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联邦滤波器广泛应用于多传感器信息融合领域,联邦滤波中的信息分配原则影响滤波精度.针对联邦Kalman滤波器进行改进,采用基于估计协方差阵奇异值动态确定信息分配系数.对子滤波器进行重置时,采用新的重置方法,保证了子滤波器误差协方差阵的对称性,确保Kalman滤波器的一致收敛稳定性.新的联邦滤波算法允许每个状态分量拥有不同的动态信息分配因子,从而改进了联邦滤波信息融合的精度.设计了SINS/GPS/电子罗盘组合导航系统,仿真结果说明,与传统联邦滤波算法相比,改进的联邦滤波器估计精度得到了提高,可以更好地对SINS误差进行校准,提高系统的精度. 相似文献
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针对无卫星信号环境中单兵人员导航定位需求,设计了一种基于自包含传感器的单兵导航系统,重点研究了惯性传感器和压力传感器组合的零速区间检测算法,并通过对单兵导航系统背景磁场误差进行补偿来计算航向角,实现了速度观测量和航向观测量的准确提取。在此基础上,采用Kalman滤波器对系统状态误差进行估计,并对惯性导航解算结果中的累积误差进行修正。最后,在实际路线上开展了单兵导航系统定位实验,实验结果表明,行人在矩形路线终点位置处的位置误差为0.42m,占行走总路程的0.33%,从而证明了零速修正和航向修正能有效提高单兵导航系统的定位精度。 相似文献
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为了对零点配置/回路传函恢复(ZP/LTR)方法无法保证闭环系统稳定性的缺陷进行改进,在设计过程中引入了Kalman滤波器,通过选取特殊的参数,使Kalman滤波器回路逼近并取代原目标回路,从而在保留ZP/LTR方法性能的同时,通过保证Kalman滤波器回路的稳定性保证了最后闭环系统的稳定性。研究还证明了如果按文中方法选取参数,则所得的Kalman滤波器回路是稳定的,从而最终保证了闭环系统的稳定性。最后的实例验证了所得的结论。 相似文献
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设计了一种基于GPS辅助以DR和DM的组合导航定位的联邦滤波器,采用GPS、DR和DM组合导航定位的设计方案的基础上,提出一种先分散式再局部集中联邦滤波器并采用一种简化的自适应联邦滤波器算法对各定位数据进行融合优化,并进行了仿真.仿真结果表明本文设计的联邦卡尔曼滤波器自适应算法对多传感器系统进行数据处理,能够提供一种最佳估计途径,在容错、数据容量及降低系统费用等方面,都比集中卡尔曼滤波器更为优越,使误差进一步减小.该设计与一般的分散卡尔曼滤波器比较,在信息综合方面更加快捷. 相似文献
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针对惯性/卫星组合导航系统易受干扰或自主性不足等问题,引入偏振光传感器和光流传感器,分别建立航向角和速度量测方程,以辅助惯性导航系统,提出了一种基于惯性/偏振光/光流的自主导航方法。同时,为实现惯性传感器、偏振光传感器和光流传感器等多传感器的融合,设计了无迹Kalman滤波器。为验证该方法的有效性,以六足步行机器人为对象开展仿真和实验验证。结果表明,在没有卫星信号源的情况下,仅依靠机器人自身感知,可实现较高精度的机器人位姿估计,实现了不依赖于卫星导航信号的自主导航,提升了导航系统的自主性。 相似文献
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自适应高阶容积卡尔曼滤波在目标跟踪中的应用 总被引:1,自引:1,他引:0
针对传统容积卡尔曼滤波(CKF)在系统状态发生突变时估计精度下降的问题,将强跟踪滤波(STF)算法与高阶容积卡尔曼滤波(HCKF)算法相结合,提出了一种自适应高阶容积卡尔曼滤波(AHCKF)方法。该算法采用高阶球面-相径容积规则,可获得高于传统CKF的估计精度,同时在HCKF算法中引入STF,通过渐消因子在线修正预测误差协方差阵,强迫残差序列正交,提高了算法的鲁棒性,增强了算法应对系统状态突变等不确定因素的能力。将提出的AHCKF算法应用于具有状态突变的机动目标跟踪问题并进行数值仿真,仿真结果表明,AHCKF算法在系统状态发生突变的情况下表现出良好的滤波性能,有效地避免了状态突变造成的滤波精度下降,较传统的CKF、HCKF、交互式多模型-容积滤波(IMM-CKF)和自适应容积卡尔曼滤波(ACKF)算法有更强的鲁棒性和系统自适应能力。 相似文献
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This paper is concerned with the adaptive robust cubature Kalman filtering problem for the case that the dynamics model error and the measurement model error exist simultaneously in the satellite attitude estimation system. By using Hubel-based robust filtering methodology to correct the measurement covariance formulation of cubature Kalman filter, the proposed filtering algorithm could effectively suppress the measurement model error. To further enhance this effect and reduce the impact of the dynamics model error, two different adaptively robust filtering algorithms, one with the optimal adaptive factor based on the estimated covariance matrix of the predicted residuals and the other with multiple fading factors based on strong tracking algorithm, are developed and applied for the satellite attitude estimation. The quaternion is employed to represent the global attitude parameter, and three-dimensional generalized Rodrigues parameters are introduced to define the local attitude error. A multiplicative quaternion error is derived from the local attitude error to maintain quaternion normalization constraint in the filter. Simulation results indicate that the proposed novel algorithm could exhibit higher accuracy and faster convergence compared with the multiplicative extended Kalman filter, the unscented quaternion estimator, and the adaptive robust unscented Kalman filter. 相似文献
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移动机器人通过跟随领航员以实现导航是一种便捷的导航方式。针对行人引领导航中的领航员定位问题,提出了一种基于视觉的行人引领导航方法。该方法利用卡尔曼滤波器预测领航员的位置和尺度,并基于深度神经网络的行人检测器提供的结果更新滤波器的状态。为了关联检测结果和卡尔曼滤波器预测结果,提出了2个指标用于衡量两者之间的关联性。其中,为了提高在多个行人中辨认领航员的可靠性,创新性地引入了一个孪生神经网络,使用该网络全连接层提取的特征作为候选人的特征描述子,并通过计算特征之间的余弦距离来验证检测器检测到的行人身份。此外,当卡尔曼滤波器跟踪领航员失败时,综合考虑检测结果和孪生网络的判断结果重新初始化卡尔曼滤波器,以实现持续的领航员定位。视频实验和物理机器人实验验证了所提出的方法的有效性和可靠性。 相似文献
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The paper deals with state estimation problem of nonlinear non-Gaussian discrete dynamic systems for improvement of accuracy and consistency. An efficient new algorithm called the adaptive Gaussian-sum square-root cubature Kalman filter(AGSSCKF) with a split-merge scheme is proposed. It is developed based on the squared-root extension of newly introduced cubature Kalman filter(SCKF) and is built within a Gaussian-sum framework. Based on the condition that the probability density functions of process noises and initial state are denoted by a Gaussian sum using optimization method, a bank of SCKF are used as the sub-filters to estimate state of system with the corresponding weights respectively, which is adaptively updated. The new algorithm consists of an adaptive splitting and merging procedure according to a proposed split-decision model based on the nonlinearity degree of measurement. The results of two simulation scenarios(one-dimensional state estimation and bearings-only tracking) show that the proposed filter demonstrates comparable performance to the particle filter with significantly reduced computational cost. 相似文献
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基于改进容积卡尔曼滤波的奇异避免姿态估计 总被引:2,自引:0,他引:2
利用矢量进行卫星姿态估计可以归结为非线性滤波问题。为了提高卫星姿态估计的精度,利用龙贝格-马尔塔(LM)迭代算法改进了容积卡尔曼滤波(CKF)。继而,提出改进容积卡尔曼滤波与四元数结合的容积四元数估计器(CQE),有效地避免了卫星大角度机动出现的奇异现象。进一步,给出了一种与影子修正罗德里格参数切换的容积修正罗德里格参数估计器(CME)。仿真对比表明,初始误差较大时容积修正罗德里格参数估计器具有更好的收敛速度和鲁棒性。 相似文献
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基于自适应容积卡尔曼滤波方法的涡扇发动机气路部件故障诊断 总被引:2,自引:1,他引:1
针对涡扇发动机气路部件故障诊断中参数存在不同的噪声统计特性,提出了一种自适应平方根容积卡尔曼滤波(ASRCKF)器的自适应滤波方法.该方法直接利用基于3阶容积积分方法近似发动机的非线性统计特性,用于替代非线性无迹卡尔曼滤波方法的系统模型,避免了滤波过程参数选取的问题;采用移动窗口法对噪声协方差矩阵进行自适应估计,提高了算法对不同统计特性噪声的自适应能力和滤波精度.通过对发动机气路部件健康参数蜕化过程仿真结果表明:ASRCKF方法相比平方根容积卡尔曼滤波(SRCKF)方法,精度提高40%~50%,对不同噪声信号具有更好的适应能力. 相似文献
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Gas-path performance estimation plays an important role in aero-engine health management, and Kalman Filter(KF) is a well-known technique to estimate performance degradation. In previous studies, it is assumed that different kinds of sensors are with the same sampling rate, and they are used for state estimation by the KF simultaneously. However, it is hard to achieve state estimation using various kinds of sensor measurements at the same sampling rate due to a complex network and physical characteristic differences between sensors, especially in an advanced multisensor architecture. For this purpose, a multi-rate sensor fusion using the information filtering approach is proposed based on the square-root cubature rule, which is called Multi-rate Squareroot Cubature Information Filter(MSCIF) to track engine performance degradation. Soft measurement synchronization of the MSCIF is designed to provide a sensor fusion condition for multiple sampling rates of measurement, and a fault sensor is isolated by maximum likelihood validation before state estimation. The contribution of this paper is to supply a novel multi-rate informationfilter approach for sensor fault tolerant health estimation of an aero-engine in a multi-sensor system. Tests are conducted for aero-engine performance degradation estimation with multiple sampling rates of sensor measurement on both digital simulation and semi-physical experiment.Experimental results illustrate the superiority of the proposed algorithm in terms of degradation estimation accuracy and robustness to sensor failure in a multi-sensor system. 相似文献