Abstract
This paper presents the equations of the steady state Kalman Filter (ssKF) for both variable and constant sampling times, in order to state how important it is for the stability of this filter to have a constant sampling time. Under the condition of a constant sampling time (achieved here by using reconfigurable hardware), the steady-state Kalman Filter is then rewritten using a matrix property that will allow an efficient implementation in a parallel processor (although not in a sequential one), substantially improving the filter performance. This work also presents the solution to the particular cases for the propagation of the filter which can be found when implementing the algorithm, and demonstrates that the error introduced by using a fixed-point numerical implementation is stable with time.
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Pérez, C., Gracia, L., García, N., Sabater, J.M., Azorín, J.M., de Gea, J. (2009). On Accelerating the ss-Kalman Filter for High-Performance Computation. In: Corchado, J.M., Rodríguez, S., Llinas, J., Molina, J.M. (eds) International Symposium on Distributed Computing and Artificial Intelligence 2008 (DCAI 2008). Advances in Soft Computing, vol 50. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85863-8_17
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DOI: https://doi.org/10.1007/978-3-540-85863-8_17
Publisher Name: Springer, Berlin, Heidelberg
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