On impulse and continuous observation control design in Kalman filtering problem

This paper develops the observation control method for refining the Kalman–Bucy estimates, which is based on impulsive modeling of the transition matrix in an observation equation, thus engaging discrete-continuous observations. The impulse observation control generates on-line computable jumps of the estimate variance from its current position towards zero and, as a result, enables us to instantaneously obtain the estimate, whose variance is closer to zero. The filtering equations over impulse-controlled observations are obtained in the Kalman–Bucy filtering problem. The method for feedback design of control of the estimate variance is developed. First, the pure impulse control is used, and, next, the combination of the impulse and continuous control components is employed. The considered examples allow us to compare the properties of these control and filtering methodologies.     

Set-membership filtering for systems with sensor saturation

This paper addresses the set-membership filtering problem for a class of discrete time-varying systems with sensor saturation in the presence of unknown-but-bounded process and measurement noises. A sufficient condition for the existence of set-membership filter is derived. A convex optimisation method is proposed to determine a state estimation ellipsoid that is a set of states compatible with sensor saturation and unknown-but-bounded process and measurement noises. A recursive algorithm is developed for computing the ellipsoid that guarantees to contain the true state by solving a time-varying linear matrix inequality. Simulation results are provided to demonstrate the effectiveness of the proposed method.     

Energy-efficient filtering for skyline queries in cluster-based sensor networks

Filtering is a generic technique for skyline retrieval in sensor networks, for the purpose of reducing the communication cost, the dominant part of energy consumption. The vast majority of existing filtering approaches are suitable for uniform and correlated datasets, whereas in many applications the data distribution is clustered or anti-correlated. The only work considering anti-correlated dataset requires significant energy for filtering construction, and it is hard to be efficiently adapted to clustered databases. In this paper, we propose a new filtering algorithm, which settles the problem by utilizing individual node characteristics and generating personalized filters. Given a fraction k, a personalized filter prunes at least k percent of points on assigned nodes. A novel scheme for data cluster representation and a sampling method are then proposed to reduce the filtering cost and maximize the benefit of filtering. Extensive simulation results show the superiority of our approach over existing techniques.     

用于血糖无创检测的葡萄糖传感器研究

研制一种新型葡萄糖传感器,初步实现用于反离子电渗透技术提取皮下组织液的葡萄糖的浓度测量。用铁氰化钾作为电子媒介体,固定在聚环氧乙烷凝胶里的葡萄糖氧化酶与溶液中的葡萄糖催化氧化生成葡萄糖酸和亚铁氰化钾,通过检测该反应产生的氧化还原电流的大小来计算葡萄糖溶液的浓度。新型葡萄糖传感器的检测浓度范围2～22 mmol/L内线性度较好,传感器的一致性测试表明:同一传感器多次测量的偏差不超过2%,反应时间较短,接近于1 s。     

卡尔曼滤波在应变式力传感器中的应用

由于应变式力传感器系统中存在较大的随机噪声,降低了系统的标定精度和测量准确性,而卡尔曼滤波适合实时滤除干扰信号。在建立传感器测试模型基础上,通过推导卡尔曼滤波算法,确定了滤波初值和滤波参数。在传感器—A/D转换器—DSP硬件平台上,进行了滤波算法验证。实验表明：卡尔曼滤波有效地滤除了随机干扰信号,适用于静动态测量过程,提高了系统标定精度。     

A statistics-based sensor selection scheme for continuous probabilistic queries in sensor networks

A common approach to improve the reliability of query results based on error-prone sensors is to introduce redundant sensors. However, using multiple sensors to generate the value for a data item can be expensive, especially in wireless environments where continuous queries are executed. Moreover, some sensors may not be working properly and their readings need to be discarded. In this paper, we propose a statistical approach to decide which sensor nodes to be used to answer a query. In particular, we propose to solve the problem with the aid of continuous probabilistic query (CPQ), which is originally used to manage uncertain data and is associated with a probabilistic guarantee on the query result. Based on the historical data values from the sensor nodes, the query type, and the requirement on the query, we present methods to select an appropriate set of sensors and provide reliable answers for several common aggregate queries. Our statistics-based sensor node selection algorithm is demonstrated in a number of simulation experiments, which shows that a small number of sensor nodes can provide accurate and robust query results.     

Stochastic modelling of insulin sensitivity and adaptive glycemic control for critical care

Targeted, tight model-based glycemic control in critical care patients that can reduce mortality 18-45% is enabled by prediction of insulin sensitivity, S(I). However, this parameter can vary significantly over a given hour in the critically ill as their condition evolves. A stochastic model of S(I) variability is constructed using data from 165 critical care patients. Given S(I) for an hour, the stochastic model returns the probability density function of S(I) for the next hour. Consequently, the glycemic distribution following a known intervention can be derived, enabling pre-determined likelihoods of the result and more accurate control. Cross validation of the S(I) variability model shows that 86.6% of the blood glucose measurements are within the 0.90 probability interval, and 54.0% are within the interquartile interval. "Virtual Patients" with S(I) behaving to the overall S(I) variability model achieved similar predictive performance in simulated trials (86.8% and 45.7%). Finally, adaptive control method incorporating S(I) variability is shown to produce improved glycemic control in simulated trials compared to current clinical results. The validated stochastic model and methods provide a platform for developing advanced glycemic control methods addressing critical care variability.     

Distributed filtering over sensor networks for autonomous navigation of UAVs

The paper studies the problem of localization and autonomous navigation of a multi-UAV system with the use of Distributed Filtering methods (DF). It is considered that m UAV (helicopter) models are monitored by n different ground stations. The overall concept is that at each monitoring station a filter is used to track each UAV by fusing measurements which are provided by various UAV sensors, while by fusing the state estimates from the distributed local filters an aggregate state estimate for each UAV is obtained. In particular, the paper proposes first the extended information filter (EIF) and the unscented information filter (UIF) as possible approaches for fusing the state estimates provided by the local monitoring stations, under the assumption of Gaussian noises. The EIF and UIF estimated state vector is in turn used by a flatness-based controller that makes the UAV follow the desirable trajectory. Moreover, the distributed particle filter (DPF) is proposed for fusing the state estimates provided by the local monitoring stations (local filters). The motivation for using DPF is that it is well-suited to accommodate non-Gaussian measurements. The DPF estimated state vector is again used by the flatness-based controller to make each UAV follow a desirable flight path. Finally, a derivative-free implementation of the extended information filter (DEIF) is introduced aiming at obtaining more accurate estimates of the UAV state vector in real-time. The performance of the EIF, of the UIF, of the DPF and of the DEIF is evaluated through simulation experiments in the case of a 2-UAV model monitored and remotely navigated by two local stations.     

传感器网络中鲁棒性虚假数据过滤方案

虚假数据攻击不仅误导基站做出错误的决定,同时也会耗尽宝贵的网络资源。提出了一个鲁棒性虚假数据过滤方案(a Robust Filtering False Date scheme,RFFD)。该方案主要包括一个密钥管理架构及与之对应的虚假数据过滤安全机制两个部分。理论分析和模拟实验表明,与SEF方案相比,RFFD方案过滤虚假数据包的性能显著提高。     

RBFNN在速度传感器异常值滤除中的应用

基于速度的延滞特性即不会瞬间突变,利用径向基函数（RBF）优秀的预估和拟合逼近能力,通过对已知时刻的速度值进行网络训练和学习,可以很好地预测下一刻的值及其变化趋势。根据是否落入由训练误差所确定的置信区间,判定异常值并进行异常值的滤除。经实验测试,95%的置信区间能够完全满足剔除异常数据、保留正常数据的功能。     

Real-time optimal filtering for stochastic systems with multiresolutional measurements

A real-time optimal filtering algorithm for stochastic systems with multiresolutional measurements is derived. The algorithm gives fused estimates based upon all available data at a particular time index. A multiresolutional distributed filtering scheme is employed. The wavelet transform is utilized as a bridge, effectively linking different resolution levels. A tree-like hierarchical data structure introduced in this paper facilitates the real-time multiresolutional filtering.     

Variance-constrained filtering for uncertain stochastic systems with missing measurements

In this note, we consider a new filtering problem for linear uncertain discrete-time stochastic systems with missing measurements. The parameter uncertainties are allowed to be norm-bounded and enter into the state matrix. The system measurements may be unavailable (i.e., missing data) at any sample time, and the probability of the occurrence of missing data is assumed to be known. The purpose of this problem is to design a linear filter such that, for all admissible parameter uncertainties and all possible incomplete observations, the error state of the filtering process is mean square bounded, and the steady-state variance of the estimation error of each state is not more than the individual prescribed upper bound. It is shown that, the addressed filtering problem can effectively be solved in terms of the solutions of a couple of algebraic Riccati-like inequalities or linear matrix inequalities. The explicit expression of the desired robust filters is parameterized, and an illustrative numerical example is provided to demonstrate the usefulness and flexibility of the proposed design approach.     

Quantised polynomial filtering for nonlinear systems with missing measurements

This paper is concerned with the polynomial filtering problem for a class of nonlinear systems with quantisations and missing measurements. The nonlinear functions are approximated with polynomials of a chosen degree and the approximation errors are described as low-order polynomial terms with norm-bounded coefficients. The transmitted outputs are quantised by a logarithmic quantiser and are also subject to randomly missing measurements governed by a Bernoulli distributed sequence taking values on 0 or 1. Dedicated efforts are made to derive an upper bound of the filtering error covariance in the simultaneous presence of the polynomial approximation errors, the quantisations as well as the missing measurements at each time instant. Such an upper bound is then minimised through designing a suitable filter gain by solving a set of matrix equations. The filter design algorithm is recursive and therefore applicable for online computation. An illustrative example is exploited to show the effectiveness of the proposed algorithm.     

Optimal filtering for networked systems with stochastic sensor gain degradation

In this paper, the optimal filtering problem is investigated for a class of networked systems in the presence of stochastic sensor gain degradations. The degradations are described by sequences of random variables with known statistics. A new measurement model is put forward to account for sensor gain degradations, network-induced time delays as well as network-induced data dropouts. Based on the proposed new model, an optimal unbiased filter is designed that minimizes the filtering error variance at each time-step. The developed filtering algorithm is recursive and therefore suitable for online application. Moreover, both currently and previously received signals are utilized to estimate the current state in order to achieve a better accuracy. A numerical simulation is exploited to illustrate the effectiveness of the proposed algorithm.     

Integral-based identification of patient specific parameters for a minimal cardiac model

A minimal cardiac model has been developed which accurately captures the essential dynamics of the cardiovascular system (CVS). However, identifying patient specific parameters with the limited measurements often available, hinders the clinical application of the model for diagnosis and therapy selection. This paper presents an integral-based parameter identification method for fast, accurate identification of patient specific parameters using limited measured data. The integral method turns a previously non-linear and non-convex optimization problem into a linear and convex identification problem. The model includes ventricular interaction and physiological valve dynamics. A healthy human state and four disease states, valvular stenosis, pulmonary embolism, cardiogenic shock and septic shock are used to test the method. Parameters for the healthy and disease states are accurately identified using only discretized flows into and out of the two cardiac chambers, the minimum and maximum volumes of the left and right ventricles, and the pressure waveforms through the aorta and pulmonary artery. These input values can be readily obtained non-invasively using echo-cardiography and ultra-sound, or invasively via catheters that are often used in Intensive Care. The method enables rapid identification of model parameters to match a particular patient condition in clinical real time (3-5 min) to within a mean value of 4-10% in the presence of 5-15% uniformly distributed measurement noise. The specific changes made to simulate each disease state are correctly identified in each case to within 10% without false identification of any other patient specific parameters. Clinically, the resulting patient specific model can then be used to assist medical staff in understanding, diagnosis and treatment selection.     

Linear filtering for time-varying systems using measurements containing colored noise

The Kalman-Bucy filter for continuous linear dynamic systems assumes all measurements contain "white" noise, i.e. noise with correlation times short compared to times of interest in the system. It is shown here that if correlation times are not short, or if some measurements are free of noise, the optimal filter is a modification of the Kalman-Bucy filter which, in general, contains differentiators as well as integrators. It is also shown for this case that the estimate and its covariance matrix are, in general, discontinuous at the time when measurements are begun. The case of random bias errors in the measurements is shown by example to be a limiting case of colored noise.     

基于稀疏表示的惯性传感器信号实时滤波方法

针对惯性传感器信号处理的特点,提出了基于稀疏表示的信号滤波处理系统模型和方法。通过 K-SVD算法对信号学习训练获得字典,为了减少计算量,满足实时性,尽量降低字典的大小,仿真结果表明,在满足一定精度的条件下,字典的大小最小为3×10。在该字典下对信号进行稀疏表示和重构,改变信号的输入方式,可以实现信号的实时滤波。仿真结果表明提出的滤波方法能有效地消除噪声,改善输出信号精度,可以提高信噪比最大为4．5 dB。该滤波方法与传统的滤波方法相比有较大的优势,为惯性传感器信号处理提供了一种新的方法。     

Distributed processing of continuous sliding-window k-NN queries for data stream filtering

A sliding-window k-NN query (k-NN/w query) continuously monitors incoming data stream objects within a sliding window to identify k closest objects to a query. It enables effective filtering of data objects streaming in at high rates from potentially distributed sources, and offers means to control the rate of object insertions into result streams. Therefore k-NN/w processing systems may be regarded as one of the prospective solutions for the information overload problem in applications that require processing of structured data in real-time, such as the Sensor Web. Existing k-NN/w processing systems are mainly centralized and cannot cope with multiple data streams, where data sources are scattered over the Internet. In this paper, we propose a solution for distributed continuous k-NN/w processing of structured data from distributed streams. We define a k-NN/w processing model for such setting, and design a distributed k-NN/w processing system on top of the Content-Addressable Network (CAN) overlay. An extensive evaluation using both real and synthetic data sets demonstrates the feasibility of the proposed solution because it balances the load among the peers, while the messaging overhead within the P2P network remains reasonable. Moreover, our results clearly show the solution is scalable for an increasing number of queries and peers.