共查询到18条相似文献,搜索用时 736 毫秒
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把一种基于stirling内插公式的插值滤波器(DDF),应用于单站外辐射源无源定位中的非线性滤波问题。与传统扩展kalman滤波(EKF)相比,插值滤波器避免了非线性方程泰勒展开求解的过程,同时实现复杂性和计算量都很小。计算机仿真表明,一阶插值滤波(DD1)滤波精度相当EKF,而二阶插值滤波(DD2)滤波精度接近甚至略优于无轨迹kalman滤波(UKF)。 相似文献
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在处理目标跟踪等动态系统实时估计问题中,通常采用EKF作为状态估计方法提高估计精度。由于EKF进行非线性估计存在一些缺陷,将系统进行线性化近似存在估计误差,从而影响目标跟踪的精度。为了获得更高的估计精度,介绍了几种非线性滤波算法,包括unscented卡尔曼滤波算法、简单粒子滤波算法以及无味粒子滤波算法(UPF)。分析了这几种算法的原理和实现,对各种算法的适应性进行了比较。通过目标跟踪仿真实验,表明UKF、PF较EKF估计精度和收敛速度有所提高。 相似文献
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基于EKF的纯方位目标状态滤波器的性能依赖状态初值的选取,为了有效地提高估计的收敛速度,提出了一种滤波器状态初始化方法.首先,简要阐述了修正极坐标系下的推广卡尔曼滤波算法(EKF).然后,基于非线性最小二乘法的思想,推导了一种滤波器状态初始化方法.针对实际应用背景,提出一种组合滤波器结构并进行了仿真验证.结果表明,该算法收敛速度快,滤波精度与EKF相当. 相似文献
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研究了无味粒子滤波器的基本思想和具体算法实现步骤,在给出的闪烁噪声统计模型基础上,将PF、UKF和UPF算法应用在雷达目标跟踪中,解决了闪烁噪声情况下的雷达目标跟踪问题,仿真结果表明,UPF的状态估计性能优越。 相似文献
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动力定位(DP)船舶状态估计的准确性是影响其在海面上沿期望航迹运行或位置固定的关键因素。在DP状态估计研究中,当遇到观测噪声反常或噪声协方差与算法不符等情况时,无迹卡尔曼滤波(UKF)无法调整算法参数以适应海洋环境的变化,严重影响着状态估计的精度。鉴于此,提出一种基于误差序列协方差估计的自适应UKF,利用观测变量残差更新观测噪声协方差矩阵R。设计基于自适应UKF的状态估计器,对DP船舶纵荡、横荡和艏摇3个重要状态变量进行估计。数值仿真结果表明,提出的自适应UKF能明显降低纵荡、横荡和艏摇3个状态变量的估计误差,状态估计的准确性和滤波平滑性均优于传统UKF算法。 相似文献
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针对SINS/GPS组合导航系统的特点,建立了系统的非线性误差模型。根据系统状态方程为非线性而观测方程为线性的特点,将一种简化的UKF方法(Rao-Blackwellisation Additive Unscented Kalman Filter,RBAUKF)用于SINS/GPS组合导航系统中,RBAUKF采用较少的采样点数目和简化的更新算法,降低了计算复杂度。最后,在机动条件下,进行了SINS/GPS组合导航实验仿真。仿真结果表明,RBAUKF相比EKF具有更高的滤波精度,更适合在SINS/GPS组合导航系统中应用。 相似文献
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为了解决非线性、非高斯系统目标跟踪问题,研究了一种新的滤波方法——高斯粒子滤波算法。通过基于重要性采样和蒙特卡罗模拟方法得到一高斯分布来近似未知状态变量的后验分布。并讨论了此算法在机动目标非线性转弯运动中的跟踪应用,与粒子滤波算法相比,其优点是不需要重采样步骤。在闪烁噪声下比较了高斯粒子滤波器、粒子滤波器和扩展卡尔曼滤波器在滤波精度、运算时间等方面的差异,仿真结果表明该算法性能优于其他算法。 相似文献
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Comparison of robust H∞ filter and Kalman filter for initial alignment of inertial navigation system
There are many filtering methods that can be used for the initial alignment of an integrated inertial navigation system.This paper discussed the use of GPS,but focused on two kinds of filters for the initial alignment of an integrated strapdown inertial navigation system (SINS).One method is based on the Kalman filter (KF),and the other is based on the robust filter.Simulation results showed that the filter provides a quick transient response and a little more accurate estimate than KF,given substantial process noise or unknown noise statistics.So the robust filter is an effective and useful method for initial alignment of SINS.This research should make the use of SINS more popular,and is also a step for further research. 相似文献
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Currently there are different approaches to filter algorithms based on the Kalman filter. One of the most used filter algorithms is the Ensemble Kalman Filter (EnKF). It uses a Monte Carlo approach to the filtering problem. Another approach is given by the Singular Evolutive Extended Kalman (SEEK) and Singular Evolutive Interpolated Kalman (SEIK) filters. These filters operate explicitly on a low-dimensional error space which is represented by an ensemble of model states. The EnKF and the SEIK filter have been implemented within a parallel data assimilation framework in the Finite Element Ocean Model FEOM. In order to compare the filter performances of the algorithms, several data assimilation experiments are performed. The filter algorithms have been applied with a model configuration of FEOM for the North Atlantic to assimilate the sea surface height in twin experiments. The dependence of the filter estimates on the represented error subspace is discussed. In the experiments the SEIK algorithm provides better estimates than the EnKF. Furthermore, the SEIK filter is much cheaper in terms of computing time. 相似文献
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Detection of weak underwater signals is an area of general interest in marine engineering. A weak signal detection scheme
was developed; it combined nonlinear dynamical reconstruction techniques, radial basis function (RBF) neural networks and
an extended Kalman filter (EKF). In this method chaos theory was used to model background noise. Noise was predicted by phase
space reconstruction techniques and RBF neural networks in a synergistic manner. In the absence of a signal, prediction error
stayed low and became relatively large when the input contained a signal. EKF was used to improve the convergence rate of
the RBF neural network. Application of the scheme to different experimental data sets showed that the algorithm can detect
signals hidden in strong noise even when the signal-to-noise ratio (SNR) is less than −40d B. 相似文献