不完全量测下估计误差协方差与传感器精度上–下确界的关系

在不完全量测下估计系统状态时,状态的稳态误差协方差与各个传感器精度指标有关.今提出一种新算法.可以根据估计误差协方差确定出传感器精度的上-下确界.算法根据稳态卡尔曼滤波的估计误差协方差表达式,推出传感器探测概率以及量测噪声方差指标的容差,并结合线性矩阵不等式求出传感器量测噪声方差的上-下界.根据这些结果,可以对给定的估计误差协方差,采用传感器精度指标的下界,从而在满足其他工程要求的前提下,放宽采样频率,降低传感器成本.     

量测不确定下多传感器自适应粒子滤波算法

针对量测不确定条件下多传感器量测数据的有效利用问题,提出一种多传感器自适应粒子滤波算法.利用随机采样策略和量测模型转移概率实现当前时刻多传感器量测集合的采样,通过粒子滤波中重采样步骤完成估计状态和量测集合的更新,进而依据重采样后单个传感器量测数目在传感器量测集合中的比重实现当前时刻传感器量测的确认.该算法通过有效量测的合理选择,改善了扰动对滤波精度和计算量的不利影响.理论分析和仿真实验均验证了所提出算法的有效性.     

基于多传感器不完全量测下的机动目标跟踪算法

针对传感器探测概率小于1的不完全量测情况下的非机动目标跟踪问题,提出一种基于多传感器不完全量测下的扩展Kalman滤波算法。首先,利用残差检测的野值剔除方法,确定目标状态估计过程中传感器是否接收到正确的量测数据;其次,基于每个传感器的量测数据,在不完全量测下采用改进的扩展卡尔曼滤波算法分别对目标运动状态进行估计;进而结合多传感器最优加权融合方法求解基于多传感器观测数据的状态估计;最后,将算法应用到光电跟踪系统中。仿真实验得到不完全量测下传感器探测概率对滤波效果的影响,验证了算法的有效性,其跟踪精度接近完全量测下的状态估计精度。     

不完全量测下随机有偏离散系统的Cramér-Rao下界

针对含有不完全随机有偏测量序列的状态估计问题, 给出了统计意义下的修正递推估计误差方差Cramér-Rao下界(CRLB)求解算法. 首先建立了不完全随机有偏量测离散系统的数学模型, 进而推导了枚举的CRLB和统计意义的CRLB计算式, 该统计意义的CRLB为枚举CRLB的下界, 其计算量远小于枚举CRLB求解的计算量. 最后, 以给定探测概率和偏差发生率下的一类光电跟踪系统为例, 进行了数字仿真.     

快速协方差交叉融合算法及应用

针对分布式传感网络系统中存在互协方差未知的情形, 融合系数的科学设计对于融合性能至关重要. 本文以各节点估计方差矩阵逆的迹的倒数作为计算融合系数的中间变量, 设计了一种序贯快速协方差交叉融合算法, 可以显著减少各个融合节点的计算量, 能够保证各融合节点融合结果相同. 在给定系统的误差方差上界约束与优化指标前提下, 该融合算法结合粒子群优化算法, 能够给出对分布式系统中各个节点的传感器精度要求. 工程实践中, 可为传感器的选型提供理论依据. 最后, 给出了一个分布式网络传感器精度选型的算例及快速协方差交叉融合算法在雷达网中的应用实例.     

风电技术监督数据的采样频率确定方法研究

为降低网络专线的数据传输负荷,以一次调频技术监督指标为具体对象,研究了风电技术监督数据的采样频率确定方法。首先,通过一次调频数据段辨识得到风电场的一次调频过程单机等值动态模型,并估计实发功率的噪声方差。其次,以噪声方差估计值构造输出量测噪声,得到动态模型在典型输入下的输出信号。然后,以输入和输出信号在不同采样频率下的样本序列,得到一组技术监督指标估计值。最后,重复上述步骤,得到多组一次调频技术监督指标估计值,并以估计值变异系数小于给定阈值时的最小采样频率作为技术监督数据的采样频率。所提方法实现了技术监督指标估计值与数据采样频率间的关联。该研究对促进技术监督数据采集工作的发展有重要的意义。     

多站虚拟量测变换均值无源定位算法

在多站无源均值定位算法中,为了解决部分传感器间夹角过大或过小所导致的定位精度下降问题,提出一种基于虚拟量测变换的多传感器管理无源定位算法.首先在全局坐标系下分析了传感器间夹角对误差几何稀释度(GDOP)的影响,进而得到双站获得较好定位精度的夹角约束关系;其次针对不满足该约束关系的传感器组合提出一种虚拟量测变换定位算法,通过空间管理的方法达到对传感器的优化布站,并结合算法的实施步骤对其原理及特点进行了理论分析,尤其对变换前后的交点精度进行了比较.仿真结果表明虚拟量测算法的定位精度要明显优于均值算法,进而说明该算法的有效性及传感器管理在多站无源定位中的重要作用.     

基于改进卡尔曼滤波的陀螺仪误差补偿算法

针对基于卡尔曼滤波的MEMS陀螺仪误差补偿算法中量测噪声方差选取不准确的问题,提出一种基于改进卡尔曼滤波的陀螺仪误差补偿算法.卡尔曼滤波通常采用统计特性估计得到固定的量测噪声方差,无法自适应地估计不同环境下陀螺仪噪声特性.该算法将卡尔曼滤波与神经网络相融合,使用卡尔曼滤波新息矩阵作为神经网络输入,通过神经网络得到新息协方差矩阵,以此自适应地估计卡尔曼滤波量测噪声方差.将该算法应用到陀螺仪信号误差补偿中,使用Allan方差分析法对原始信号以及误差补偿后的陀螺仪信号进行分析,实验结果表明该算法能够有效地抑制陀螺仪随机误差,提高MEMS陀螺仪的精度.     

WSNs自适应DCS目标跟踪算法

无线传感器网络中传感器节点相互协同完成感知任务,以传感器量测的信息效用和获取量测的能量消耗来折中地选择参与的节点。针对IDSQ和DCS算法仅在节点协同环节极小化能耗的问题,提出自适应DCS算法(ADCS),以用户设定的精度阈值为约束,根据当前性能指标自适应降低节点采样频率,在数据采集阶段对节点进行有效控制,从根源上实现了极小化网络能耗的目的。仿真结果表明:ADCS在满足用户精度需求的前提下,大幅度减少了网络能耗。     

异类异步多传感器的目标跟踪算法

针对主动传感器与被动传感器采样频率不相同的目标跟踪问题,提出一种新的解耦算法。在没有测距信息的采样时刻,通过构造虚拟量测点的方法进行滤波跟踪,有效地利用了全部测角信息,提高了跟踪性能。仿真实验表明,该算法具有较高的跟踪精度,且只占用较小的时间花费。     

Impulsive noise suppression in the case of frequency estimation by exploring signal sparsity

The frequency estimation problem is addressed in this work in the presence of impulsive noise. Two typical scenarios are considered; that is, the received data are assumed to be uniformly sampled, i.e., without data missing for the first case and data are randomly missed for the second case. The main objective of this work is to explore the signal sparsity in the frequency domain to perform frequency estimation under the impulsive noise. Therefore, to that end, a DFT-like matrix is created in which the frequency sparsity is provided. The missing measurements are modeled by a sparse representation as well, where missing samples are set to be zeros. Based on this model, the missing pattern represented by a vector is indeed sparse since it only contains zeros and ones. The impulsive noise is remodeled as a superposition of a unknown sparse vector and a Gaussian vector because of the impulsive nature of noise. By utilizing the sparse property of the vector, the impulsive noise can be treated as a unknown parameter and hence it can be canceled efficiently. By exploring the sparsity obtained, therefore, a joint estimation method is devised under optimization framework. It renders one to simultaneously estimate the frequency, noise, and the missing pattern. Numerical studies and an application to speech denoising indicate that the joint estimation method always offers precise and consistent performance when compared to the non-joint estimation approach.     

Detection of intermittent faults for nonuniformly sampled multi-rate systems with dynamic quantisation and missing measurements

ABSTRACT

This paper is concerned with the fault detection problem for a class of networked multi-rate systems with nonuniform sampling and dynamic quantization. The sampling interval of the measurements is allowed to be nonuniform that is governed by a time-homogenous Markov process with partly unknown and uncertain transition probabilities. The measured output is quantized by a dynamic quantizer and then transmitted through communication network subject to data missing. The main purpose of the problem under consideration is to design sampling-interval-dependent fault detection filters such that, in the simultaneous presence of nonuniform sampling, dynamic quantization, intermittent faults as well as missing measurements, the robustness of residuals with respect to the disturbance and the sensitivity of the residuals against the fault are guaranteed. Finally, a three-tank system is utilized to illustrate the effectiveness of the proposed fault detection scheme.     

离散随机系统的容错约束方差控制设计

本文考虑线性离散随机系统的容错约束方差控制设计问题，即设计反馈控制器，使闭环系统在可能的传感器失效不仅保持渐近稳定，而且满足预先给定的稳态方差约束，文中导出期望了容约束方差控制器存在的充分条件，并进一步给出了其参数化代数表达式。     

Variance-constrained filtering for discrete-time genetic regulatory networks with state delay and random measurement delay

This paper is concerned with the variance-constrained filtering problem for a class of discrete-time genetic regulatory networks (GRNs) with state delay and random one-step measurement delay. The phenomenon of the random one-step measurement delay is characterised by a random variable, which is assumed to obey the Bernoulli distribution with known occurrence probability. The purpose of the addressed problem is to design a filter such that, in the presence of state delay and random one-step measurement delay, an upper bound of the filtering error covariance matrix can be obtained and the explicit expression of the filter gain matrix is given. Then, the proposed variance-constrained filtering method can be used to approximate the concentrations of mRNAs and proteins. Finally, a numerical example is provided to illustrate the effectiveness of the designed filtering scheme.     

具有丢失数据的可分解马尔可夫网络结构学习

具有丢失数据的可分解马尔可夫网络结构学习是一个重要而困难的研究课题，数据的丢失使变量之间的依赖关系变得混乱，无法直接进行可靠的结构学习．文章结合最大似然树和Gibbs抽样，通过对随机初始化的丢失数据和最大似然树进行迭代修正一调整，得到修复后的完整数据集；在此基础上基于变量之间的基本依赖关系和依赖分析思想进行可分解马尔可夫网络结构学习，能够避免现有的丢失数据处理方法和可分解马尔可夫网络结构学习方法存在的效率和可靠性低等问题．试验结果显示，该方法能够有效地进行具有丢失数据的可分解马尔可夫网络结构学习．     

侧扫雷达的流速测验不确定度分析与研究

针对提高侧扫雷达测流系统河流表面流速测验精度,关键在于确定影响侧扫雷达流速测验不确定度因素的情况,结合雷达系统理论设计,分析侧扫雷达产生不确定性的理论原因,包括收发宽波束带来的天线方向展宽效应、河面随机起伏及系统参数设计引起的不确定度等。对侧扫雷达实测数据进行分析,证实系统不确定度的产生对雷达数据频谱的影响,验证不确定度在测流时对测量信号谱宽的影响效应。针对采样不确定度,采用基于点模式的数据采集方法和分析策略,通过配置不同采样的数据集,有效降低测流不确定度,提高雷达数据质量。测站侧扫雷达系统的实测流速数据分析表明：按照相关理论对应要求设置参数,侧扫雷达可取得较好的表面流速测验精度和稳定性,能够满足水文测验的要求,更好地服务于水文测报工作。     

Recursive state estimation for discrete‐time nonlinear systems with event‐triggered data transmission,norm‐bounded uncertainties and multiple missing measurements

In this paper, we consider the recursive state estimation problem for a class of discrete‐time nonlinear systems with event‐triggered data transmission, norm‐bounded uncertainties, and multiple missing measurements. The phenomenon of event‐triggered communication mechanism occurs only when the specified event‐triggering condition is violated, which leads to a reduction in the number of excessive signal transmissions in a network. A sequence of independent Bernoulli random variables is employed to model the multiple measurements missing in the transmission. The norm‐bounded uncertainties that could be considered as external disturbances which lie in a bounded set. The purpose of the addressed filtering problem is to obtain an optimal robust recursive filter in the minimum‐variance sense such that with the simultaneous presence of event‐triggered data transmission, norm‐bounded uncertainties, and multiple missing measurements; the filtering error is minimized at each sampling time. By solving two Riccati‐like difference equations, the filter gain is calculated recursively. Based on the stochastic analysis theory, it is proved that the estimation error is bounded under certain conditions. Finally, two numerical examples are presented to demonstrate the effectiveness of the proposed algorithm. Copyright © 2016 John Wiley & Sons, Ltd.     

基于GRU和XGBoost的矿压显现规律预测

采用光纤传感器监测的光纤频移值对矿压显现规律进行表征的过程中,传感器采集的数据存在缺失现象,无法准确预测矿压显现规律。针对该问题,以千秋煤矿为工程背景,在假设光纤下半部分数据丢失的前提下,引入GRU(门控循环单元)和LSTM(长短期记忆网络)2种预测模型,对缺失的光纤频移值进行对比预测,得出GRU模型的收敛速度优于LSTM模型的收敛速度,说明基于GRU模型的缺失值处理方法较优。将原始完整的光纤频移值转换为可表征矿压显现位置的光纤平均频移变化度,引入XGBoost(极端梯度提升)模型和BP神经网络模型进行对比预测,XGBoost模型能准确预测出测试集中所有出现“尖峰”的位置,而BP神经网络模型只预测出2处“尖峰”位置,说明XGBoost模型的预测效果优于BP神经网络模型的预测效果。将预测出的光纤频移缺失值替换至缺失位置,形成“完整”光纤频移值数据,将该数据转换为光纤平均频移变化度后,采用XGBoost模型进行预测。验证结果表明:LSTM模型及GRU模型均可准确预测出光纤下半部分的数据,且GRU模型准确性较LSTM模型准确性高;使用XGBoost可准确预测出测试集中出现的周期来压;通过GRU模型预测出的缺失数据经整合至缺失位置后,使用XGBoost模型仍可进行有效的矿压预测。     

具有丢失数据的贝叶斯网络结构学习算法

学习具有丢失数据的贝叶斯网络结构主要采用结合 EM 算法的打分一搜索方法,其效率和可靠性比较低.针对此问题建立一个新的具有丢失数据的贝叶斯网络结构学习算法.该方法首先用 Kullback-Leibler(KL)散度来表示同一结点的各个案例之间的相似程度,然后根据 Gibbs 取样来得出丢失数据的取值.最后,用启发式搜索完成贝叶斯网络结构的学习.该方法能够有效避免标准 Gibbs 取样的指数复杂性问题和现有学习方法存在的主要问题.     

Filling gaps in diameter measurements on standing tree boles in the urban forest of Thessaloniki,Greece

Missing data are omnipresent in forestry research, and this poses problems in the analysis of primary data. Many statistical problems have been viewed as missing data problems. To cope with incomplete data, several methods are currently being used. They are all based on assumptions some of which might not be valid in a particular case. The choice mostly depends on the objective of the study. Considerable mensuration research is motivated by the need for yield projections that can support forest management decisions. This paper is focused on a new approach for filling gaps in diameter measurements on standing tree boles. Dealing with this problem, an attempt was made to examine the applicability of artificial neural network models for missing data estimation and to use the estimated values in the subsequent analysis. The procedure that should be followed in the development of such models is outlined. The results show good performance of the examined ANN models compared to regression treatments for missing data and ANN models demonstrate their adequacy and potential for filling gaps in diameter measurements on standing tree boles. The ANN models applied in this study are sufficiently general and have great potential to be applicable for estimating the missing values of many variables in environmental applications.