Evaluation of linear Kalman filter processing geodetic kinematic measurements

The purpose of this paper is to assess the efficiency of the linear Kalman filter as a method for the estimation of kinematic process observed with electronic tacheometer. On the assumption that the kinematic process is observed with only one measurement system there is no redundancy of the measurements. For evaluation of the unknown system state and its statistics in real time, other methods such as filters have to be used instead of classical geodetic adjustment. In this contribution, the efficiency of the three-dimensional linear Kalman filter model, i.e., continuous Wiener process acceleration model, in combination with the law on transfer of variances and covariances, is controlled using a known reference trajectory and statistical tests. The results of numerical tests confirmed the appropriateness of the model to evaluate geodetic kinematic measurements, where no redundant observations are available.     

Intelligent fuzzy weighted input estimation method for the forces generated by an operating rotating machine

This paper presents a novel inverse method which efficiently and robustly estimates the vibration forces of a rotating machine mounted upon isolators under the operating condition. This new input estimation method includes the Kalman filter (KF) and the recursive least square estimator (RLSE), weighted by adopting a fuzzy weighting factor based on the fuzzy logic inference system. The excellent performance of this inverse method is demonstrated by solving the vibration problem and comparing it with constant weighting factors. The results reveal that this method has better feasibility and effectiveness in estimating vibratory forces.     

Decentralized state estimation for a large-scale spatially interconnected system

A decentralized state estimator is derived for the spatially interconnected systems composed of many subsystems with arbitrary connection relations. An optimization problem on the basis of linear matrix inequality (LMI) is constructed for the computations of improved subsystem parameter matrices. Several computationally effective approaches are derived which efficiently utilize the block-diagonal characteristic of system parameter matrices and the sparseness of subsystem connection matrix. Moreover, this decentralized state estimator is proved to converge to a stable system and obtain a bounded covariance matrix of estimation errors under certain conditions. Numerical simulations show that the obtained decentralized state estimator is attractive in the synthesis of a large-scale networked system.     

多旋翼姿态解算中的改进自适应扩展Kalman算法

提出了一种改进的Sage-Husa自适应扩展Kalman滤波算法,用于保证多旋翼无人机在噪声统计特性未知且时变、振动为主要扰动源、姿态角高动态变化等飞行条件下飞行姿态角解算的精度与稳定性。该算法采用微机电系统陀螺仪实时动态解算的姿态角方差估计系统噪声方差;并采用自适应滤波算法在线估计量测噪声方差,从而保证滤波的精度与稳定性;同时引入滤波器收敛性判据,结合强跟踪Kalman滤波算法来抑制滤波发散。飞行实验与分析表明:改进算法解算的俯仰角与横滚角均方根误差分别为1.722°和1.182°,明显优于常规的Sage-Husa自适应滤波算法。实验还显示:改进的算法自适应能力强、实时性好、精度高、运行可靠,能够满足多旋翼无人机自主飞行的需要,若对参数进行适当修改,还可应用于其它动态性能要求较高的导航信息测量系统中。     

一种用于散货码头的GPS精度提升算法

GPS是广泛应用于散货码头的自动化和远程监控中的一种设备.但由于噪声的存在,GPS接收器并不能提供高精度的定位.主要阐述了一种基于卡尔曼滤波器快速去除噪声的方法,并提高GPS系统的定位精度.通过实验法,卡尔曼滤波器的关键参数首先被确定,然后通过卡尔曼滤波器的自动递归运算,快速获取正确的GPS定位数据.最后,还通过了一组对比实验,验证了这种算法的有效性.     

视觉伺服机器人系统的无标定目标运动估计

机器人视觉伺服系统是机器人领域一重要的研究方向。该文建立了三自由度平面机器人视觉伺服系统,着重研究在摄像机与机器人坐标系无标定的情况下对目标的运动估计,提出了一种无标定算法,采用Kalman滤波完成目标的运动估计,并验证了它的有效性。     

自适应卡尔曼滤波器在火箭弹落点估计中的应用

利用雷达对火箭弹一段飞行过程中的参数进行量测,对火箭弹落点进行了准确估计,实现了火箭弹的轨迹修正。采用具有自适应调节滤波增益矩阵的卡尔曼滤波器,结合质点弹道模型,建立了自适应卡尔曼滤波弹道模型,完成了对三坐标雷达探测的一段火箭弹飞行参数的野值处理与滤波,并对火箭弹落点进行外推。数值仿真结果表明,经自适应调节的卡尔曼滤波器滤波后,弹道量测信号中的野值与噪声被有效去除,且滤波方差可以在短时间内收敛。根据滤波时间与落点估计误差的关系,采用滤波时间为8-10 s 方案,可得到最佳的落点估计。     

Parameter estimation for a dual-rate system with time delay

This paper investigates the parameter estimation problem of the dual-rate system with time delay. The slow-rate model of the dual-rate system with time delay is derived by using the discretization technique. The parameters and states of the system are simultaneously estimated. The states are estimated by using the Kalman filter, and the parameters are estimated based on the stochastic gradient algorithm or the recursive least squares algorithm. When concerning state estimate of the dual-rate system with time delay, the state augmentation method is employed with lower computational load than that of the conventional one. Simulation examples and an experimental study are given to illustrate the proposed algorithm.     

Improvement on state estimation for discrete-time LTI systems with measurement loss

In this paper, we present a novel state estimation procedure for the LTI systems with loss of data at the output measurement channels. The proposed methodology aims at compensating such output measurement losses through an innovative design methodology which is based on the so-called linear prediction (LP) and Kalman filter theories. A compensated observation signal is first reconstructed using an LP subsystem and then supplied to a discrete-time Kalman filter in the closed-loop framework. We show that, under suitable assumptions, it is possible to reconstruct the lost data using an straightforward algorithm with the capability of associating an optimal filter order. A mass-spring-damper case study subject to measurement loss is provided to demonstrate some of the promising results of our proposed algorithm. Simulation results illustrate that the proposed estimation methodology is too far superior than those offered in the literature.     

Analysis of the optimal sampling rate for state estimation in sensor networks with delays

When addressing the problem of state estimation in sensor networks, the effects of communications on estimator performance are often neglected. High accuracy requires a high sampling rate, but this leads to higher channel load and longer delays, which in turn worsens estimation performance. This paper studies the problem of determining the optimal sampling rate for state estimation in sensor networks from a theoretical perspective that takes into account traffic generation, a model of network behaviour and the effect of delays. Some theoretical results about Riccati and Lyapunov equations applied to sampled systems are derived, and a solution was obtained for the ideal case of perfect sensor information. This result is also interesting for non-ideal sensors, as in some cases it works as an upper bound of the optimisation solution.     

Laboratory-scale experiments on wind turbine nacelle movement estimation

The effect of nacelle motion should be considered when calculating the wind speed relative to the wind turbine structure, which is essential in wind turbine control and performance testing. A Kalman filter approach is applied to estimate the nacelle motion of a wind turbine. Information from several accelerometers and strain gauges which are installed on the wind turbine tower is combined with the Kalman filter. An optimization algorithm is used to choose the optimal locations for strain gauge and accelerometer installation. A laboratory-scale experimental rig which mimics the tower and nacelle of the wind turbine is constructed to evaluate the performance of the proposed estimator algorithm. The usefulness of the proposed algorithm is validated by these laboratory-scale experimental results.     

Dual extended Kalman filter for combined estimation of vehicle state and road friction

Vehicle state and tire-road adhesion are of great use and importance to vehicle active safety control systems. However, it is always not easy to obtain the information with high accuracy and low expense. Recently, many estimation methods have been put forward to solve such problems, in which Kalman filter becomes one of the most popular techniques. Nevertheless, the use of complicated model always leads to poor real-time estimation while the role of road friction coefficient is often ignored. For the purpose of enhancing the real time performance of the algorithm and pursuing precise estimation of vehicle states, a model-based estimator is proposed to conduct combined estimation of vehicle states and road friction coefficients. The estimator is designed based on a three-DOF vehicle model coupled with the Highway Safety Research Institute(HSRI) tire model; the dual extended Kalman filter (DEKF) technique is employed, which can be regarded as two extended Kalman filters operating and communicating simultaneously. Effectiveness of the estimation is firstly examined by comparing the outputs of the estimator with the responses of the vehicle model in CarSim under three typical road adhesion conditions(high-friction, low-friction, and joint-friction). On this basis, driving simulator experiments are carried out to further investigate the practical application of the estimator. Numerical results from CarSim and driving simulator both demonstrate that the estimator designed is capable of estimating the vehicle states and road friction coefficient with reasonable accuracy. The DEKF-based estimator proposed provides the essential information for the vehicle active control system with low expense and decent precision, and offers the possibility of real car application in future.     

Road vehicle state estimation using low-cost GPS/INS

Assuming known vehicle parameters, this paper proposes an innovative integrated Kalman filter (IKF) scheme to estimate vehicle dynamics, in particular the sideslip, the heading and the longitudinal velocity. The IKF is compared with the 2DoF linear bicycle model, the triple Kalman filter (KF) and a model-based KF (MKF) in a simulation environment. Simulation results show that the proposed IKF is superior to other KF designs (both Kinematic KF and MKF) on state estimation when tyre characteristics are within the linear region (i.e. manoeuvres below 55 kph).     

Incorporating delayed measurements in an improved high-degree cubature Kalman filter for the nonlinear state estimation of chemical processes

The on-line estimation of process quality variables has a large impact on the advanced monitoring and control techniques of chemical processes. The present study offers an improved high-degree cubature Kalman filter (HCKF) to solve the nonlinear state estimation problem of high-dimensional chemical processes. We substituted the Cholesky decomposition in the HCKF filter with a diagonalization transformation of the matrix. In addition, we enhanced numerical stability and estimation accuracy. On this basis, we present one nonlinear state estimation method based on the sample-state augmentation and improved HCKF to handle issues with delayed measurements. Finally, we used the nonlinear state estimation experiments for the polymerization process to validate the proposed method. The numerical results indicated the achievement of state estimation with higher accuracy and better stability following the effective utilization of the delayed measurements for nonlinear chemical processes.     

Recursive parameter estimation for Hammerstein-Wiener systems using modified EKF algorithm

This paper focuses on the recursive parameter estimation for the single input single output Hammerstein-Wiener system model, and the study is then extended to a rarely mentioned multiple input single output Hammerstein-Wiener system. Inspired by the extended Kalman filter algorithm, two basic recursive algorithms are derived from the first and the second order Taylor approximation. Based on the form of the first order approximation algorithm, a modified algorithm with larger parameter convergence domain is proposed to cope with the problem of small parameter convergence domain of the first order one and the application limit of the second order one. The validity of the modification on the expansion of convergence domain is shown from the convergence analysis and is demonstrated with two simulation cases.     

An approach to improve the GPS positioning performance under urban environment conditions

Satellite deficiency degrades the performance of GPS single point positioning (SPP) in city urban areas. If the number of observed satellites is smaller than that of the unknown independent parameters in the data processing, extended Kalman filter (EKF) methods will usually be employed to transit the variable values epoch by epoch. In this case, the prediction model in EKF is very important for the positioning results. This paper focuses on the receiver clock offset models in the prediction equation. The investigation shows that, the clock offset model with clock shift is able to improve the accuracy of the clock offset prediction for internal crystal clocks within a short time span, compared with the clock offset model without clock shift. Besides, in order to further improve the performance of positioning in the case of satellite deficiency, an adaptive EKF is developed to enhance the clock prediction. The experiments show that this approach can improve the positioning accuracy in the scenarios that only fewer satellites are available.     

Estimator design for re-entry targets

This study proposes a trajectory estimation scheme for tactical ballistic missiles (TBMs). Target information acquired from the ground-based radar system is investigated by incorporating input estimation (IE) and extended Kalman filtering techniques. In addition to estimate the missile's position and velocity, our special focus is put on the estimation of the TBMs evasive acceleration and ballistic coefficient. In the demonstrative example, radar measurement errors are served as specifications while characterizing the acquirable zone of the ground-based radar system. Effect of the proposed design is fully verified by examining the estimation performance.     

Implementation of a Kalman filter in positioning for autonomous vehicles, and its sensitivity to the process parameters

Modern autonomous vehicles are using more than one method for performing the positioning task. The most common positioning methods for indoor vehicles are odometry for relative positioning and triangulation for absolute positioning. In many cases a Kalman filter is required to merge the data from the positioning systems and determines the vehicle position based on error analysis of the measurements and calculation procedures. A Kalman filter is particularly advantageous for on-the-fly positioning, which is performed while the vehicle is in motion. This paper presents the implementation of a Kalman filter in ROBI — an AGV for material handling in a manufacturing environment. The performance of the filter in estimating the position of the AGV and the effect of motion parameters (speed, path curvature, beacon layout etc.) on filter accuracy are shown.