Topic
Alpha beta filter
About: Alpha beta filter is a research topic. Over the lifetime, 5653 publications have been published within this topic receiving 128415 citations.
Papers published on a yearly basis
Papers
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TL;DR: Two observer design techniques for state estimation of high-dimensional chemical processes are presented, one of which is used for systems with inputs, whereas the other one is specifically geared towards systems that are not excited from the outside.
29 citations
TL;DR: In this article, a new particle filter called Mean Shifted Particle Filter (MSPFb) was proposed, which is based on the Central Dierence Kalman Filter (CDKF).
Abstract: This paper shows how non-linear DSGE models with potential non-normal shocks can be estimated by Quasi-Maximum Likelihood based on the Central Dierence Kalman Filter (CDKF). The advantage of this estimator is that evaluating the quasi log-likelihood function only takes a fraction of a second. The second contribution of this paper is to derive a new particle …lter which we term the Mean Shifted Particle Filter (MSPFb). We show that the MSPFb outperforms the standard Particle Filter by delivering more precise state estimates, and in general the MSPFb has lower Monte Carlo variation in the reported log-likelihood function.
29 citations
TL;DR: In this paper, an adaptive alpha/beta filter representative of systems now in UAVC systems is presented, and three algorithms for central tracking in air traffic control systems are examined.
Abstract: This article presents an analysis of algorithms for central tracking in air traffic control systems. Three algorithms were examined: an adaptive alpha/beta filter representative of systems now in u...
29 citations
TL;DR: Two motion estimation algorithms using Kalman filter to further enhance the performance of the conventional R-D motion estimation at a relative low computational cost and improves the rate-distortion performance significantly.
Abstract: The rate-constrained (R-D) motion estimation techniques have been presented to improve the conventional block-matching algorithm by using a joint rate and distortion criterion. This paper presents two motion estimation algorithms using Kalman filter to further enhance the performance of the conventional R-D motion estimation at a relative low computational cost. The Kalman filter exploits the correlation of block motion to achieve higher precision of motion estimation and compensation. In the first algorithm, the Kalman filter is utilized as a postprocessing to raise the motion compensation accuracy of the conventional R-D motion estimation. In the second algorithm, the Kalman filter is embedded into the optimization process of R-D motion estimation by defining a new R-D criterion. It further improves the rate-distortion performance significantly.
29 citations
TL;DR: In this article, the authors investigated the means to design the observer for a class of nonlinear systems with Lipschitz conditions and unknown parameters and proposed a new design approach of full-order state adaptive observer.
29 citations