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Journal ArticleDOI

Alpha-Beta Filter with Correlated Measurement Noise

Steven R. Rogers
- 01 Jul 1987 - 
- Vol. 23, Iss: 4, pp 592-594
TLDR
In this paper, a first-order Markov model for the correlation of the measurement errors on successive observations is used to estimate the effect of correlation on the time interval between measurements by a factor (1+a)/(1-a).
Abstract
For many tracking applications, the measurement errors onsuccessive observations are correlated. Using a first-order Markov model for the correlation, we present analytical expressions for the time-varying covariance and gains of an alpha-beta tracking filter.To a good approximation, the effect of correlation is to increase the time interval between measurements by a factor (1+a)/(1-a),where a is the coefficient of correlation between successive measurements.

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Citations
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Journal ArticleDOI

Interacting multiple model methods in target tracking: a survey

TL;DR: The objective of this work is to survey and put in perspective the existing IMM methods for target tracking problems, with special attention to the assumptions underlying each algorithm and its applicability to various situations.
Journal ArticleDOI

Recursive filtering with random parameter matrices, multiple fading measurements and correlated noises

TL;DR: The purpose of the addressed filtering problem is to design an unbiased and recursive filter for the random parameter matrices, stochastic nonlinearity, and multiple fading measurements as well as correlated noises.
Journal ArticleDOI

The Role of Pseudo Measurements in Equality-Constrained State Estimation

TL;DR: It is found that under certain conditions, the use of the pseudo measurement for filtering is redundant, which motivates the discussion of processing order for this type of estimation problem, especially in the extension to the nonlinear case.
Journal ArticleDOI

Maneuvering target tracking with colored noise

TL;DR: This work incorporates oar method into the interacting multiple model (IMM) tracking algorithm and shows that the performance is almost as good as that in the known parameters case.
References
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Journal ArticleDOI

Low-angle radar tracking

TL;DR: In this article, a detailed model of diffuse scattering is constructed which agrees with available test data and which permits the power distributions in radar coordinates to be estimated, using this model, the potential performance of various techniques proposed for avoidance of multi-path is evaluated, several are found effective in maintaining tracks of reasonable accuracy down to one-fourth beamwidth above the horizon, but no generally practical solution to height measurement below that angle appears available.
Journal ArticleDOI

Decoupled Kalman filters for phased array radar tracking

TL;DR: In this article, the authors consider the design of Kalman filters to reduce computational requirements, ill-conditioning, and the effects of nonlinearities and discuss methods to mitigate their ill effects.
Journal ArticleDOI

Multipath Limitations on Low-Angle Radar Tracking

TL;DR: Quantitative estimates are derived of the elevation angles, and hence, range, at which targets of specified height can be accurately tracked, and to indicate specific areas in which additional experimental data are critically needed.
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