A delay-dependent approach to H ∞ filtering for stochastic delayed jumping systems with sensor non-linearities

In this paper, a delay-dependent approach is developed to deal with the stochastic H _∞ filtering problem for a class of Itô type stochastic time-delay jumping systems subject to both the sensor non-linearities and the exogenous non-linear disturbances. The time delays enter into the system states, the sensor non-linearities and the external non-linear disturbances. The purpose of the addressed filtering problem is to seek an H _∞ filter such that, in the simultaneous presence of non-linear disturbances, sensor non-linearity as well as Markovian jumping parameters, the filtering error dynamics for the stochastic time-delay system is stochastically stable with a guaranteed disturbance rejection attenuation level γ. By using Itô's differential formula and the Lyapunov stability theory, we develop a linear matrix inequality approach to derive sufficient conditions under which the desired filters exist. These conditions are dependent on the length of the time delay. We then characterize the expression of the filter parameters, and use a simulation example to demonstrate the effectiveness of the proposed results.     

改进的互功率谱时延估计算法

麦克风阵列的声源定位一直是阵列信号处理领域的研究热点.以互功率谱相位估计法（mutual power spectrum phase estimation,CSP）为代表的时延估计法因为其原理简单、计算量小、易于实现而得到广泛应用.虽然CSP算法在高信噪比环境下有不错的估计效果,但当信噪比较低和声学场景较复杂时,算法效果急剧下降.为了解决这一问题,本文对CSP算法进行改进.通过对CSP算法的时延估计结果进行筛选,剔除不合理的时延值,更新算法参数后重新进行估计以得到合理的时延值,并经过多帧信号加权得到声源的时延值与位置信息.为了验证所提算法的有效性,本文分别在Matlab与真实环境下进行了实验验证,结果表明,改进后的CSP算法相比原有算法在时延估计精度方面有明显改善.     

基于强化学习的移动传感器网络时延系统控制数学建模

为了提高移动传感器网络时延系统控制能力,提出基于强化学习的移动传感器网络时延系统控制模型,采用高阶近似微分方程构建移动传感器网络时延系统的控制目标函数,结合最大似然估计方法进行移动传感器网络的时延参数估计,采用强化学习方法进行移动传感器网络的收敛性控制和自适应调度,建立传感器网络时延系统控制的多维测度信息配准模型,在强化跟踪学习寻优模式下实现移动传感器网络时延系统的自适应控制。仿真结果表明,采用该方法进行移动传感器网络时延系统控制的自适应性较好,时延参数估计准确度较高,控制过程的鲁棒性较强。     

Optimizing search-based identification of stochastic time-delay systems

A frequently used identification method of time-delay systems, namely the method of direct least-squares (LS) estimation with step-by-step search, is significantly improved in some aspects. First, a discussion is made on how to design a proper digital filter in order to derive a unimodal criterion function with respect to the time delay. It is then suggested that the golden section algorithm can be implemented to search the time delay optimizingly, while the system parameters are simultaneously estimated by use of the standard LS method. The presented optimizing search-based identification algorithm exhibits superior performance to the step-by-step search-based method, in that it is more widely applicable and less computational demanding. Illustrative examples are also included.     

带有色观测噪声多传感器多重时滞系统分布式融合滤波器

基于新息分析方法, 对带有色观测噪声的多重时滞系统, 提出了一种带白噪声估值器的非增广的最优滤波器. 它等价于一个带相关白噪声多重时滞系统的一步预报器. 当系统带有多个传感器时, 推导了多重时滞系统的任意两个传感器子系统之间的估计误差互协方差阵. 基于线性最小方差最优加权融合估计算法, 给出了分布式加权融合最优滤波器. 分布式融合估计比基于每个传感器的局部估计具有更高的精度. 比增广的集中式最优滤波器具有更好的可靠性, 且避免了高维计算和大存储空间. 仿真例子验证了其有效性.     

On-line estimation of time delay and continuous-time process parameters†

A simple technique is presented for on-line estimation of constant or slowly-varying continuous-time process parameters and time delay. The method is shown to allow considerable flexibility for application to systems of varying complexity. A major advantage of the algorithm lies in its ability to track time-delay variations over a practically unlimited range. The technique is based on approximation of time delay in the frequency domain by a rational transfer function, construction of the derivatives of process input and output using multiple filters, and estimation using a model which is non-linear in the desired parameters. In spite of this inherent non-linearity with respect to the sought parameters, in general the estimation schemes lead to the true, unique solution. The cases when this is not true are shown not to be of serious consequence.     

一种改进的声测定位时延估计算法

研究了时延估计算法在被动声测定位中的应用,提出了一种改进的基于最大似然(ML)权函数的广义互相关时延估计算法。改进的算法采用加窗法和最小均方差（LMS)滤波法,弥补了原算法计算量大及无法消除回响干扰的不足。仿真结果表明,改进的算法计算复杂度明显降低,能够有效地消除回响干扰,具有较高的时延估计精度和鲁棒性。     

随机时滞系统的优化搜索辨识方法

本文对时滞系统的常用辨识方法,即直接最小二乘逐步搜索法做了重要改进.首先讨论了如何设计适当的滤波器以获得关于纯时滞的单峰指标函数,据此提出了采用黄金分割法对纯时滞进行搜索,同时用最小二乘法估计系统参数.这种优化搜索法具有计算量小、应用范围广的特点.本文还给出了计算机仿真结果.     

基于自适应和相关检测的两级时延估计算法

简要本文提出了一种新的两级时延估计方法,采用自适应时延估计与相关检测相结合,在最小均方误差准则下（LMS）通过对FIR滤波器参数的迭代,峰值检测得到时延的整数值,再通过相关检测得到时延估计的小数值。通过对算法原理的推导,给出了时延估计的整数值和小数值的理论依据,并给出了算法具体实现过程。实验结果和分析表明,在非平稳噪音和信噪比大干6dB情况下,时延估计的准确度可以达到95％以上,运算复杂度显著降低。     

Event-triggered fuzzy filtering in networked control system for a class of non-linear system with time delays

This paper focuses on the design of H _∞ filter and event-triggered scheme for a class of continuous non-linear networked control systems (NCSs) based on fuzzy system appeared with time delays. First, we consider the discrete event-triggered (ET) scheme to make efficient utilisation of bandwidth. Under this ET-scheme, sensor releases the data only when our sampled-data plant violates the specific event-triggered condition. Second, the T-S fuzzy system is used to model the non-linear NCSs. Another purpose of this paper is to design filters involving delays. Such filters have a more general form than the delay-free filters that have been mostly considered in the traditional studies. By using the time-delay system, co-design of event-triggered scheme and H _∞ filter for the delayed NCSs is presented in a unified frame work. To choose the latest data packet and discard the dis-ordered packet logic, zero-order hold is inserted between the fuzzy filter and event generator. Then, by using the novel fuzzy Lyapunov–Krasovskii function approach with free-weighting matrix technique, H _∞ filter design of event-triggered delay NCSs is proposed. Finally, to show the effective result of our co-design method, a tunnel-diode example is given.     

Optimal tracking control of a flexible hub–beam system with time delay

In this paper, a new technique of time-delay compensation is proposed for active control of a flexible hub–beam system. The first-order approximation coupling (FOAC) model proposed recently for dynamics of hub–beam systems is used to verify the applicability of this technique. The FOAC model is first linearized to obtain a linearized equation. The linearized equation with time delay is then transformed into a standard form with no time delay by a particular integral transformation. The time-delay controller is designed based on this standard equation using the classical optimal tracking control theory. Since the controller is a function of modal coordinates, a modal filter is presented to estimate the modal coordinates from physical sensor measurements. The effectiveness of the proposed technique for time delay is demonstrated by numerical simulations. Simulation results indicate that a very small time delay may result in instability of the control system if it is not compensated in control design. The proposed time-delay controller is effective in controlling the system even when the maximum time delay for stability without time-delay compensation is greatly exceeded. Moreover, for the system without time delay, the proposed time-delay controller may possibly obtain much better control effectiveness than the controller without time delay.     

Interval estimation for uncertain systems with time-varying delays

The estimation problem for uncertain time-delay systems is addressed. A design method of reduced-order interval observers is proposed. The observer estimates the set of admissible values (the interval) for the state at each instant of time. The cases of known fixed delays and uncertain time-varying delays are analysed. The proposed approach can be applied to linear delay systems and nonlinear time-delay systems in the output canonical form. It involves the properties of quasi-monotone/Metzler/cooperative systems. In this framework, it is shown that if under a suitable coordinate transformation the delay-free subsystem is cooperative, then the delayed estimation error dynamics inherits this property. The conditions to find the observer gains are formulated in the form of LMI. The framework efficiency is demonstrated on examples of nonlinear systems.     

A SLAM based on auxiliary marginalised particle filter and differential evolution

FastSLAM is a framework for simultaneous localisation and mapping (SLAM) using a Rao-Blackwellised particle filter. In FastSLAM, particle filter is used for the robot pose (position and orientation) estimation, and parametric filter (i.e. EKF and UKF) is used for the feature location's estimation. However, in the long term, FastSLAM is an inconsistent algorithm. In this paper, a new approach to SLAM based on hybrid auxiliary marginalised particle filter and differential evolution (DE) is proposed. In the proposed algorithm, the robot pose is estimated based on auxiliary marginal particle filter that operates directly on the marginal distribution, and hence avoids performing importance sampling on a space of growing dimension. In addition, static map is considered as a set of parameters that are learned using DE. Compared to other algorithms, the proposed algorithm can improve consistency for longer time periods and also, improve the estimation accuracy. Simulations and experimental results indicate that the proposed algorithm is effective.     

通用航空集成航电系统GPS延迟时间的分析

针对GPS信号定位延迟的问题,提出一种估算延迟时间的方法.在对延迟时间粗略估计的基础上,将延迟时间分成可测量和不可测量两部分.根据GPS接收时间周期和航电系统服务周期,确定最佳时间延迟计算公式,基于卡尔曼滤波原理估算不可测量时间,对GPS信号延迟进行精确估算.进行仿真实验,验证了该方法计算GPS信号延迟时间的精确性.     

Parameter and delay estimation of continuous-time models using a linear filter

Linear filter methods have been used in the field of continuous-time identification over a considerable time period. Due to the effectiveness and simplicity of the approach, they have found widespread applications and drawn much interest from the system identification community. However, the estimation of time delay along with continuous-time model parameters has remained an unsolved problem although there are some simple step response based methods. In this paper, a new linear filter method is introduced for simultaneous parameter and delay estimation of continuous-time transfer function models. The proposed method estimates the time delay along with other model parameters in an iterative way through simple linear regression. In addition, the estimated delay is not necessarily an integer multiple of the sampling interval. The applicability of the developed procedure is demonstrated by simulations as well as a laboratory scale experimental example.     

等间隔分数时延滤波器及其线性插值设计

针对宽带数字信号精确可变时延需求,研究等间隔分数时延滤波器及其线性插值方法。现有分数时延滤波器设计方法不能应用于可变时延,基于多采样率信号处理理论,提出等间隔分数时延滤波器设计方法。将所需时延近似为等间隔分数时延,选取对应时延滤波器组对信号进行时延,满足工程中变分数时延滤波需求。为提高时延滤波精度,提出对相邻滤波器系数线性插值的分数时延滤波器设计方法。对线性调频信号仿真结果表明,所提方法与常规分数时延滤波方法运算量相当,且适用于宽带信号的精确可变时延应用。     

基于空时扩展虚拟阵列的高分辨率多参数联合估计

针对雷达、声纳、移动通信等系统中对多个目标方位、速度与距离等信息同步提取的要求,文章通过对原始数据的时延补偿等处理,构造了具有时移旋转不变性的一组虚拟相关矩阵,提出了一种基于空时扩展虚拟传感器阵列的二维方位角、多普勒频率和相对时延联合估计的新方法。该方法对于噪声不敏感,可应用于低信噪比环境,具有较高的估计精度,且无需谱峰搜索,各参数在求解过程中自然配对,理论可同时处理的目标数目超过系统阵元数限制。算法对于传感器阵列的结构无特殊限制,可应用于任意常见阵列如均匀方阵、“L”阵、圆阵等等,具有很好的理论价值以及应用前景。理论分析与计算机仿真实验都证明了算法的有效性。     

Sensor adaptive fault tolerant control for non-linear processes

Adaptive fault tolerant control of non-linear processes is an open problem. In this paper, on the basis of a strong tracking filter (STF), an approach to sensor adaptive fault tolerant generic model control for non-linear processes is proposed. When the process runs normally, Adaptive Generic Model Control (AGMC) based on parameter estimation is used to control non-linear time-varying processes. A sensor fault model is set up by introducing a bias vector into the output equation of the process. The bias vector is estimated on-line based on the STF during every control period. With the estimated sensor bias vector and the time-varying parameters, a fault detection mechanism is developed to supervise sensors. When a sensor fault is detected, AGMC will be switched to a state estimation and soft-sensor-based GMC. This strategy constitutes a sensor-adaptive fault tolerant generic model control for non-linear processes. Experimental results on a three-tank system demonstrate the effectiveness of the proposed approach.     

Equalization with decision delay estimation using recurrent neural networks

In this paper, we propose a new recursive classifier based on a recurrent neural network. A supervised algorithm is employed to estimate the classifier parameters. The proposed classifier is used to form a non-linear Decision Feedback Equalizer (DFE) for communication channels. A new procedure allowing the estimation of the decision delay is also presented so that the classifier parameters and the decision delay are estimated at the same time. This new DFE leads to suitable equalization performances even in presence of non-linear and non–minimum phase channels.     

有色量测噪声下的改进求容积卡尔曼滤波器

为解决标准求容积卡尔曼滤波器在有色量测噪声条件下滤波精度退化的问题,提出改进求容积卡尔曼滤波器及其平方根形式.首先利用一阶马尔科夫模型白化非线性离散随机系统中有色量测噪声,将有色量测噪声下非线性离散随机系统转化为白噪声下非线性时滞系统.然后根据所得非线性时滞系统推导其高斯域的贝叶斯滤波框架,最后基于3度Spherical-Radial规则将该滤波框架近似为改进的求容积卡尔曼滤波器和其平方根形式.机动目标跟踪仿真试验结果表明两种改进求容积卡尔曼滤波算法在标准白噪声条件下与标准求容积卡尔曼滤波算法的估计精度相同,而在有色量测噪声背景下滤波精度和鲁棒性更优.