What is Kalman Filter

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Algorithm developed to estimate the future stateof a dynamic systemin an optimal way, depending on the delay of the samples admitted minimizing the noise. Learn more in: Maximum Likelihood-Based Fuzzy Adaptive Kalman Filter Applied to State Estimation of Permanent Magnet Synchronous Motors

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The Kalman filter, also known as Linear Quadratic Estimation (LQE), is an algorithm for estimating the state variables of stochastic systems that are described by state-space mathematical models, based on experimental measurements. Learn more in: State and Parametric Estimation of Nonlinear Systems Described by Wiener Sate-Space Mathematical Models

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An algorithm that uses a series of measurements observed over time, containing statistical noise and other inaccuracies, and produces estimates of unknown variables that tend to be more accurate than those based on a single measurement alone, by using Bayesian inference and estimating a joint probability distribution over the variables for each timeframe. The filter is named after Rudolf E. Kálmán, one of the primary developers of its theory. Learn more in: The Educational and Academic Innovation of the Avionics Engineering Center

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An algorithm used to reduce the amount of noise in a stream of data. Learn more in: Underwater Swarm Robotics: Challenges and Opportunities

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Is dynamic adaptive filter the amplifier factor of which calculated from the Riccati differential equation using statistical information of signal and noise. Learn more in: Non-Invasive Active Acousto-Thermometer

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It is a mathematical tool proposed by Rudolph E. Kalman in 1960 for the problem of linear filtering and optimal recursive estimation from noisy measurements. Your set of equations are divided into two distinct steps: propagation and assimilation or updating. Learn more in: Methodology for Model-Based Fuzzy Kalman Filter Design via Singular Spectral Analysis of Experimental Data

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An algorithm that uses a series of measurements observed over time, containing random variations and other inaccuracies, and produces estimates of unknown variables that tend to be more precise than those based on a single measurement alone. Learn more in: A Survey of Using Microsoft Kinect in Healthcare

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A recursive predictive filter used for estimating Kalman filter is used to estimate the state of a linear system where the state is assumed to be distributed by a Gaussian. Learn more in: Moving Object Detection and Tracking Based on the Contour Extraction and Centroid Representation

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An algorithm that uses a series of measurements over time to estimate the unknown states of a system. Learn more in: EFWA as a Method of Optimizing Model Parameters: Example of an Expensive Function Evaluation