Search - adaptive extended Kalman filter

Search - adaptive extended Kalman filter - List

[Other resource] Anadaptivefilteringapproachtotargettracking DL : 0: A method is presented for augmenting an extended Kalman filter with an adaptive element. The resulting estimator provides robustness to parameter uncertainty and unmodeled dynamics.-A method is presented for augmenting an ext Kalman ended with an adaptive filter element. T he resulting estimator provides robustness to parameter uncertainty and unmodeled dynamics .
Update : 2008-10-13 Size : 331.11kb Publisher : rifer
[Other resource] kalman DL : 0: Carrier-phase synchronization can be approached in a general manner by estimating the multiplicative distortion (MD) to which a baseband received signal in an RF or coherent optical transmission system is subjected. This paper presents a unified modeling and estimation of the MD in finite-alphabet digital communication systems. A simple form of MD is the camer phase exp GO) which has to be estimated and compensated for in a coherent receiver. A more general case with fading must, however, allow for amplitude as well as phase variations of the MD. We assume a state-variable model for the MD and generally obtain a nonlinear estimation problem with additional randomly-varying system parameters such as received signal power, frequency offset, and Doppler spread. An extended Kalman filter is then applied as a near-optimal solution to the adaptive MD and channel parameter estimation problem. Examples are given to show the use and some advantages of this scheme.
Update : 2008-10-13 Size : 808.2kb Publisher : 吴大亨
[Industry research] Anadaptivefilteringapproachtotargettracking DL : 0: A method is presented for augmenting an extended Kalman filter with an adaptive element. The resulting estimator provides robustness to parameter uncertainty and unmodeled dynamics.-A method is presented for augmenting an ext Kalman ended with an adaptive filter element. T he resulting estimator provides robustness to parameter uncertainty and unmodeled dynamics .
Update : 2025-03-07 Size : 331kb Publisher : rifer
[Other] kalman DL : 0: Carrier-phase synchronization can be approached in a general manner by estimating the multiplicative distortion (MD) to which a baseband received signal in an RF or coherent optical transmission system is subjected. This paper presents a unified modeling and estimation of the MD in finite-alphabet digital communication systems. A simple form of MD is the camer phase exp GO) which has to be estimated and compensated for in a coherent receiver. A more general case with fading must, however, allow for amplitude as well as phase variations of the MD. We assume a state-variable model for the MD and generally obtain a nonlinear estimation problem with additional randomly-varying system parameters such as received signal power, frequency offset, and Doppler spread. An extended Kalman filter is then applied as a near-optimal solution to the adaptive MD and channel parameter estimation problem. Examples are given to show the use and some advantages of this scheme.
Update : 2025-03-07 Size : 808kb Publisher : 吴大亨
[matlab] ExtendedKalmanfilter DL : 1: 自适应扩展卡尔曼滤波算法Extended Kalman filter-Adaptive Extended Kalman Filter Extended Kalman filter algorithm
Update : 2025-03-07 Size : 1kb Publisher : aeou
[Other] IMM DL : 0: 用于机动目标跟踪的交互式多模型算法完整实现程序，无BUG-For maneuvering target tracking interactive multiple model algorithm for the full realization of program, no BUG
Update : 2025-03-07 Size : 1kb Publisher : 汪俊
[AI-NN-PR] Adaptive-Online-Learning DL : 0: 基于EKF的神经网络自适应在线学习算法，包含例子和文档。-We show that a hierarchical Bayesian modeling approach allows us to perform regularization in sequential learning. We identify three inference levels within this hierarchy: model selection, parameter estimation, and noise estimation. In environments where data arrive sequentially, techniques such as cross validation to achieve regularization or model selection are not possible. The Bayesian approach, with extended Kalman filtering at the parameter estimation level, allows for regularization within a minimum variance framework. A multilayer perceptron is used to generate the extended Kalman filter nonlinear measurements mapping. We describe several algorithms at the noise estimation level that allow us to implement on-line regularization.We also show the theoretical links between adaptive noise estimation in extended Kalman filtering, multiple adaptive learning rates, and multiple smoothing regularization coefficients.
Update : 2025-03-07 Size : 384kb Publisher : xiaochen
[Software Engineering] N_sensors-12-13212-v2 DL : 0: This paper presents an adaptive information fusion method to improve the accuracy and reliability of the altitude measurement information for small unmanned aerial rotorcraft during the landing process. Focusing on the low measurement performance of sensors mounted on small unmanned aerial rotorcraft, a wavelet filter is applied as a pre-filter to attenuate the high frequency noises in the sensor output. Furthermore, to improve altitude information, an adaptive extended Kalman filter based on a maximum a posteriori criterion is proposed to estimate measurement noise covariance matrix in real time. Finally, the effectiveness of the proposed method is proved by static tests, hovering flight and autonomous landing flight tests.
Update : 2025-03-07 Size : 379kb Publisher : Clovis
[e-language] extend_kalman_filter DL : 0: 这是卡尔曼滤波的一个扩展形式，即自适应卡尔曼滤波，应用举例是变波长LMS自适应卡尔曼滤波算法-This is an extended Kalman filter forms, namely adaptive Kalman filtering, application example is the variable wavelength LMS adaptive Kalman filter algorithm
Update : 2025-03-07 Size : 2kb Publisher : 董金鲁
[Industry research] 00573617 DL : 0: A new approach to improved filter design is presented for the radar tracking problem. An idealized version of the extended Kalman filter, which is unrealizable in practice, is constructed using a universal linearization concept, and then it is utilized for the development of a practical tracking filter. The resulting filter tunes the measurement error variance in an adaptive manner to account for the measurement nonlinearities effectively.
Update : 2025-03-07 Size : 185kb Publisher : aida
[Other] Railway-track-geometry-determination-using-adapti DL : 0: The Kalman filter has been widely used to solve different filtering problems especially in tracking and estimation applications. Besides its simplicity, robustness and optimality, the application of Kalman filter to nonlinear systems can be complicated. The most common method is to use extended Kalman filter which linearizes the nonlinear model so that the standard Kalman filter can be applied. In this paper, a new adaptive Kalman filtering algorithm is designed and applied to a railway track geometry surveying system which has been designed in the scope of a research project at Yildiz Technical University/Turkey. Track gauge, super-elevation, gradient and track axis coordinates which are the railway geometrical parameters can be instantly determined while making measurements by using adaptive Kalman filtering algorithm integrated surveying system
Update : 2025-03-07 Size : 866kb Publisher : Gomaa Haroun