Recursive computation of a posteriori density functions for arbitrary i.i.d. state noise and its application to impulsive interference mitigation

Studies the a posteriori probability density function of the state of a discrete-time system. By applying the Bayesian law to the state and measurement equations of the stochastic system, the a posteriori density is obtained in closed-form and computed recursively for arbitrary i.i.d. state noise and binary measurement noise (or signal). As an example, the highly impulsive state process driven by the noise with /spl alpha/-stable distribution is estimated and significantly suppressed from the measurement.<
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