Recursive hybrid CRB for Markovian systems with time-variant measurement parameters

Abstract : In statistical signal processing, hybrid parameter estimation refers to the case where the parameters vector to estimate contains both deterministic and random parameters. Lately computationally tractable hybrid Cramér-Rao lower bounds for discrete-time Markovian dynamic systems depending on unknown time invariant deterministic parameters has been released. However in many applications (radar, sonar, telecoms, ...) the unknown deterministic parameters of the measurement model are time variant which prevents from using the aforementioned bounds. It is therefore the aim of this communication to tackle this issue by introducing new computationally tractable hybrid Cramér-Rao lower bounds.
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Communication dans un congrès
IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP 2015) , Dec 2015, Cancun, Mexico. 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), pp. 473-476, 2016
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  • HAL Id : hal-01447112, version 1
  • OATAO : 16660

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Jérôme Galy, Alexandre Renaux, Eric Chaumette, François Vincent, Pascal Larzabal. Recursive hybrid CRB for Markovian systems with time-variant measurement parameters. IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP 2015) , Dec 2015, Cancun, Mexico. 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), pp. 473-476, 2016. 〈hal-01447112〉

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