Performance evaluation of local state estimation methods in bearings-only tracking problems

The paper deals with a performance analysis of several local filters within three bearing-only tracking scenarios. Performance of the extended Kalman filter, unscented Kalman filter, unscented Kalman filter with adaptive scaling parameter, which represent generic filters, and the shifted Rayleigh fi...

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Main Authors: Straka, O., Dunik, J., Simandl, M.
Format: Conference Proceeding
Language:English
Subjects:
Approximation methods
bearings-only tracking
Covariance matrix
Kalman filters
Measurement
Noise
nonlinear filtering
State estimation
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creator Straka, O.
Dunik, J.
Simandl, M.
description The paper deals with a performance analysis of several local filters within three bearing-only tracking scenarios. Performance of the extended Kalman filter, unscented Kalman filter, unscented Kalman filter with adaptive scaling parameter, which represent generic filters, and the shifted Rayleigh filter, which is designed solely for the bearing-only tracking problem, is compared using the root mean square error, averaged normalized estimation error squared and non-credibility index. The simulations show that the unscented Kalman filter with adaptive scaling parameter achieves similar or even better performance than the shifted Rayleigh filter.
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source IEEE Electronic Library (IEL) Conference Proceedings
subjects Approximation methods
bearings-only tracking
Covariance matrix
Kalman filters
Measurement
Noise
nonlinear filtering
State estimation
title Performance evaluation of local state estimation methods in bearings-only tracking problems
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