If you program using values that represent anything in the real world, you have probably at least heard of the Kalman filter. The filter allows you to take multiple value estimates and process them ...
If we are hiring someone such as a carpenter or an auto mechanic, we always look for two things: what kind of tools they have and what they do when things go wrong. For many types of embedded systems, ...
This example estimates the normal SSM of the mink-muskrat data using the EM algorithm. The mink-muskrat series are detrended. Refer to Harvey (1989) for details of this data set. Since this EM ...
Kalman filter models based on the assumption of multivariate Gaussian distributions are known to be nonrobust. This means that when a large discrepancy arises between the prior distribution and the ...
The well-known SOTA model for non-linear state estimation is the Extended Kalman Filter [1–3]. However, if the knowledge of the noise covariances is not available or there are model uncertainties, the ...
In this example, the log likelihood function of the SSM is computed using prediction error decomposition. The annual real GNP series, y t, can be decomposed as where ...
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Abstract: This paper investigates swarm navigation performance for unmanned aerial vehicles (UAVs) under varying flight path geometries and swarm sizes. Employing both local and master Kalman filter ...
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