Adaptive nonlinear information fusion preview control for autonomous surface vessels subject to measurement noises and unknown input saturations

This study presents an adaptive nonlinear information fusion preview control (NIFPC) method for trajectory tracking of autonomous surface vessels (ASVs) subject to system uncertainty, measurement noise, and unknown input saturations. The NIFPC is developed based on the nonlinear information fusion estimation methodology, in which the system's future reference trajectory information, noise information, performance index requirements, and system dynamic model are all transformed into information equations related to control input, and then the current control action is obtained by fusing these previewed future information via the nonlinear information fusion optimal estimation. In order to avoid the unknown input saturation constraints, a fuzzy asymmetric saturated approximator (FASA) is designed and integrated into the controller, where the fuzzy logic system (FLS) is used to adaptively adjust the key boundary parameters of the approximator. As a result, the negative effects caused by system uncertainty and measurement noise can be effectively suppressed, while the completely unknown input saturation constraints in the system actuator are guaranteed not to be violated. The convergence of the tracking errors of the closed-loop system is guaranteed via Lyapunov stability theory. Numerical simulation results have been provided to demonstrate the satisfactory performance of the proposed control scheme.     

通信受限下网络化多传感器系统的Kalman融合估计

研究了一类通信受限下网络化多传感器系统的 Kalman 融合估计问题, 其中通信受限 是指系统在一个采样周期内只允许有限个传感器与融合中心通信. 首先, 提出了一种周期性分组传输的通信策略, 并将每组传感器所对应的局部估计系统描述成一个离散周期子系统模型. 其次, 每个子系统根据最新测量信息的更新时刻, 选择相应的 Kalman 估计器 (滤波器或预报器), 从而得到各子系统在每一时刻的一个局部最优估计, 再通过矩阵加权线性最小方差最优融合准则得到最优融合估计,并给出了Kalman融合估计器的设计方法. 最后, 通过一个目标跟踪例子验证所提方法的有效性.     

用于胎压监测系统的一种改进贝叶斯估计数据融合算法的研究

针对小型汽车胎压监测系统(TPMS)利用单一传感器测量数据不确定性的问题,提出一种将贝叶斯估计和卡尔曼滤波相结合的多传感器数据融合的方法.设计满足系统功能要求的方案,运用贝叶斯估计对SP370轮胎模块中传感器采集的数据进行融合,排除失效的数据以及故障的传感器,提高系统的精度.结合卡尔曼滤波器优化融合的结果,消除噪声信号.研究结果表明,采用上述的数据融合方法能够有效的解决单一传感器测量数据的局限性,抑制传感器引入的噪声,并通过仿真验证了本系统的可行性、可靠性.     

A novel motion-based online temporal calibration method for multi-rate sensors fusion

Accurate and robust calibration is an essential prerequisite for multi-rate sensors fusion. However, most existing calibration methods ignore the temporal calibration and assumed the timestamps of the multi-rate sensors are precisely aligned; more importantly, many approaches are designed for offline calibration. For these reasons, this paper develops a novel online temporal calibration method for multi-rate sensors fusion based on the motion constrains of the sensors. In this new calibration framework, the high update rate inertial measurement unit (IMU) is utilized as the unified calibrating references, while other moderate or low-frequency target sensors can be estimated based on the reference IMU. As a result, the targetless, online, and high-precision temporal self-calibration can be achieved. During the calibration, an improved multi-state constraint Kalman filter (I-MSCKF) algorithm is proposed for both position and temporal states estimation of the multi-rate sensors to establish a multi-constraint filter and correct the temporal offset error in a real-time manner. Furthermore, the motion constraints models in the two-dimensional (2D) planar and three-dimensional (3D) space are developed from per-sensor ego-motion to enhance the robust and reliable abilities of the proposed temporal self-calibration method. Experimental results demonstrate that the proposed method can accurately and online estimate the temporal offset error and transformation parameters, which significantly improves the performance of moving trajectory estimation for robots equipped with the multi-rate sensors.     

可应用于气体泄漏源搜寻的融合算法

多源信息融合算法主要应用于移动机器人对有害气体泄漏源的搜寻。为提高搜寻效率,用视觉传感器和嗅觉传感器共同获取环境信息,其中 嗅觉传感器 采用多气体传感器代替单气体传感器以提高测量的可靠性,测量位置也由单点向多点转变,并选用合适的算法分别实现各级数据的融合,最终决策移动机器人的搜寻方向。数据表明, 加权平均法用于融合同类气体传感器的数据,可减小噪声和仪器故障的影响;最小二乘法可最优估计未知参数,用于反求泄漏源信息,可初步估计泄漏源的位置和流量;概率赋值方式可容纳多种信息途径共同判断泄漏源,从而更合理地确定搜寻目标。     

Individual cylinder characteristic estimation for a spark injection engine

Engine control policies are mostly based on the assumption that all injectors have the same behavior independent of location and aging. In reality, injectors do vary and age. To contain variations around a nominal value, tight tolerances are imposed on the manufacturing process. Even if the manufacturing process is tightly controlled, the air-to-fuel (A/F) ratio needed to satisfy emission constraints is difficult to achieve due to aging and even slight mismatch among different injectors. To devise control policies that take into account behavior differences among injectors, we need to estimate injector characteristics from measurements that are taken on the engine during its life time. In this paper, we present an estimation technique for injector characteristics based on a set of measurements that can be carried out using the sensors present in the car, i.e., intake manifold pressure, crank-shaft speed, throttle-valve plate angle, injection timings and exhaust A/F ratio, which is measured by a single UEGO sensor placed at the exhaust pipe output.     

标量及对角阵加权的序贯估计融合算法研究

针对多传感器分布式估计融合系统,在最小化估计误差的协方差矩阵迹的准则下,采用标量加权及对角阵加权融合方法,提出了估计误差相关条件下的序贯处理式最优估计融合Kalman滤波器。该融合滤波器以两传感器估计融合算法为基础,对传感器采集信息依次进行融合计算,得到多传感器融合结果。比较两种算法与局部滤波器的估计精度,并进行了仿真。仿真结果表明了基于加权估计融合的序贯处理算法的可行性和有效性。     

Robust integrated sequential covariance intersection fusion Kalman filters and their convergence and stability for networked sensor systems with five uncertainties

For networked sensor systems (NSSs) with hard and soft sensors including five uncertainties, two universal approaches of solving the robust fusion estimation problems are presented. It includes an integrated sequential covariance intersection (SCI) fusion minimax robust Kalman filtering approach with cross-covariance information and a generalized Lyapunov equation approach with four pairs of Lyapunov equations. Applying them, the robust local and SCI fused time-varying and steady-state Kalman filters are presented in the sense that their actual estimation error variances are guaranteed to have the corresponding minimal upper bounds. The equivalent batch SCI fusers are also presented. Their robustness and accuracy relations are proved, and the sensitivity of the SCI fuser with respect to the fused orders of sensors is analyzed. Applying the dynamic error system analysis method and the dynamic variance error system analysis method, a new convergence and absolute asymptotic stability theory of robust fusion Kalman filtering is presented. The classical Kalman filtering convergence and stability theory is developed. Compared with the original covariance intersection fuser, they significantly reduced the computational complexity and burden. Compared with the optimal and conservative SCI fusers, they significantly improved the robust accuracies. They are suitable to deal with asynchronous or random delayed data and are suitable for real-time applications. A simulation applied to the two-mass spring damper mechanical system shows their effectiveness.     

Multi-rate distributed fusion estimation for sensor networks with packet losses

This paper presents a distributed fusion estimation method for estimating states of a dynamical process observed by wireless sensor networks (WSNs) with random packet losses. It is assumed that the dynamical process is not changing too rapidly, and a multi-rate scheme by which the sensors estimate states at a faster time scale and exchange information with neighbors at a slower time scale is proposed to reduce communication costs. The estimation is performed by taking into account the random packet losses in two stages. At the first stage, every sensor in the WSN collects measurements from its neighbors to generate a local estimate, then local estimates in the neighbors are further collected at the second stage to form a fused estimate to improve estimation performance and reduce disagreements among local estimates at different sensors. Local optimal linear estimators are designed by using the orthogonal projection principle, and the fusion estimators are designed by using a fusion rule weighted by matrices in the linear minimum variance sense. Simulations of a target tracking system are given to show that the time scale of information exchange among sensors can be slower while still maintaining satisfactory estimation performance by using the developed estimation method.     

Fault‐tolerant fusion‐based MPC with sensor recovery for constrained LPV systems

In this paper, we present a robust fault‐tolerant control scheme for constrained multisensor linear parameter‐varying systems, subject to bounded disturbances, that utilises multiple sensor fusion. The closed‐loop scheme consists of a tube model predictive control‐based feedback tracking controller and sensor‐estimate fusion strategy, which allows for the reintegration of previously faulty sensors. The active fault‐tolerant fusion‐based mechanism tracks the healthy‐faulty transitions of suitable residual variables by means of set separation and precomputed transition times. The sensor‐estimate pairings are then reconfigured based on available healthy sensors. Under the proposed scheme, robust preservation of closed‐loop system boundedness is guaranteed for a wide range of sensor fault situations. An example is presented to illustrate the performance of the fault‐tolerant control strategy.     

Robust and ubiquitous smartphone-based lane detection

Lane-level positioning is required for several location-based services such as advanced driver assistance systems, driverless cars, predicting driver’s intent, among many other emerging applications. Yet, current outdoor localization techniques fail to provide the required accuracy for estimating the car’s lane.In this paper, we present LaneQuest: an accurate and energy-efficient smartphone-based lane detection system. LaneQuest leverages hints from the ubiquitous and low-power inertial sensors available in commodity off-the-shelf smartphones about the car’s motion and its surrounding environment to provide an accurate estimate of the car’s current lane position. For example, a car making a u-turn, most probably, will be in the left-most lane; a car passing by a pothole will be in the pothole’s lane; and the car angular velocity when driving through a curve reflects its lane. Our investigation shows that there are amble opportunities in the environment, i.e. lane “anchors”, that provide cues about the car lane. To handle the ambiguous location, sensors noise, and fuzzy lane anchors; LaneQuest employs a novel probabilistic lane estimation algorithm. Furthermore, it uses an unsupervised crowd-sourcing approach to learn the position and lane span distribution of the different lane-level anchors.Our evaluation results from implementation on different Android devices and driving traces in different cities covering 260 km shows that LaneQuest can detect the different lane-level landmarks with an average precision and recall of more than 91%. This leads to an accurate detection of the exact car lane position 84% of the time, increasing to 92% of the time to within one lane. This comes with a low-energy footprint, allowing LaneQuest to be implemented on the energy-constrained mobile devices.     

一种基于数据驱动传输策略的带宽受限的分布式融合估计器

针对无线网络化多传感器融合估计中存在的网络拥堵、传感器能量有限以及通信带宽有限的问题, 本文以多传感器经通信网络组成的线性离散随机系统为研究对象, 提出了一种基于数据驱动传输策略的带宽受限的分布式融合估计器, 能够在降低传感器数据传输率的同时满足有限带宽的限制. 在目标状态满足高斯性的前提下, 给出了融合估计误差均方差一致有界的条件. 最后通过算例仿真验证所提方法的有效性.     

多传感器异步线性测量系统的数据融合

由于采样速率和传送数据到融合中心的通信延迟的不同,现代工业生产过程中用于对未知的常值或缓变参数进行估计的多传感器通常是异步工作的,且受到加性测量噪声的干扰。在最小二乘估计意义下,对于测量噪声互不相关的多传感器异步线性测量系统,提出了集中式和分布式递推参数估计数据融合算法,两种算法完全等价,且都是全局最优的。数值仿真实验的结果表明,通过利用多传感器的测量数据,增大了对参数测量的数据流和数据率,传感器测量参数的估计准确度得到明显改善。     

A delay-aware schedule method for distributed information fusion with elastic and inelastic traffic

Information fusion is an efficient way to detect the specified events and extract useful information, especially in the context of big data. As a large-scale data-gathering system, Internet of Things (IoT) has the traffic with the mixed timing characteristics. The real-time observations with various delay constraints and the non-real-time observations are needed in information fusion. In order to guarantee the performance of Distributed Information Fusion (DIF), the paper focuses on the communication mechanism from the perspective of real-time delivery of sensing data. An online scheduling algorithm and its distributed implementation, named Delay-Guaranteed CSMA, are proposed. Both the timing constraints and the historical transmission statistics of sensors are taking into consideration. The simulation results have shown that the proposed policy achieves good delay-guaranteed satisfaction. The goal of real-time data delivery for distributed information fusion is achieved.     

Sensor selection strategies for state estimation in energy constrained wireless sensor networks

Wireless Sensor Networks (WSNs) enable a wealth of new applications where remote estimation is essential. Individual sensors simultaneously sense a dynamic process and transmit measured information over a shared channel to a central base station. The base station computes an estimate of the process state by means of a Kalman filter. In this paper we assume that, at each time step, only a subset of all sensors are selected to send their observations to the fusion center due to channel capacity constraints or limited energy budget. We propose a multi-step sensor selection strategy to schedule sensors to transmit for the next T steps of time with the goal of minimizing an objective function related to the Kalman filter error covariance matrix. This formulation, in a relaxed convex form, defines an unified framework to solve a large class of optimization problems over energy constrained WSNs. We offer some numerical examples to further illustrate the efficiency of the algorithm.     

卡尔曼滤波在两轮自平衡代步车姿态检测中的应用

介绍了一种MENS加速度计、陀螺仪与嵌入式微控制器相结合的两轮自平衡代步车姿态检测系统。针对加速度计和陀螺仪测量分别存在噪声干扰和随机漂移误差,采用卡尔曼滤波实现传感器数据融合,补偿传感器测量误差,得到车体姿态的最优估计。将该算法移植到姿态检测系统的微控制器中,测试结果表明卡尔曼信息融合可以有效提高系统检测精度。     

Covariance intersection based image fusion technique with application to pansharpening in remote sensing

Image fusion of multi-spectral images and panchromatic images has been widely applied to imaging sensors. Multi-spectral images are rich in spectral information whereas panchromatic images have relatively higher spatial resolution. In this paper, we consider the image fusion as an estimation problem, that is to estimate the ideal scene of multi-spectral images at the resolution of panchromatic images. We propose a method of combining the covariance intersection (CI) principle with the expectation maximization (EM) algorithm to develop a novel image fusion approach. In contrast to other fusion methods, the proposed scheme takes cross-correlation among data sources into account, and thus provides consistent and accurate estimates through convex combinations. Since the covariance information is usually unknown in practice, the EM method is employed to provide a maximum likelihood estimate (MLE) of the covariance matrix. Real multi-spectral and panchromatic images are used to evaluate the effectiveness of the proposed EM-CI method. The proposed algorithm is found to preserve both the spectral information of the multi-spectral image and the high spatial resolution information of the panchromatic image more effectively than the conventional image fusion techniques.     

Decentralized robust set-valued state estimation in networked multiple sensor systems

This paper addresses a decentralized robust set-valued state estimation problem for a class of uncertain systems via a data-rate constrained sensor network. The uncertainties of the systems satisfy an energy-type constraint known as an integral quadratic constraint. The sensor network consists of spatially distributed sensors and a fusion center where set-valued state estimation is carried out. The communications from the sensors to the fusion center are through data-rate constrained communication channels. We propose a state estimation scheme which involves coders that are implemented in the sensors, and a decoder–estimator that is located at the fusion center. Their construction is based on the robust Kalman filtering techniques. The robust set-valued state estimation results of this paper involve the solution of a jump Riccati differential equation and the solution of a set of jump state equations.     

On infinite-horizon sensor scheduling

In this paper we consider the problem of infinite-horizon sensor scheduling for estimation in linear Gaussian systems. Due to possible channel capacity, energy budget or topological constraints, it is assumed that at each time step only a subset of the available sensors can be selected to send their observations to the fusion center, where the state of the system is estimated by means of a Kalman filter. Several important properties of the infinite-horizon schedules will be presented in this paper. In particular, we prove that the infinite-horizon average estimation error and the boundedness of a schedule are independent of the initial covariance matrix. We further provide a constructive proof that any feasible schedule with finite average estimation error can be arbitrarily approximated by a bounded periodic schedule. We later generalized our result to lossy networks. These theoretical results provide valuable insights and guidelines for the design of computationally efficient sensor scheduling policies.     

通信受限下网络化多传感器系统序贯卡尔曼滤波加权融合

针对通信受限下网络化多传感器系统难以实时滤波的问题,提出实时序贯滤波融合方法和故障诊断方法.首先基于周期性分组传输通信策略,采用序贯卡尔曼滤波方法,对当前时刻访问融合中心的传感器组进行局部滤波,并导出剩余传感器组的最优局部估计,进而得到线性最小方差意义下的最优融合估计.利用残差加权平方和方法对发生故障的传感器进行定位,仿真结果验证了所提出算法的有效性.