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[Industry research] AnadaptiveKalmanfilterfordynamicharmonicstateestim DL : 0: Knowledge of the process noise covariance matrix is essential for the application of Kalman filtering. However, it is usually a difficult task to obtain an explicit expression of for large time varying systems. This paper looks at an adaptive Kalman filter method for dynamic harmonic state estimation and harmonic injection tracking.-Knowledge of the process noise covariance matrix is essential for the application of Kalm an filtering. However, it is usually a difficult task to obtain an expli cit for large expression of time varying system s. This paper looks at an adaptive Kalman filter method for dynamic estimation a harmonic state nd harmonic injection tracking.
Date : 2026-01-07 Size : 410kb User : rifer
[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 .
Date : 2026-01-07 Size : 331kb User : rifer
[Industry research] RobustadaptiveKalmanfilteringwithunknowninputs DL : 0: The standard optimum Kalman filter demands complete knowledge of the system parameters, the input forcing functions, and the noise statistics. Several adaptive methods have already been devised to obtain the unknown information using the measurements and the filter residuals.-The optimum standard Kalman filter demand 's complete knowledge of the system parameters. the input forcing functions. and the noise statistics. Several adaptive met hods have already been devised to obtain the unk nown information using the measurements and th e filter residuals.
Date : 2026-01-07 Size : 927kb User : rifer
[Industry research] RobustlyImplementationoftheAdaptiveInverseControl. DL : 0: This paper addresses the issues relating to the enforcement of robust stability when implementing the Adaptive Inverse Control (AIC) scheme. In this scheme, an adaptive FIR filter is added to a closed-loop system in order to reduce the output error caused by external disturbances, enhancing the performance achieved by linear time-invariant controllers alone.-This paper addresses the issues relating to theenforcement of robust stability when implementing the AdaptiveInverse Control (AIC) scheme. In this scheme, an adaptiveFIR filter is added to a closed-loop system in order toreduce the output error caused by external disturbances, enhancing the performance achieved by linear time-invariantcontrollers alone.
Date : 2026-01-07 Size : 412kb User : rifer
[Industry research] particle DL : 0: 关于粒子滤波关键技术研究,最新国内外文章-Particle filter on the research of key technologies, the latest article at home and abroad
Date : 2026-01-07 Size : 9.79mb User : 华国向
[Industry research] multiscale DL : 0: 几篇多尺度滤波的论文，最新下载的．多尺度滤波在目标跟踪中需要考虑到的-Several multi-scale filter papers, the latest download. Multi-scale filtering in target tracking in the need to take into account the
Date : 2026-01-07 Size : 891kb User : 邱雪娜
[Industry research] particle-filter DL : 1: 粒子滤波，这个里面包含了相关发表的文章，ｐｐｔ　，简介等。还有ｍａｔｌａｂ　代码，（ｕｐｆ，ｕｋｆ）。这是０８年１月在我校开的第一届科学会议时，一个教授给的！比较全面，网站上的都很零碎！所以发一下-the relative materials about parice-filter theory. It includes the paper ,PPt ,and review .Also the matlab code(upf ,ukf).It is more detailed!
Date : 2026-01-07 Size : 3.55mb User : 齐小伟
[Industry research] Controller DL : 0: Controller for kalman filter
Date : 2026-01-07 Size : 1kb User : Younes
[Industry research] FuzzyModel DL : 0: Fuzzy model for kalman filter
Date : 2026-01-07 Size : 2kb User : Younes
[Industry research] 10.1.1.101.7122 DL : 0: it is a development of iterative wiener filter it puts a clear creterion for convergence
Date : 2026-01-07 Size : 602kb User : king_of_rules
[Industry research] paper01 DL : 0: A FFT-based partial matched filter (PMF) acquisition threshold setting method for fast GPS acquisition is presented. Normal distribution and numerical fitting technique instead of real decision variable (DV) distribution are used in derivation to approximate the threshold expression, and the modification method for the threshold expression is also presented. The threshold expression is based on the individual FFT Doppler bin false alarm probability rather than the convention whole detector false alarm probability. The proposed threshold expression can be adjusted according to the false alarm probability, the noncoherent accumulation (NCA) length and the estimated noise variance. Furthermore we discuss the implementation of zero-padding FFT with its advantage and limitation. The performance is superior compared to the reference method in terms of signal-to-noise ratio (SNR) when detection and false alarm probabilities are used as measurement criteria.
Date : 2026-01-07 Size : 274kb User : zohreh
[Industry research] AES2final DL : 0: PHD Filter for Estimating the multitarget tracking
Date : 2026-01-07 Size : 309kb User : N
[Industry research] PHD DL : 0: PHD Filter for Estimating the multitarget tracking
Date : 2026-01-07 Size : 119kb User : N
[Industry research] phdvsmht DL : 0: Probability Hypothesis Density filter versus Multiple Hypothesis Tracking
Date : 2026-01-07 Size : 195kb User : N
[Industry research] getPDF DL : 0: object tracking using particle filter
Date : 2026-01-07 Size : 1.08mb User : roops
[Industry research] KalmanFilterBasedVisualTracking DL : 0: A paper on A Kalman Filter Based Visual Tracking Algorithm for an Object Moving in 3D
Date : 2026-01-07 Size : 470kb User : Vidsocio
[Industry research] kalman_intro DL : 0: particle filter and kalmen filter
Date : 2026-01-07 Size : 155kb User : eee
[Industry research] KFILADAP DL : 0: Experiments with Kalman Gain for a simple noisy measurements - Adaptive Kalman filter technique
Date : 2026-01-07 Size : 2kb User : srivardhan
[Industry research] KFILADPQ DL : 0: Experiments with adaptive kalman filter for a Simple Noisy measurements-Experiments with adaptive kalman filter for a Simple Noisy measurements
Date : 2026-01-07 Size : 2kb User : srivardhan
[Industry research] Particle_filter DL : 0: 基于粒子滤波器的机动目标跟踪技术首先，概要介绍传统的Kalman滤波器，以及有所改进的扩展Kalman滤波器。其次，为了能更好地解决在动态模型为非线性且噪声为非高斯的条件下对机动目标的跟踪问题，通过概率统计理论详细阐述粒子滤波器基本原理。然后，针对不同的使用条件，根据粒子滤波器的基本理论做出适当的修改和整理，就得到了四个相关的粒子滤波器的变型，使用州以JLAB把它们对机动目标的跟踪性能作了详细地计算机模拟仿真且用均方根误差更加精确地进行了比较。最后，把粒子滤波器运用到近年来被人们越来越关注的无线传感器网络中，结合数据融合、后验概率分布的嫡、核表示等算法实现了对网络中机动目标的跟踪，并且使用州叭JLAB对这一应用做了详细的计算机模拟仿真。-Particle filter based target tracking techniques
Date : 2026-01-07 Size : 1.63mb User : jiaotianqi

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