Book ChapterDOI
The Kalman Filter
Sharon Gannot,Arie Yeredor +1 more
- pp 135-160
TLDR
This chapter introduces the Kalman filter, providing a succinct, yet rigorous derivation thereof, which is based on the orthogonality principle, and introduces several important variants of the Kal man filter, namely various Kalman smoothers, a Kalman predictor, a nonlinear extension, and adaptation to cases of temporally correlated measurement noise.Abstract:
The Kalman filter and its variants are some of the most popular tools in statistical signal processing and estimation theory. In this chapter, we introduce the Kalman filter, providing a succinct, yet rigorous derivation thereof, which is based on the orthogonality principle. We also introduce several important variants of the Kalman filter, namely various Kalman smoothers, a Kalman predictor, a nonlinear extension (the extended Kalman filter), and adaptation to cases of temporally correlated measurement noise.read more
Citations
More filters
Journal ArticleDOI
Reinforcement learning to adjust parametrized motor primitives to new situations
TL;DR: This paper proposes a method that learns to generalize parametrized motor plans by adapting a small set of global parameters, called meta-parameters, and introduces an appropriate reinforcement learning algorithm based on a kernelized version of the reward-weighted regression.
Journal ArticleDOI
Real-Time RFID Indoor Positioning System Based on Kalman-Filter Drift Removal and Heron-Bilateration Location Estimation
TL;DR: Experimental results reveal that the proposed Kalman-filter DR method is faster and better to converge the distance measurement (DM) error than conventional probability/statistics in terms of various relative distances under certain RSSI drift effect condition.
Book
Learning Motor Skills: From Algorithms to Robot Experiments
Jens Kober,Jan Peters +1 more
TL;DR: This book illustrates a method that learns to generalize parameterized motor plans which is obtained by imitation or reinforcement learning, by adapting a small set of global parameters and appropriate kernel-based reinforcement learning algorithms.
Book ChapterDOI
Change detection with kalman filter and CUSUM
Milton Severo,João Gama +1 more
TL;DR: The experimental results showed that the DSKC system detected changes fast and with high probability and can be applied with efficiency to problems where the information is available over time.
Journal ArticleDOI
DISTY: Dynamic Stochastic Time Synchronization for Wireless Sensor Networks
TL;DR: A dynamic stochastic model inserted into a Kalman filter formulation is applied to track the clock evolution of oscillators and achieve synchrony to a central time reference and shows the synchronization accuracy under stable and varying temperature conditions.
References
More filters
Journal ArticleDOI
Maximum likelihood from incomplete data via the EM algorithm
Journal ArticleDOI
Unscented filtering and nonlinear estimation
Simon Julier,Jeffrey Uhlmann +1 more
TL;DR: The motivation, development, use, and implications of the UT are reviewed, which show it to be more accurate, easier to implement, and uses the same order of calculations as linearization.
Journal ArticleDOI
Suppression of acoustic noise in speech using spectral subtraction
TL;DR: A stand-alone noise suppression algorithm that resynthesizes a speech waveform and can be used as a pre-processor to narrow-band voice communications systems, speech recognition systems, or speaker authentication systems.
Journal ArticleDOI
The generalized correlation method for estimation of time delay
TL;DR: In this paper, a maximum likelihood estimator is developed for determining time delay between signals received at two spatially separated sensors in the presence of uncorrelated noise, where the role of the prefilters is to accentuate the signal passed to the correlator at frequencies for which the signal-to-noise (S/N) ratio is highest and suppress the noise power.