Probability densities for conditional statistics in the cubic sensor problem

This paper applies the techniques of Malliavin’s stochastic calculus of variations to Zakai’s equation for the one-dimensional cubic sensor problem in order to study the existence of densities of conditional statistics. Let {X _t} be a Brownian motion observed by a cubic sensor corrupted by white noise, and let denote the unnormalized conditional estimate of φ(X _i ). If φ₁,...,φ _n are linearly independent, and if , it is shown that the probability distribution of admits a density with respect to Lebesgue measure for anyn. This implies that, at any fixed time, the unnormalized conditional density cannot be characterized by a finite set of sufficient statistics. Research supported in part by NSF Grant No. MCS-8301880 and by the Institute for Mathematics and It Applications, Minneapolis, Minnesota.     

On necessary conditions for the existence of finite-dimensional filters in discrete time

Under some regularity assumptions we show that, if a finite-dimensional filter in discrete time exists, then the observation, prediction, and filtering distributions are all of exponential class. Our results, motivated by analogous results in the field of statistics, hold for arbitrary (finite) dimensions of the state and observation spaces as well as of the filter itself.     

The problem of optimal robust sensor scheduling

This paper considers the sensor scheduling problem which consists of estimating the state of an uncertain process based on measurements obtained by switching a given set of noisy sensors. The noise and uncertainty models considered in this paper are assumed to be unknown deterministic functions which satisfy an energy type constraint known as an integral quadratic constraint. The problem of optimal robust sensor scheduling is formulated and solution to this problem is given in terms of the existence of suitable solutions to a Riccati differential equation of the game type and a dynamic programming equation. Furthermore, a real time implementable method for sensor scheduling is also presented.     

Characterization of a subclass of finite-dimensional estimation algebras with maximal rank application to filtering

Finite-dimensional estimation Lie algebras play a crucial role in the study of finite-dimensional filters for partially observed stochastic process. When the dynamics noise is Gaussian we can characterize the so-called estimation Lie algebras with maximal rank in terms of the observation functions (necessarily affine) and the drift (necessarily a sum of a skew-symmetric linear term and a gradient vector field, with a functional relationship), under the assumption that the estimation algebra has one and only one operator of order greater or equal to two in any of its basis.     

A new design of robust filters for uncertain systems

In this paper, a structured polynomial parameter-dependent approach is proposed for robust H₂ filtering of linear uncertain systems. Given a stable system with parameter uncertainties residing in a polytope with s vertices, the focus is on designing a robust filter such that the filtering error system is robustly asymptotically stable and has a guaranteed estimation error variance for the entire uncertainty domain. A new polynomial parameter-dependent idea is introduced to solve the robust H₂ filtering problem, which is different from the quadratic framework that entails fixed matrices for the entire uncertainty domain, or the linearly parameter-dependent framework that uses linear convex combinations of s matrices. This idea is realized by carefully selecting the structure of the matrices involved in the products with system matrices. Linear matrix inequality (LMI) conditions are obtained for the existence of admissible filters and based on these, the filter design is cast into a convex optimization problem, which can be readily solved via standard numerical software. Both continuous and discrete-time cases are considered. The merit of the methods presented in this paper lies in their less conservatism than the existing robust filter design methods, as shown both theoretically and through extensive numerical examples.     

Exact finite-dimensional filters via systems realization for a class of discrete-time nonlinear systems

We obtain necessary and sufficient conditions for the existence of a finite-dimensional filter for the discrete-time nonlinear system (ε x_k+1 =φ(x_k), y_k = h(x_k)+η(x_k)ν_k, K=0, 1,…. This system is distinguished by the absence of noise in the dynamic and by the correlation between the state and the intensity of noise in the observations.The necessary and sufficient condition provides an explicit formula for the minimal filter and various system-theoretic properties of (ε) and of the minimal filter.     

The explicit solution of the unnormalized conditional probability equation of a one-dimensional linear system

The unnormalized conditional probability equation of a one-dimensional linear system is solved by using the Lie algebra associated with the system. The explicit form of the conditional probability with arbitrary initial distribution is obtained.     

红外气体传感器信号调理及数据处理

针对红外气体传感器易受外界光源、电磁等干扰影响,其输出信号中存在大量噪声而影响检测精度的问题,设计了一种红外气体传感器信号调理及数据处理电路。该电路采用硬件和软件相结合的方法,在硬件上设计了信号调理电路,在保证信号真实性的同时具有非常好的放大滤波功能;在软件上采用中位值平均滤波算法对信号进行处理,可有效克服信号中混入的噪声,且对周期性和热噪声产生的干扰也有很好的抑制作用。试验结果表明,该电路可有效滤除传感器中的大量噪声,准确提取有用信号,提高检测精度及稳定性,满量程精度可达0.54%。     

Unification of nonlinear filtering in the context of binary logical calculus,part II: Gray-scale filters

This second part of a two-part study concerning the logical structure of nonlinear filters treats gray-scale filters. The algebraic framework of threshold decomposition is dedscribed in terms of the appropriate underlying commuting diagram, along with the manner in which generalized stack filters fall out of the framework when it is interpreted in the context of logical calculus. Relationships between representations for morphological and generalized stack filters are expressed in cellular logic.     

无线传感器网络车流量统计的路由协议

提出了一种应用于交通管理中心统计道路车流量的路由协议,以便更好地进行交通管理。根据无线传感器网络能量有限的特点和具体应用的需求,在路由表中添加了车辆在检测区域内的有效时间,动态控制了中间节点是否转发信息,避免了基站接收无用信息,减少了节点的能量消耗。提出了一种节点唤醒策略,有效地提高了车流量统计应用中对目标追踪的质量。仿真实验表明:与AODV协议和TSR协议相比,延长了网络生存周期。     

Unification of nonlinear filtering in the context of binary logical calculus,part I: Binary filters

The mathematical structure of nonlinear filtering is expressed in the context of binary logic. This first part of a two-part study concerns the binary setting. Operator properties, such as antiextensivity and idempotence, are expressed in finite logical expressions, as are the Matheron representation for morphological filters and its extension to translation-invariant operators, thereby giving simplicity to both operational properties and representations and also exposing the manner in which logic methods can be used for filter design and analysis. The second part of the study treats gray-scale filters.     

Robust nonlinear control of shunt active filters for harmonic current compensation

An advanced control strategy of shunt active filters (SAF) aiming to compensate for harmonic current in the electric supply grid is proposed. The SAF considered here is suitable for three-phase three-wire current harmonic compensation and is based on AC/DC three-phase boost converter topology. Robust control of the active-reactive current/power delivered by the SAF is designed exploiting the internal model principle. The stabilization of the DC-link voltage dynamics is addressed along with the fulfillment of the harmonics compensation objective. The two-time scale behavior of the SAF is exploited to apply the averaging theory in the control design. Experiments are provided to show the effectiveness of the proposed solution.     

Adaptive weighting of local classifiers by particle filters for robust tracking

This paper presents an adaptive weighting method for combining local classifiers using a particle filter. Although the effectiveness of weighting methods based on combinations of local classifiers (features) has been reported recently, such methods fail in cases where there is partial occlusion or when shadows appear due to changes in the illumination direction since fixed weights are used for combining the local classifiers. In order to achieve the desired robustness, the weights should be changed adaptively. For this purpose, we use a particle filter, where each particle is assigned to the weight set for combining local classifiers. By estimating the posterior probability in weight space by using a particle filter, the effective weights for current time-step are obtained, and as a result the proposed method can account for dynamic occlusion. As a means of a demonstration, our approach is applied to the face tracking problem. The adaptability and the robustness of the method with respect to partial occlusion are evaluated using test sequences in which the occluded areas are changed dynamically. The weights of the occluded regions decrease automatically without the need for explicit knowledge about the occurrence of occlusion, which makes it possible to track the face under conditions of dynamic occlusion.     

压力传感器动态性能分析及改进

传感器的动态性能是否符合要求对测试结果的准确性至关重要。通过压阻式压力传感器的动态校准实验数据，建立其动态数学模型，并由此求出动态特性指标，最后设计出动态补偿数字滤波器，明显提高了传感器动态响应的快速性和展宽了工作频带。     

Adaptive control for output feedback nonlinear systems in the presence of modeling errors

The paper is extending output feedback nonlinear control and backstepping approaches to a class of systems approximately diffeomorphic to output feedback systems that include unknown functions. The unknown functions are addressed via online function approximation, which results in two types of uncertainty. Parametric uncertainty due to online function approximation and non-parametric uncertainty. The non-parametric uncertainty results from the inability of any function approximator to perfectly model an unknown function and from terms unmodeled by the output feedback form. The non-parametric terms are assumed to be bounded by unknown constants. The backstepping procedure is applied to adapt with respect to both parametric uncertainties and the upper bound on the non-parametric uncertainties.     

无线传感器网络的定位问题综述

无线传感器网络是最近出现的一种新型网络,其定位问题是组网的基本和重要问题。文章首先给出了该网络实施节点定位的主要方法和技术原理,综述了定位问题的研究进展,对典型的节点定位算法进行了系统分类和优缺点分析,指出了无线传感器网络实施精确定位需要解决的问题。     

An ant direction hybrid differential evolution heuristic for the large-scale passive harmonic filters planning problem

This article presents a heuristic which combines the orthogonal array experiment technique and an ant direction hybrid differential evolution algorithm (ADHDEOA) for planning of a large-scale passive harmonic filters problem. The addressed problem has a multi-bus and under abundant harmonic current sources in the system. In this study, an orthogonal array is first conducted to obtain the initial solution. Next, an ant direction hybrid differential evolution (ADHDE) is applied to search for a near optimum solution. The objective is to minimize the cost of the filter, the total demand distortion of harmonic currents and the total harmonic distortion of voltages at each bus simultaneously. In order to determine a set of weights of each term in the objective function, the simplest and most efficient form of triangular membership functions has been considered. The designed heuristic ADHDEOA is applied to a practical harmonic problem in a steel plant, and three design schemes are compared to demonstrate the performance of the ADHDEOA.