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In this demo, I use the EM algorithm with a Rauch-Tung-Striebel smoother and an M step, which I ve recently derived, to train a two-layer perceptron, so as to classify medical data (kindly provided by Steve Roberts and Will Penny from EE, Imperial Co
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《Optimal State Estimation - Kalman, H Infinity, and Nonlinear Approaches》 一书的配套源码，包括了Kalman Filter、Hinf Filter、Particle Filter等的Matlab源码,《Optimal State Estimation- Kalman, H Infinity, and Nonlinear Approaches》source code，including Matlab code of Kalm
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学习扩展卡尔曼滤波气的基本文件，可以随便下载并讨论-This is a tutorial on nonlinear extended Kalman filter (EKF). It uses the standard EKF fomulation to achieve nonlinear state estimation. Inside, it uses the complex step Jacobian to linearize the nonlinear dynamic system. Th
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The Unscented Kalman Filter (UKF) is a novel development in the field. The idea is to produce several sampling points (Sigma points) around the current state estimate based on its covariance. Then, propagating these points through the nonlinear map t
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在机动目标跟踪中，机动目标模型是机动目标跟踪的基本要素之一，一般希望机动目标模型能准确表征目标机动时的各种运动状态。比较常用的模型有匀速运动(CV)模型、匀加速运动(CA) 模型、时间相关模型（Singer）和机动目标“当前”统计模型。上述模型均采用机动频率表征目标的机动情况。在应用当中，通常采用固定的机动频率，这就表示机动目标的机动时间是一定的，而实际上机动目标的机动时间是不断变化的，也就是说机动频率是不断变化的，采用固定机动频率必然会带来误差。采样周期在0.5—2S时，机动频率越小跟踪精度越
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基于非线性干扰观测器的直升机滑模反演控制-Based on nonlinear disturbance observer helicopter sliding inversion control
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An implementation of Unscented Kalman Filter for nonlinear state estimation.-Nonlinear state estimation is a challenge problem. The well-known Kalman Filter is only suitable for linear systems. The Extended Kalman Filter (EKF) has become a standarded
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EKF仅仅利用了非线性函数Taylor展开式的一阶偏导部分（忽略高阶项），常常导致在状态的后验分布的估计上产生较大的误差，影响滤波算法的性能，从而影响整个跟踪系统的性能。最近，在自适应滤波领域又出现了新的算法——无味变换Kalman滤波器（Unscented Kalman Filter-UKF）。UKF的思想不同于EKF滤波，它通过设计少量的σ点，由σ点经由非线性函数的传播，计算出随机向量一、二阶统计特性的传播。因此它比EKF滤波能更好地迫近状态方程的非线性特性，从而比EKF滤波具有更高的估计精
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非线性滤波方法，主要包括EKF(扩展卡尔曼滤波)与UKF（无迹卡尔曼滤波），对于非线性状态、参数估计的学习有很大的帮助-Nonlinear filtering methods, including EKF (EKF) and UKF (unscented Kalman filter) for nonlinear state estimation is very helpful in learning
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将卡尔曼滤波器用于非线性状态预测，代码具有极高的参考价值。-An implementation of Extended Kalman Filter for nonlinear state estimation.
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本文讨论了小波神经网络在机动多目标跟踪中的应用,多目标跟踪就是主体为了维持对多个目标(客体)当前状态的估计而对所接收的量测信息进行处理的过程。以非线性大规模并行分布式处理为特征的神经网络可以解决传统的目标跟踪方法的难以解决的计算量组合爆炸问题以及需要确定机动目标的数学模型的问题, 将小波分析原理与神经网络相融合,提出了基于小波神经网络的目标跟踪方法来提高系统的学习能力、表达能力以及机动多目标状态的估计精度。-This article discusses the application of wa
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EM算法在神经网络中的应用，可以用来进行视频数据分类。-In this demo, I use the EM algorithm with a Rauch-Tung-Striebel smoother and an M step, which I ve recently derived, to train a two-layer perceptron, so as to classify medical data (kindly provided by Steve Roberts and Wil
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非线性扩展卡尔曼滤波算法的matlab程序-Descr iption:This is a tutorial on nonlinear extended Kalman filter (EKF).
It uses the standard EKF fomulation to achieve nonlinear state estimation.
Inside, it uses the complex step Jacobian to linearize the nonlinear dyn
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基于非线性动力系统的无迹卡尔曼滤波matlab程序-onlinear state estimation is a challenge problem. The well-known Kalman Filter is only suitable for linear systems.
The Extended Kalman Filter (EKF) has become a standarded formulation for nonlinear state estimation.
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This function demonstrates a simple implementation of the basic particle filter. It follows faithfully the first example from the paper: 'Novel Approach To Nonlinear/Non-Gaussian Bayesian State Estimation' by Gordon et al.
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EKF_PF 基于扩展kalman的粒子滤波 可解决非线性状态估计问题-EKF_PF particle filter based on extended Kalman can solve nonlinear state estimation problems
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针对非线性系统的卡尔曼滤波状态估计 很有参考价值，可做修改-Kalman filter for state estimation of nonlinear systems of great reference value, do modification
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Standard EKF fomulation to achieve nonlinear state estimation
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非线性滤波工具箱(nft)
包括：
1. Kalman filter
2. Extended Kalman filter
3. Iterated Kalman filter
4. Second order filter
5. Unscented Kalman filter
6. Divided difference filters
7. Central Difference Filter
8. Gaussian sum method
9. Point
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H无穷控制针对马尔科夫调变系统的非脆弱控制研究。(This paper gives attention to the issue of
nonfragile state estimation for a class of Markov jump
systems with repeated scalar nonlinearities and redundant
channels.)
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