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基于自适应Kalman滤波的二维有噪子带信号恢复 总被引:1,自引:0,他引:1
基于子带信号的多通道表示(multichannel representation)和输入信号的动态特征,本文尝试推出了一种多分辨率状态空间模型,它与带相加子带噪声的滤波器组(Filter Bank)系统是等价的,于是使有噪子带信号的恢复可表述为相应多分辨率态空间模型的最优状态估计问题。进一步又利用信号的向量动态模型,发展了适于二维Kalman滤波的二维多分辨率状态空间模型,根据信号行为的分布,目标平面(object plane)可分割为不同的区域并用不同的向量动态模型来表征信号的非平衡分布,计算机数字仿真结果进一步证实了本文所提出了二维多分辨率Kalman滤波器性能的优越性。 相似文献
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AKF与EFRLS在动态目标跟踪性能上的比较 总被引:1,自引:1,他引:0
卡尔曼滤波是具有递推估计形式的最优滤波,但最优性的获得是在过程噪声和观测噪声统计特性已知的前提下得到的。然而,在大量的动态目标跟踪实际问题中噪声具有不确定性,因而有必要研究在噪声不确定下动态目标的跟踪算法以满足实际问题的需要。文中介绍自适应Kalman滤波对过程噪声方差的估计以及推广的遗忘因子最小二乘法对状态估计的递推公式,并且在平均误差最小准则下通过计算机仿真比较两种方法对动态目标的跟踪性能.仿真结果表明,在不确定噪声下自适应Kalman滤波能够取得比推广的遗忘因子递推最小二乘法更好的跟踪性能。 相似文献
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新型自适应Kalman滤波算法及其应用 总被引:5,自引:0,他引:5
为防止滤波发散和提高系统的实时性,提出了一种新的自适应Kalman滤波算法.该算法利用滤波异常判据获得一个滤波状态因子,通过滤波状态因子确定量测噪声协方差阵的值,在线调整噪声的统计特性实现自适应滤波.将该算法应用到惯导/双星组合导航系统中,并和常规Kalman滤波和简化的Sage-Husa自适应滤波算法进行仿真比较.仿真结果表明,在滤波精度与简化Sage-Husa自适应滤波相当的情况下,新算法简化了运算,提高了实时性. 相似文献
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针对防空兵高炮火控系统中的滤波问题,应用矩阵实验室(MATLAB)实现对目标运动参数的滤波仿真.介绍了MATLAB软件的相关知识,阐述了利用MATLAB进行卡尔曼滤波仿真的实现过程.通过实例,利用MATLAB对某型雷达卡尔曼滤波的过程和结果进行了进一步的仿真.结果表明,利用MATLAB进行卡尔曼滤波仿真,对提高整个火控... 相似文献
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Yulong Huang Yonggang Zhang Ning Li Zhen Shi 《Circuits, Systems, and Signal Processing》2016,35(11):3981-4008
In this study, the authors investigate the filtering and smoothing problems of nonlinear systems with correlated noises at one epoch apart. A pseudomeasurement equation is firstly reconstructed with a corresponding pseudomeasurement noise, which is no longer correlated with the process noise. Based on the reconstructed measurement model, new Gaussian approximate (GA) filter and smoother are derived, from which Kalman filter and smoother can be obtained for linear systems. For nonlinear systems, different GA filters and smoothers can be developed through utilizing different numerical methods for computing Gaussian-weighted integrals involved in the proposed solution. Numerical examples concerning univariate nonstationary growth model, passive ranging problem, and target tracking show the efficiency of the proposed filtering and smoothing methods for nonlinear systems with correlated noises at one epoch apart. 相似文献
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Optimal signal reconstruction in noisy filter bank systems:multirate Kalman synthesis filtering approach 总被引:2,自引:0,他引:2
Bor-Sen Chen Chin-Wei Lin You-Li Chen 《Signal Processing, IEEE Transactions on》1995,43(11):2496-2504
A multirate Kalman synthesis filter is proposed in this paper to replace the conventional synthesis filters in a noisy filter bank system to achieve optimal reconstruction of the input signal. Based on an equivalent block representation of subband signals, a state-space model is introduced for an M-band filter bank system with subband noises. The composite effect of the input signal, analysis filter bank, decimators, and interpolators is represented by a multirate state-space model. The input signal is embedded in the state vector, and the corrupting noises in subband paths are generally considered as additive noises. Hence, the signal reconstruction problem in the M-band filter bank systems with subband noises becomes a state estimation procedure in the resultant multirate state-space model. The multirate Kalman filtering algorithm is then derived according to the multirate state-space model to achieve optimal signal reconstruction in noisy filter bank systems. Based on the optimal state estimation theory, the proposed multirate Kalman synthesis filter provides the minimum-variance reconstruction of the input signal. Two numerical examples are also included. The simulation results indicate that the performance improvement of signal reconstruction in noisy filter bank systems is remarkable 相似文献
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Maneuvering Target Tracking with High-Order Correlated Noise – A Multirate Kalman Filtering Approach
A multirate Kalman filtering algorithm for target tracking with high-ordercorrelatednoise is proposed. The measurement signal is first split into subbands usinga filter bank.Then, the correlated noise in each subband is modeled using a first-order ARprocess and the AR parametersare identified online. Finally, a multirate Kalman reconstruction filter isused to obtain the state estimate.This method can be directly incorporated into the IMM algorithm, resulting inan effective tracking scheme.Simulations show that the new multirate processing scheme can significantlyimprove tracking performance. 相似文献
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基于卡尔曼滤波和粒子滤波器级联模型的静基座惯导初始对准算法及仿真 总被引:4,自引:1,他引:3
传统静基座初始对准主要采用扩展卡尔曼滤波技术。扩展卡尔曼滤波器本质上要求系统近似线性。当近似线性要求得不到满足,会产生很大的偏差,大大降低对准精度,甚至会发散。粒子滤波是一种新出现的滤波技术,对模型不作线性限制,非常适于解决非线性问题,估计精度大大高于传统扩展卡尔曼滤波器。但是,要求维数不能太高,否则会产生计算灾难问题。惯导误差模型的维数较高,这使得粒子滤波技术无法实际应用于初始对准中。本文通过对静基座误差方程进行分析,提出了一种级联模型。将原有模型分解为级联的两个子模型,每个子模型的状态变量维数都很低,然后对两个子模型分别应用卡尔曼滤波器和粒子滤波器进行滤波处理。实验仿真结果表明,这种基于卡尔曼滤波器和粒子滤波器级联模型的算法降低了计算量,大大提高了初始对准的精度,具有重要的现实意义。 相似文献
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针对基于小波变换与Kalman滤波相结合的多尺度联合估计方法中存在的问题,本文利用新的系统分块技术与多尺度变换方法相结合,建立一个动态系统基于时域与频域相结合的多尺度联合滤波器.首先,将时域中描述的状态方程和观测方程改写为块状态方程和块观测方程;利用多尺度变换技术在时域和频域中联合描述它们;结合Kalman滤波与顺序滤波的思想,建立了一类应用于动态系统的多尺度估计联合滤波器.新滤波器不仅保留了传统Kalman滤波器的实时性和递归性等优良性质,而且在滤波过程中还具有对随机状态信号进行多尺度分析的能力.计算机仿真实验验证了利用新估计器得到的估计精度可与利用传统Kalman滤波器得到的估计精度相媲美. 相似文献