Kalman filter

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Kalman filter

[′kal·mən ‚fil·tər]
(control systems)
A linear system in which the mean squared error between the desired output and the actual output is minimized when the input is a random signal generated by white noise.
McGraw-Hill Dictionary of Scientific & Technical Terms, 6E, Copyright © 2003 by The McGraw-Hill Companies, Inc.
References in periodicals archive ?
Juzhong, "A new adaptive square-root unscented kalman filter for nonlinear systems," Applied Mechanics and Materials, vol.
[25] Niko Sunderhauf, Sven Lange, and Peter Protzel, "Using the Unscented Kalman Filter in MonoSLAM with Inverse Depth Parametrization for Autonomous Airship Control," In Proceedings of IEEE International Workshop on SSRR 2007, pp.1-6, 2007.
Among specific topics of the 168 papers are four statistical approaches for multi-sensor data fusion under non-Gaussian noise, information hiding based on structural similarity, an adaptive multi-objective immune optimization algorithm, empirical findings on social capital and cognitive bias in China, an extended symmetric sampling strategy for an unscented Kalman filter, and wireless networked control systems with neuron adaptive control and novel Smith predictor.
Multisensor Unscented Kalman Filter. Finally, a nonlinear estimator is used for attitude determination using all sensor data, due to the nonlinear nature of attitude to sensor observation transformation.
The unscented Kalman filter (UKF) [23, 24] uses a nonlinear transformation to deal with nonlinearities and outperforms the EKF in a wide range of applications [22, 25].
For the nonlinear filter design, there are three main methods, including Extended Kalman filter (EKF), Unscented Kalman filter (UKF), and Particle filter (PF) [3, 4].
Van Der Merwe, "The unscented Kalman filter for nonlinear estimation," in Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat.
Combined with the support vector machine model of the state space and the unscented Kalman filter of the dynamic state estimation, a new hybrid model is proposed [11].
Roumeliotis, "A quadratic-complexity observability-constrained unscented Kalman filter for SLAM," Robotics, IEEE Transactions on, vol.
Due to the tilt-rotor UAV highly nonlinear dynamic behavior, the unscented Kalman filter is chosen as state estimation strategy since it is not based on model linearization.
We propose using a new RFS based data assimilation algorithm by combining the basic ideas of Gaussian mixture approximation, Cardinalized Probability Hypothesis Density (CPHD) filter, and the unscented Kalman filter [40] together.