Morten Stakkeland

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In this paper, optimal best linear unbiased estimation (BLUE) filters are derived for cases where measurement errors depend on the state of the target. The standard Kalman filter fails to provide optimal estimates in these cases. Previously applied measurement models are reformulated in order to apply BLUE filters, and two new measurement models with state(More)
Range and angle measurement errors may be correlated when centroid image processing is applied on radar images of extended targets. This paper describes how a model of the correlation between target heading and measurement error can be used to improve the accuracy of tracking filters. The performance of the presented tracking algorithms is tested using a(More)
In this paper it is shown that the instantaneous false alarm rate in a constant false alarm Rate (CFAR) system fluctuates from scan to scan and that Bayesian and Empirical Bayesian estimators can be applied to decrease the error between the actual and the assumed false alarm rate. The instantaneous false alarm rate is a random variable and its probability(More)
In this paper, we describe a method to test and characterize accelerometers using an accurate position sensor and nonlinear Kalman filters. The method is designed to estimate parameters in nonlinear accelerometers and could be a simpler alternative to methods using centrifuges or vibrational testing. The method makes it possible to do real-time parameter(More)
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