Journal ArticleDOI
Robust Preprocessing for Kalman Filtering of Glint Noise
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TLDR
In this paper, the non-Gaussian character of glint noise is demonstrated by exploratory data analysis and robust preprocessing strategies are proposed to minimize the effect of these glint spikes.Abstract:
The non-Gaussian character of glint noise is demonstrated by exploratory data analysis. This non-Gaussian behavior is characterized by outliers in the form of glint spikes. Since glint noise is processed by an angle-tracking Kalman filter, and since the latter is quite nonrobust, strategies are proposed to minimize the effect of these glint spikes. One of the strategies, which involves robust preprocessing of the data, is pursued in detail. Finally, some results of a planar missile simulation are presented that clearly demonstrate the merits of the robust preprocessing strategy.read more
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
Survey of maneuvering target tracking. Part V. Multiple-model methods
X. Rong Li,Vesselin P. Jilkov +1 more
TL;DR: A comprehensive survey of techniques for tracking maneuvering targets without addressing the so-called measurement-origin uncertainty is presented in this article, which is centered around three generations of algorithms: autonomous, cooperating, and variable structure.
Journal ArticleDOI
Discrete-Time Nonlinear Filtering Algorithms Using Gauss–Hermite Quadrature
TL;DR: The Gaussian sum-quadrature Kalman filter (GS-QKF) as mentioned in this paper approximates the predicted and posterior densities as a finite number of weighted sums of Gaussian densities.
References
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Journal ArticleDOI
Robust Estimation of a Location Parameter
TL;DR: In this article, a new approach toward a theory of robust estimation is presented, which treats in detail the asymptotic theory of estimating a location parameter for contaminated normal distributions, and exhibits estimators that are asyptotically most robust (in a sense to be specified) among all translation invariant estimators.
Journal ArticleDOI
The Influence Curve and Its Role in Robust Estimation
TL;DR: In this article, the first derivative of an estimator viewed as functional and the ways in which it can be used to study local robustness properties are discussed, and a theory of robust estimation "near" strict parametric models is briefly sketched and applied to some classical situations.
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Estimating Optimal Tracking Filter Performance for Manned Maneuvering Targets
TL;DR: In this paper, an optimal Kalman filter has been derived for this purpose using a target model that is simple to implement and that represents closely the motions of maneuvering targets, using this filter, parametric tracking accuracy data have been generated as a function of target maneuver characteristics, sensor observation noise, and data rate and that permits rapid a priori estimates of tracking performance to be made when the target is to be tracked by sensors providing any combination of range, bearing, and elevation measurements.
Journal ArticleDOI
A General Qualitative Definition of Robustness
TL;DR: In this paper, two very closely related definitions of robustness of a sequence of estimators are given which take into account the types of deviations from parametric models that occur in practice.