (FCM). An extended suite of experimental studies offering some comparative analysis is covered as well.     

Application of Laguerre polynomials to invert a semi-infinite convolution

We propose a spectral method of inverting one- and two-dimensional semi-infinite convolutions using the Laguerre polynomials. We construct systems of linear algebraic equations to determine the coefficients of the corresponding Fourier-Laguerre series. We analyze the computational error in the coefficients of the orthogonal series that arise as a result of errors in the data. Applying the Laguerre polynomials makes it possible to eliminate the discretization procedure, which may take the solution of the perturbed equation outside the region of well-posedness. Translated fromMatematichni Metody i Fiziko-Mekhanichni Polya, Vol. 38, 1995.     

Strapdown INS/GPS Integrated Navigation Using Geometric Algebra

A strapdown inertial navigation system (INS)/global positioning system (GPS) integrated navigation Kalman filter in terms of geometric algebra (GA) is proposed. Two error models, i.e., the additive GA error (AGAE) model and the multiplicative GA error (MGAE) model, are developed on the ground of the GA-based strapdown INS model. The AGAE model describes the navigation error by means of perturbation. In contrast, the MGAE model which is indirectly derived from the AGAE one, can physically represent the difference between the computed frame and the true frame. Subsequently, one Kalman filter is constructed on the basis of the MGAE model of the strapdown INS and the error model of GPS. A variety of simulations are carried out to test the proposed Kalman filter. The results show that the Kalman filter can reduce the navigation error remarkably.     

On an asymptotic analysis of polynomial approximation to halfband filters

In this paper we provide information about the asymptotic properties of polynomial filters which approximate the ideal filter. In particular, we study this problem in the special case of polynomial halfband filters. Specifically we estimate the error between a polynomial filter and an ideal filter and show that the error decays exponentially fast. For the special case of polynomial halfband filters, our n-th root asymptotic error estimates are sharp.     

Stochastic Detectability and Mean Bounded Error Covariance of the Recursive Kalman Filter with Markov Jump Parameters

In this article, we study the error covariance of the recursive Kalman filter when the parameters of the filter are driven by a Markov chain taking values in a countably infinite set. We do not assume ergodicity nor require the existence of limiting probabilities for the Markov chain. The error covariance matrix of the filter depends on the Markov state realizations, and hence forms a stochastic process. We show in a rather direct and comprehensive manner that this error covariance process is mean bounded under the standard stochastic detectability concept. Illustrative examples are included.     

Self-triggered filter design for a class of nonlinear stochastic systems with Markovian jumping parameters

This paper is concerned with the self-triggered filtering problem for a class of Markovian jumping nonlinear stochastic systems. The event-triggered mechanism (ETM) is employed between the sensor and the filter to reduce unnecessary measurement transmission. Governed by the ETM, the measurement is transmitted to the filter as long as a predefined condition is satisfied. The purpose of the addressed problem is to synthesize a filter such that the dynamics of the filtering error is bounded in probability (BIP). A sufficient condition is first given to ensure the boundedness in probability of the filtering error dynamics, and the characterization of the desired filter gains is then realized by means of the feasibility of certain matrix inequalities. Furthermore, a self-triggered mechanism is designed to guarantee the filtering error dynamics to be BSP with excluded Zeno phenomenon. In the end, numerical simulation is carried out to illustrate the usefulness of the proposed self-triggered filtering algorithm.