Filtering the histories of a partially observed marked point process

We consider a situation in which the evolution of an ‘underlying’ marked point process is of interest, but where this process is not directly observable. Instead, we assume that another marked point process, which is fully determined by the underlying process, can be observed. The problem is then the estimation, at any given time t, of the underlying development so far, given the corresponding observations. The solution, in the sense of a conditional distribution of the underlying pre-t history, is shown to satisfy a recursive filter formula. Sufficient conditions for the uniqueness of the solution are given. Two non-trivial examples are considered in detail.     

基于双树复小波与波原子的图像扩散滤波

图像的非线性扩散滤波来源于热方程的思想,其关键在于计算适当的扩散系数和控制扩散方向. 在已有的扩散模型中,由于扩散系数仅依赖于图像的梯度,因而这类模型容易受噪声的干扰;同时,图像的细节信息(如纹理)容易被误认为是噪声而被去除. 为克服这些不足,首先给出了一种采用双树复小波变换计算扩散系数的方法;然后设计了一种用于图像滤波的非线性扩散模型,最后提出了基于双树复小波变换和波原子阈值相结合的图像滤波算法. 仿真结果表明,所提出的算法在对含噪图像滤波的同时,能够较好地保持图像的边缘和纹理等细节信息. 关键词： 图像扩散滤波 非线性扩散 波原子 双树复小波变换     

Improved Potter-Anderson-Moore algorithm for the differential Riccati equation

The paper presents a method to find the solution of the constant coefficient matrix differential Riccati differential in terms of solutions of algebraic Riccati and Lyapunov equations, and the state transition matrix (matrix exponential) of the corresponding linear dynamic system. The method presented represents an improved method of Potter-Anderson-Moore since the solution is obtained under milder assumptions than the original algorithm of Potter-Anderson-Moore. An aircraft and satellite examples done in the paper demonstrate the advantages of the improved algorithm.     

线性加速度计在压电陀螺卡尔曼滤波技术中的应用

针对光电稳定平台常用的压电陀螺随机游走噪声大的缺点,提出采用基于线性加速度计的卡尔曼滤波技术对其进行信号滤波。利用卡尔曼滤波理论,建立了压电陀螺角速率状态观测方程,采用线性加速度计测量平台惯性角加速度,由此对陀螺信号进行了滤波。实验结果表明:采用线性加速度计能够在不影响陀螺带宽的前提下将压电陀螺的随机游走噪声水平由原有的0.005(°).s-1/槡Hz降低到0.001 25(°).s-1/槡Hz,提高了光电平台的稳定精度。     

Explicit Hybrid Numerical Method for the Allen-Cahn Type Equations on Curved Surfaces

We present a simple and fast explicit hybrid numerical scheme for the motion by mean curvature on curved surfaces in three-dimensional (3D) space. We numerically solve the Allen-Cahn (AC) and conservative Allen-Cahn (CAC) equations on a triangular surface mesh. We use the operator splitting method and an explicit hybrid numerical method. For the AC equation, we solve the diffusion term using a discrete Laplace-Beltrami operator on the triangular surface mesh and solve the reaction term using the closed-form solution, which is obtained using the separation of variables. Next, for the CAC equation, we additionally solve the time-space dependent Lagrange multiplier using an explicit scheme. Our numerical scheme is computationally fast and efficient because we use an explicit hybrid numerical scheme. We perform various numerical experiments to demonstrate the robustness and efficiency of the proposed scheme.     

求解阵列天线自适应滤波问题的一种调比随机逼近算法

提出了求解阵列天线自适应滤波问题的一种调比随机逼近算法.每一步迭代中,算法选取调比的带噪负梯度方向作为新的迭代方向.相比已有的其他随机逼近算法,这个算法不需要调整稳定性常数,在一定程度上解决了稳定性常数选取难的问题.数值仿真实验表明,算法优于已有的滤波算法,且比经典Robbins-Monro （RM）算法具有更好的稳定性.     

NUMERICAL STUDIES OF A CLASS OF COMPOSITE PRECONDITIONERS

In this paper, we study a composite preconditioner that combines the modified tangential frequency filtering decomposition with the ILU（O） factorization. Spectral property of the composite preconditioner is examined by the approach of Fourier analysis. We illustrate that condition number of the preconditioned matrix by the composite preconditioner is asymptotically bounded by O（hp -2/3） on a standard model problem. Performance of the composite preconditioner is compared with other preconditioners on several problems arising from the discretization of PDEs with discontinuous coefficients. Numerical results show that performance of the proposed composite preconditioner is superior to other relative preconditioners.     

Mean-Square Filtering Problem for Discrete Volterra Equations

Abstract

The mean-square filtering problem for the discrete Volterra equations is a nontrivial task due to an enormous amount of operations required for the implementation of optimal filter. A difference equation of a moderate dimension is chosen as an approximate model for the original system. Then the reduced Kalman filter can be used as an approximate but efficient estimator. Using the duality theory of convex variational problems, a level of nonoptimality for the chosen filter is obtained. This level can be efficiently computed without exactly solving the full filtering problem.     

Pricing Fixed-Income Securities in an Information-Based Framework

Abstract

The purpose of this article is to introduce a class of information-based models for the pricing of fixed-income securities. We consider a set of continuous-time processes that describe the flow of information concerning market factors in a monetary economy. The nominal pricing kernel is assumed to be given at any specified time by a function of the values of information processes at that time. Using a change-of-measure technique, we derive explicit expressions for the prices of nominal discount bonds and deduce the associated dynamics of the short rate of interest and the market price of risk. The interest rate positivity condition is expressed as a differential inequality. An example that shows how the model can be calibrated to an arbitrary initial yield curve is presented. We proceed to model the price level, which is also taken at any specified time to be given by a function of the values of the information processes at that time. A simple model for a stochastic monetary economy is introduced in which the prices of the nominal discount bonds and inflation-linked notes can be expressed in terms of aggregate consumption and the liquidity benefit generated by the money supply.     

General approach to filtering with fractional brownian noises — application to linear systems

At first a general approach is proposed to filtering in systems where the observation noise is a fractional Brownian motion. It is shown that the problem can be handled in terms of some appropriate semimartingale and analogs of the classical innovation process and fundamental filtering theorem are obtained. Then the problem of optimal filtering is completely solved for Gaussian linear systems with fractional Brownian noises. Closed form simple equations are derived both for the mean of the optimal filter and the variance of the filtering error. Finally the results are explicited in various specific cases