组合导航信息融合技术研究

为了防止组合导航局部滤波器性能变化而调整信息分配的问题,本文分析了联合卡尔曼滤波算法中参考系统、主滤波器以及局部滤波器的特点,具体描述了组合导航系统的联合滤波算法,提出了一种基于广义特征值分解的自适应信息分配策略.该结果表明,在工程实践上,采用组合导航信息融合技术可以解决根据组合导航局部滤波器性能随时调整信息分配策略的问题.     

信息融合技术在INS/GPS/DVS组合导航系统中的应用

本文探讨了INS/GPS/DVS组合导航系统中的信息融合技术问题,介绍了联邦滤波算法的基本思想,根据INS、GPS、DVS的导航特点建立了较完整的状态方程,并以INS/GPS及INS/DVS组合分别构成组合导航系统的子滤波器,建立了相应的量测方程;阐述了主滤波器的最优估计融合算法,并给出了该算法与集中滤波算法等效的条件;最后采用无反馈的模式对组合导航系统中的联邦滤波算法进行了计算机仿真与分析.结果表明,联邦滤波算法可以充分利用各导航传感器的信息,并在减少计算量的前提下有效地提高导航系统的精度,具有较大的工程应用价值.     

多传感器组合导航系统的联邦UKF算法研究

多传感器组合导航系统是一种典型的非线性系统,为了提高其滤波精度,本文提出了多传感器组合导航系统联邦 UKF 算法。 首先,在建立多传感器组合导航系统的非线性状态方程及线性量测方程的基础上,对标准 UKF 进行了简化;然后,以简 化 UKF 为基础提出了多传感器组合导航系统的联邦 UKF 算法,并设计了姿态融合算法及其故障检测函数以验证该算法的容 错性能;最后,以 GNSS / CNS / SINS 多传感器组合导航系统为例进行了仿真验证。 仿真结果表明,相对于联邦线性卡尔曼滤波 器,联邦 UKF 算法可提高位置及姿态精度约 25. 8%、22. 2%,同时继承了联邦线性卡尔曼滤波器的容错性能。     

Self‐tuning fusion Kalman filter weighted by scalars and its convergence analysis for multi‐channel autoregressive moving average signals

For the multi‐sensor multi‐channel autoregressive (AR) moving average signals with white measurement noises and an AR‐colored measurement noise, a multi‐stage information fusion identification method is presented when model parameters and noise variances are partially unknown. The local estimators of model parameters and noise variances are obtained by the multidimensional recursive instrumental variable algorithm and correlation method, and the fused estimators are obtained by taking the average of the local estimators. They have the strong consistency. Substituting them into the optimal information fusion Kalman filter weighted by scalars, a self‐tuning fusion Kalman filter for multi‐channel AR moving average signals is presented. Applying the dynamic error system analysis method, it is proved that the proposed self‐tuning fusion Kalman filter converges to the optimal fusion Kalman filter in a realization, so that it has asymptotic optimality. A simulation example for a target tracking system with three sensors shows its effectiveness. Copyright © 2014 John Wiley & Sons, Ltd.     

基于改进WOA的鲁棒增广扩展Kalman滤波器设计

本文针对一类非线性系统,提出基于广义系统的鲁棒增广扩展Kalman滤波器,结合改进鲸群优化算法寻优系统噪声,以精确估计系统状态量以及并发执行器和传感器故障。首先,视故障为系统的状态变量,建立广义系统,将非线性系统的故障估计转化为非线性广义系统的状态估计。其次,提出鲁棒上界以降低线性化误差对估计精度的影响。然后,利用改进鲸群算法寻优系统噪声,以优化鲁棒增广扩展Kalman滤波器。最后,给出F-16飞机的纵向运动数值模型,使用本文方法与自适应无迹Kalman滤波器以及基于鲸群算法的鲁棒增广扩展Kalman滤波器进行对比仿真,仿真结果表明,相较于其他两种算法,本文方法的故障估计均方根误差降低了50%左右,验证了其优越性。     

FPGA‐based embedded platform for fiber optic gyroscope signal denoising

This paper presents System on Chip (SoC) implementation of a proposed denoising algorithm for fiber optic gyroscope (FOG) signal. The SoC is developed using an Auxillary Processing Unit of the proposed algorithm and implemented in the Xilinx Virtex‐5‐FXT‐1136 field programmable gate array. SoC implementation of this application is first of its kind. The proposed algorithm namely adaptive moving average‐based dual‐mode Kalman filter (AMADMKF) is a hybrid of adaptive moving average and Kalman filter (KF) technique. The performance of the proposed AMADMKF algorithm is compared with the discrete wavelet transform and KF of different gains. Allan variance analysis, standard deviation and signal to noise ratio (SNR) are used to measure the efficiency of the algorithm. The experimental result shows that AMADMKF algorithm reduces the standard deviation or drift of the signal by an order of 100 and improves the SNR approximately by 80 dB. The Allan variance analysis result shows that this algorithm also reduces different types of random errors of the signal significantly. The proposed algorithm is found to be the best suited algorithm for denoising the FOG signal in both the static and dynamic environments. Copyright © 2013 John Wiley & Sons, Ltd.     

小型无人飞行器机动过程中航姿互补滤波算法研究

磁惯导系统(MINS)广泛应用于小型无人飞行器的导航控制,能对加速度计、磁强计和陀螺仪等传感器的数据进行融合,得到航向与姿态信息,也被称为航姿参考系统(AHRS)。在频域实现数据融合的互补滤波算法是AHRS中的一种可靠姿态估计方法,具有简捷高效的优点。将基于不同传递函数及各种航姿表示形式的姿态互补滤波归纳为统一的广义互补滤波算法(GCF),分析该类算法中的乘性姿态误差,并引入运动加速度补偿方法,可以改善载体机动状态下的姿态精度。数值仿真及实验结果显示,GCF的滤波效果与无人机常用的卡尔曼滤波算法相当,而处理时间仅为后者的1/20,且GCF具有良好的数值稳定性,配合运动加速度补偿算法可有效消除线加速度对航姿测量的不利影响,尤其适合低成本、小型无人机应用场合。     

基于FPGA的GHz宽带中频数字采集系统的设计

针对大带宽复杂电磁信号的测试分析,介绍了一种基于FPGA的GHz带宽中频数字采集系统的设计,论述了系统的硬件总体设计和信号处理算法设计方案。采集系统ADC以1.6GHz采样率对中频信号进行采样,然后通过FPGA进行数字信号处理,通过对传统多相滤波算法的改进,设计了FPGA的高速大带宽信号的数字滤波方案,并采用多路并行处理的方法设计了高速数字正交混频算法,实现了最大为640 MHz的分析带宽和带内多路信号分析的功能。     

Fuzzy logic controller for three‐level shunt active filter compensating harmonics and reactive power

In this paper, the use of a three‐level inverter as a shunt active power filter is carried out, taking advantage of the benefits of multi‐level inverter, namely, the reduction both in the overall switching losses and in total harmonic distortion. The main focus of this article is to investigate the potentialities of the inverter employed as shunt active power filter on the compensation of the reactive power and the mitigation of harmonics drawn from a nonlinear load and unbalanced sources. The most previously reported three‐level inverter‐based shunt active power filters have been controlled and monitored through conventional controllers, which require a complicated mathematical model. In order to overcome this problem, an extended intelligent controller is proposed for a three‐level shunt active power filter. The aim of the proposed fuzzy logic control algorithm is to improve the behavior of voltage across the floating capacitors in steady/dynamic states and to minimize the switches commutations by taking into account the references of the harmonic currents injected in the network. The proposed control strategy has been simulated, and the obtained results prove that it is very successful. Copyright © 2015 John Wiley & Sons, Ltd.     

Application of Kalman filter to hot‐spot temperature monitoring in oil‐immersed power transformer

Winding hot‐spot temperature (HST) is an important factor that affects the insulation life of an oil‐immersed power transformer. Thus, precise prediction and close monitoring of HSTs are necessary to avoid thermal damage. In this paper, a differential equation for HST prediction is presented, which takes into consideration the effects of the top‐oil temperature variations and thermal dynamics of the load. A discrete form of this equation based on the framework of the Kalman filter (KF) algorithm was used to establish a real‐time estimation model for the HST. The KF‐based model was validated by a sample heat‐run test involving a transformer setup in the laboratory. Moreover, the proposed model was applied to a real, large power transformer and compared with the classical IEEE‐Annex G method. Results show that the HSTs estimated by the KF‐based model are closer to the measured values. The exhibited potential applicability and generality in real‐time prediction for HST demonstrate that the proposed model can be employed for online monitoring of HSTs for large power transformers. © 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

离散卡尔曼滤波器滤波精度与噪声统计模型准确度的关系

针对多维渐近稳定线性离散系统,推导了使用铁初始条件和噪声统计的卡尔曼滤波器实际估计误差协方关矩阵,并基于该矩阵分析了滤波精度与模型精度的关系,指出使用非准确的初始条件和噪声统计模型 仍能构造出与最优卡尔曼滤器等效的滤波器,组合导航仿真系统的仿真验证了文中的结论。     

A new kernel RLS algorithm for systems with bounded noise

In this paper, we propose a new nonlinear set‐membership recursive least‐squares algorithm. The algorithm draws on a linear set‐membership filter in conjunction with kernels for nonlinear processing. Set‐membership algorithms exploit a priori model information that directly, or indirectly, prescribes dynamic constraints on the solution space. Such information is disregarded by conventional approaches. Kernel methods provide an implicit mapping of the data in a high‐dimensional feature space where linear techniques are applied. Computations are done in the initial space by means of kernel functions. In this work, we develop a kernel‐based version of a set‐membership filter that belongs to a class of optimal bounding ellipsoid algorithms. Optimal bounding ellipsoid algorithms compute ellipsoidal approximations to regions in the parameter space that are consistent with the observed data and the model assumptions. Experiments involving stationary and nonstationary data are presented. Compared with existing kernel adaptive algorithms, the proposed algorithm offers an enhanced performance and sparsity, conjugated with better tracking capabilities.     

基于粒子滤波的运动机器人目标定位

机器人完成各种应用的前提是准确获知自身及运动目标的相对位置,由于机器人在运动控制的过程中自身携带的传感器获取的位置和角度信息存在误差,会导致移动机器人在目标定位过程中出现误差。为提高定位的准确性,提出了基于相对定位的方法,建立目标运动的相对运动模型,并基于观测距离和角度的测量方程运用粒子滤波方法对运动目标进行定位,实验与仿真结果表明,在不同强度的非高斯噪声影响下,粒子滤波算法都能够有效的对其进行定位,且具良好的精度。     

Risk‐sensitive filtering for nonlinear Markov jump systems on the basis of particle approximation

In this paper, the risk‐sensitive filtering method that relaxes the dependence on model accuracy is extended to nonlinear Markov jump systems (MJSs). In the method, the so‐called reference probability technique together with particle approximation is utilized to derive the risk‐sensitive filter in nonlinear non‐Gaussian framework. The novelty of the proposed approach is that a ‘risky’ interacting resampling step is performed to both moderate the modeling uncertainties and to solve the problem of particle explosion. A designer‐chosen parameter named risk‐sensitive parameter allows us to make a trade‐off between the filtering accuracy for the nominal model and the robustness to uncertainties. With a meaningful example, it shows that the developed method can outperform the widely used method‐particle filter and interacting multiple model‐particle filter in nonlinear MJSs with uncertainties. Copyright © 2011 John Wiley & Sons, Ltd.     

Unscented summation information‐weighted consensus filter for distributed sensor networks with incomplete information

In order to improve network scalability and fault tolerance, the distributed sensor networks are desirable. However, the distributed state estimation becomes challenging when some sensors have insufficient information due to restricted observability, and/or have imparity information due to unequal measurement‐noise covariances. Centralized summation information‐fusion (CSI) model is presented which performs weighted least‐squares estimation for all measurement information to achieve the optimal centralized state estimation. The CSI model revises the initialization and covariance propagation in the original information‐weighted consensus filter (ICF). Since centralized information fusion is a summation mode and is approached by the average consensus protocol, all the covariances involved in the CSI model contain the information regarding the total number of nodes. The artificially preset initial values are considered as measurement information and fused in accordance with the CSI model. By combining the CSI model with unscented transform, distributed unscented summation information‐weighted consensus filter (USICF) is proposed. USICF realizes the nonlinear estimation in the context of highly incomplete information. Theoretical analysis and experimental verification showed that USICF achieves better performance than UICF that is based on ICF.     

Position and Rotation Estimation for Mobile Robots in Outside of Recording Path Using Ego‐Motion

In this research, estimating the position and rotation of a mobile robot outside of a recording path is realized by applying ego‐motion to view‐based navigation. The ego‐motion is calculated based on the differences in 3D positions of SURF feature points between recording and current images obtained by a Kinect sensor. In conventional view‐based navigation, it is difficult to plan another path when people and objects are found in the recording path. By using the authors’ proposed estimation method, it is possible to realize flexible path planning in actual environments that include people and objects. Based on the results of experiments performed in actual indoor environments, the authors evaluated measurement accuracy for the robot's position and rotation estimated under their method, and confirmed the viability of their method for actual environments including people and objects.     

Adaptive fault tolerant control for a class of multi‐input multi‐output nonlinear systems with both sensor and actuator faults

Compared with the fault diagnosis, detection, and isolation literature, very few results are available to discuss control algorithms directly for multi‐input multi‐output nonlinear systems with both sensor and actuator faults in the fault tolerant control literature. In this work, we present a fault tolerant control algorithm to address the system output stabilization problem for a class of multi‐input multi‐output nonlinear systems with both parametric and nonparametric uncertainties, subject to sensor and actuator faults that can be both multiplicative and additive. All elements of the sensor measurements and actuator components can be faulty. Besides, the control input gain function is not fully known. Backstepping method is used in the analysis and control design. We show that under the proposed control scheme, uniformly ultimate boundedness of the system output is guaranteed, while all closed‐loop system signals stay bounded. In the cases where the sensor faults are only multiplicative, exponential convergence of the system state variables into small neighbourhoods around zero is guaranteed. An illustrative example on a robot manipulator model is presented in the end to further demonstrate the effectiveness of the proposed control scheme.     

一种基于IMMPDA-UKF的机动目标跟踪算法

为了实现对在航捷点附近做机动运动目标的精确跟踪,提出采用不敏卡尔曼滤波（UKF）作为底层的滤波算法,解算出方位和俯仰的角度变化率,通过角度变化率解算出目标的切向速度,在过航捷时建立新的跟踪模型,将切向速度扩充到观测方程中,并结合交互多模型概率数据关联算法（IMMPDA）实现对过航捷机动目标的跟踪。仿真结果表明,该算法跟踪精度高,在航捷点附近无论是转弯机动还是加速运动,都可以保持对目标的持续跟踪,稳定性较高,可以直接应用于工程实践。     

State estimation for bilinear systems through minimizing the covariance matrix of the state estimation errors

This paper considers the state estimation problem of bilinear systems in the presence of disturbances. The standard Kalman filter is recognized as the best state estimator for linear systems, but it is not applicable for bilinear systems. It is well known that the extended Kalman filter (EKF) is proposed based on the Taylor expansion to linearize the nonlinear model. In this paper, we show that the EKF method is not suitable for bilinear systems because the linearization method for bilinear systems cannot describe the behavior of the considered system. Therefore, this paper proposes a state filtering method for the single‐input–single‐output bilinear systems by minimizing the covariance matrix of the state estimation errors. Moreover, the state estimation algorithm is extended to multiple‐input–multiple‐output bilinear systems. The performance analysis indicates that the state estimates can track the true states. Finally, the numerical examples illustrate the specific performance of the proposed method.     

Multiple‐model multiple‐hypothesis filter with Gaussian mixture reduction

We address the problem of state estimation for Markov jump nonlinear systems and present a modified version of the multiple‐model and multiple‐hypothesis (M³H) algorithm to suboptimally solve it. In such systems, the exact filter must track an exponentially increasing number of possible trajectories. Therefore, practical solutions must approximate the exact filter trading off filter precision for computational speed. In this contribution, we employ Gaussian mixture reduction methods in the merging of hypotheses of the original M³H. Thus, information from both the analog and digital states is used to merge the hypotheses, whereas only information from the digital state is employed in the original method. In our numerical results, we show that the proposed method outperforms the original M³H when increased precision constraints are imposed to the filter.