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1.
自校正滤波器在卫星定位中的应用   总被引:1,自引:0,他引:1  
设计了一种新的基于ARMA模型的自校正卡尔曼滤波器,对卫星定位误差模型参数进行分段在线估计,根据误差模型估计参数直接求取滤波增益阵.并提出了一种直接计算滤波误差方差阵的方法,对两个不同的定位系统进行信息融合.仿真结果表明,在未知噪声统计特性的情况下,自校正卡尔曼滤波器能有效过滤观测噪声,基于它的信息融合规则能够有效提高定位精度.计算过程简单,并可以在线建模.  相似文献   

2.
基于极大后验估计的自适应容积卡尔曼滤波器   总被引:1,自引:0,他引:1  
丁家琳  肖建 《控制与决策》2014,29(2):327-334
针对标准的容积卡尔曼滤波器(CKF) 设计需要精确已知噪声先验统计知识的问题, 提出一种自适应CKF 算法. 该算法在滤波过程中, 利用Sage-Husa 极大后验估值器对噪声的统计特性进行在线估计和修正, 有效地提高了CKF 的估计精度和数值稳定性. 在某些情况下, 噪声协方差估计会出现异常现象使得滤波发散, 进而提出了相应的改进方法. 仿真结果表明了自适应CKF 算法的可行性和有效性, 且明显改善了标准CKF 算法的滤波效果.  相似文献   

3.
分析了基于卡尔曼滤波器的残差检验法对传感器缓变故障检测的不敏感性原因.针对双余度传感器缓变故障检测,采取了先故障判断后故障定位的故障检测策略,并提出了一种基于移动伪正常状态的残差构造方法.数学仿真验证了改进方法比传统方法更能够及时准确地识别双余度传感器缓变故障.  相似文献   

4.
针对目标跟踪中过程噪声统计特性未知和状态分量可观测度差而导致滤波精度不高甚至滤波发散的问题,提出了一种复合自适应滤波算法。我该算法在滤波过程中,利用Sage-Husa噪声估计器在线估计过程噪声,用可观测度分析方法抑制状态分量可观测度差对滤波器的不良影响。在滤波过程中实时估计和修正过程噪声的统计特性,同时对观测度差的分量通道进行滤波增益衰减,以减小状态估计误差,提高滤波算法的估计精度。解决了一类过程噪声统计特性未知且系统状态分量可观测度差的状态估计问题。仿真结果显示,提出的复合自适应滤波算法对比传统Sage-Husa滤波和可观测度分析方法能够抑制过程噪声时变和状态分量可观测度差对滤波器的不良影响,具有更高的估计精度。  相似文献   

5.
基于极大后验估计和指数加权的自适应UKF滤波算法   总被引:8,自引:0,他引:8  
赵琳  王小旭  孙明  丁继成  闫超 《自动化学报》2010,36(7):1007-1019
针对传统Unscented卡尔曼滤波器(Unscented Kalman filter, UKF)在噪声先验统计未知时变情况下非线性滤波精度下降甚至发散的问题, 设计了一种带噪声统计估计器的自适应UKF滤波算法. 首先根据极大后验(Maximum a posterior, MAP)估计原理, 推导出一种次优无偏MAP常值噪声统计估计器; 接着在此基础之上, 采用指数加权的方法, 给出了时变噪声统计估计器的递推公式; 最后对自适应UKF算法进行了性能分析. 相比于传统UKF, 该自适应UKF算法在噪声统计未知时变情况下不仅滤波依然收敛, 滤波精度及稳定性显著提高, 而且其具有应对噪声变化的自适应能力. 仿真实例验证了其有效性.  相似文献   

6.
基于参数估计的传感器故障诊断的改进方法   总被引:1,自引:0,他引:1  
针对基于参数估计的非线性系统传感器故障诊断方法中存在的滤波稳定性差、估计精度低的缺点,提出了卡尔曼滤波与小波滤波相结合的方法.在将传感器故障参数都等效为偏差型故障参数的基础上,通过增大强跟踪滤波器算法中的量测噪声方差和系统噪声方差,使其大于实际噪声方差,以提高滤波器的稳定性和故障检测的快速性,同时引入小波滤波以提高对故障参数的估计精度.仿真实验表明,该方法较好地兼顾了滤波稳定性、估计精度及速度.  相似文献   

7.
姿态确定算法是卫星姿态确定系统的重要组成部分。在姿态确定系统中广泛采用卡尔曼滤波作为姿态确定算法,但是卡尔曼滤波依赖于噪声统计特性的先验知识,采用不精确的噪声统计特性设计卡尔曼滤波器可能会导致较大的估计误差,甚至造成滤波发散。本文针对噪声统计特性的不确定性分别采用了自适应卡尔曼滤波器和预测滤波器估计卫星姿态,通过数学仿真验证在噪声统计特性不确定的情况下。这两种滤波器仍然可以较精确地估计卫星姿态。  相似文献   

8.
惯性测量单元(IMU)作为水下航行器导航系统关键传感器,其可靠性直接影响航行器的导航性能。为了提高IMU的容错能力,本文提出一种基于无迹卡尔曼滤波(UKF)算法的IMU故障诊断技术。首先根据水下航行器的动力学方程和导航系统特点,建立描述IMU故障与导航状态量关系的解析模型;接着基于UKF非线性滤波的特点,进行导航滤波解算,基于此,提出了解耦矩阵法以实现IMU的故障检测;并且根据无迹卡尔曼滤波器新息正交原理,提出了实时估计IMU故障的方法,从而完成水下航行器IMU故障的在线检测与诊断。最后,通过实际航行数据验证了所提出算法的有效性。  相似文献   

9.
Cubature卡尔曼滤波器(CKF)在非高斯噪声或统计特性未知时滤波精度将会下降甚至发散,为此提出了统计回归估计的鲁棒CKF算法.推导出线性化近似回归和直接非线性回归的鲁棒CKF算法,直接非线性回归克服了观测方程线性化近似带来的不足.具有混合高斯噪声的仿真实例比较了3种Cubature卡尔曼滤波器的滤波性能,结果表明这两种鲁棒CKF滤波精度及估计一致性明显优于CKF,直接非线性回归的CKF的鲁棒性更强,滤波性能更好.  相似文献   

10.
潘健  熊亦舟  张慧  梁佳成 《计算机仿真》2020,37(2):53-56,129
针对复杂环境下传感器噪声未知且不断变化,会导致姿态融合结果不准确的问题,设计了一种基于单新息自适应算法的卡尔曼滤波器,对加速度计和陀螺仪噪声协方差进行在线估计。首先,介绍了能够结合各个传感器优势的无人机姿态融合算法。然后,设计了采用基于单新息自适应算法的卡尔曼滤波器,给出了能够在线估计加速度噪声协方差R和陀螺仪噪声协方差Q的自适应算法。MATLAB仿真表明单新息自适应卡尔曼滤波器在环境噪声变化时,能够更准确地获得无人机的姿态信息,提高了姿态融合精确度,提高了滤波器的鲁棒性。  相似文献   

11.
The networked control system NCS is regarded as a sampled control system with output time-variant delay. White noise is considered in the model construction of NCS. By using the Kalman filter theory to compute the filter parameters, a Kalman filter is constructed for this NCS.By comparing the output of the filter and the practical system,a residual is generated to diagnose the sensor faults and the actuator faults. Finally, an example is given to show the feasibility of the approach.  相似文献   

12.
The networked control system NCS is regarded as a sampled control system with output time- variant delay. White noise is considered in the model construction of NCS. By using the Kalman filter theory to compute the filter parameters ,a Kalman filter is constructed for this NCS. By comparing the output of the filter and the practical system ,a residual is generated to diagnose the sensor faults and the actuator faults. Finally ,an example is given to show the feasibility of the approach.  相似文献   

13.
多故障的奇偶方程-参数估计诊断方法   总被引:6,自引:0,他引:6  
宋华  张洪钺 《控制与决策》2003,18(4):413-417
提出一种将奇偶方程与参数估计相结合的多故障诊断方法。构造了一种新的奇俩方程,其产生的残差仅对一个传感器故障和一个执行器故障敏感。将传感器和执行器故障模型表示成刘度因子和偏差的形式,应用卡尔曼滤没方法对各故障模型参数进行估计。某型号飞机控制系统的仿真结果表明,新方法能对传感器故障和执行器故障同时存在的线性系统进行诊断,有效地估计出各故障的模型参数。  相似文献   

14.
Supervision and control systems rely on signals from sensors to receive information to monitor the operation of a system and adjust manipulated variables to achieve the control objective. However, sensor performance is often limited by their working conditions and sensors may also be subjected to interference by other devices. Many different types of sensor errors such as outliers, missing values, drifts and corruption with noise may occur during process operation. A hybrid online sensor error detection and functional redundancy system is developed to detect errors in online signals, and replace erroneous or missing values detected with model-based estimates. The proposed hybrid system relies on two techniques, an outlier-robust Kalman filter (ORKF) and a locally-weighted partial least squares (LW-PLS) regression model, which leverage the advantages of automatic measurement error elimination with ORKF and data-driven prediction with LW-PLS. The system includes a nominal angle analysis (NAA) method to distinguish between signal faults and large changes in sensor values caused by real dynamic changes in process operation. The performance of the system is illustrated with clinical data continuous glucose monitoring (CGM) sensors from people with type 1 diabetes. More than 50,000 CGM sensor errors were added to original CGM signals from 25 clinical experiments, then the performance of error detection and functional redundancy algorithms were analyzed. The results indicate that the proposed system can successfully detect most of the erroneous signals and substitute them with reasonable estimated values computed by functional redundancy system.  相似文献   

15.
基于大气数据计算机、全球定位系统(GPS)和线加速度计,给出了一种高精度和高可靠性的高度确定算法。根据运动学和传感器测量模型建立了高度测量系统的状态方程和测量方程,利用Kalman滤波方法得到了高度估计,并根据测量信息的冗余设计了系统的故障检测和隔离算法。仿真结果表明:该系统的高度测量具有较高的精度,同时有着较好的可靠性。  相似文献   

16.
Freeway work zone with lane closure can lead to disruption to local traffic and cause significant impacts on mobility, safety and environmental sustainability. To mitigate traffic congestion near work zone area, many variable speed limits (VSL) control approaches have been developed. However, VSL control system, as a critical transportation management system, is prone to the occurrence of traffic sensor faults. Faulty sensors can cause great deviations of traffic measurements and system degradation. Therefore, this study aims to develop a fault-tolerant VSL control strategy for freeway work zone with the consideration of the mainline sensor fault and ramp sensor fault. To analyze the traffic dynamics near work zone area, a traffic flow model has been built first. Then a sliding mode controller in the previous study has been utilized for VSL control. In addition to the traffic states estimated by a Kalman filter, two observers have been developed to provide analytical redundancy of traffic states estimation. By comparing the logarithm of the likelihood estimations from the Kalman filter and two observers, a fault diagnosis scheme has been designed to detect and identify the faults of mainline sensors and ramp sensors. Then the VSL controller can be reconfigured accordingly in case of sensor faults. The proposed system is implemented and evaluated under a realistic freeway work zone environment using traffic simulator SUMO. The results demonstrate that the developed system can accurately detect and identify the sensor faults in real time. Consistent improvements of mobility, safety and sustainability are also achieved under fault-free and sensor faults scenarios.  相似文献   

17.
对于带未知噪声方差的多传感器系统,用相关方法给出了噪声方差的在线估值器,进而基于Riccati方程和按分量标量加权最优融合规则,提出了自校正分量解耦信息融合Kalman滤波器.用动态误差系统分析方法证明了自校正融合Kalman滤波器按实现收敛于最优融合Kalman滤波器,因而具有渐近最优性.一个3传感器跟踪系统的仿真例子说明了其有效性.  相似文献   

18.
Kalman filtering with partial Markovian packet losses   总被引:1,自引:1,他引:0  
We consider the Kalman filtering problem in a networked environment where there are partial or entire packet losses described by a two state Markovian process. Based on random packet arrivals of the sensor measurements and the Kalman filter updates with partial packet, the statistical properties of estimator error covariance matrix iteration and stability of the estimator are studied. It is shown that to guarantee the stability of the Kalman filter, the communication network is required to provide for each of the sensor measurements an associated throughput, which captures all the rates of the successive sensor measurements losses. We first investigate a general discrete-time linear system with the observation partitioned into two parts and give sufficient conditions of the stable estimator. Furthermore, we extend the results to a more general case where the observation is partitioned into n parts. The results are illustrated with some simple numerical examples.  相似文献   

19.
目前旋翼无人机组合导航系统大都使用扩展卡尔曼滤波算法,然而由于导航系统建模误差和传感器测量精度的影响,导航信息解算误差较大。为了改善旋翼无人机的飞行控制效果,应用自适应渐消卡尔曼滤波(Adaptive fading Kalman filter,AFKF)进行旋翼无人机组合导航解算,算法通过实时计算遗忘因子,对过去的数据权重进行削减,以提高扩展卡尔曼滤波算法的自适应能力。应用旋翼无人机真实飞行数据进行仿真,仿真结果表明,自适应渐消卡尔曼滤波算法能够有效抑制建模误差,弥补传感器测量精度不足,改善旋翼无人机组合导航解算结果。  相似文献   

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