The paper concerns the sub-optimal filtering problem when the measurement signal travels through an unreliable network and the noise signals are not necessarily Gaussian. In particular, the measurement packet losses are modeled by an i.i.d. Bernoulli sequence with known probability mass function, and we assume that the moments of the (generally) non- Gaussian noise sequences up to the fourth order are known. By mean of a suitable rewriting of the system through an output injection term, and by considering an augmented system with the second-order Kronecker power of the measurements, an optimal solution among the quadratic transformations of the output is provided. Numerical simulations show the effectiveness of the proposed method.
Kalman-like filtering with intermittent observations and non-Gaussian noise
CACACE F;
2019-01-01
Abstract
The paper concerns the sub-optimal filtering problem when the measurement signal travels through an unreliable network and the noise signals are not necessarily Gaussian. In particular, the measurement packet losses are modeled by an i.i.d. Bernoulli sequence with known probability mass function, and we assume that the moments of the (generally) non- Gaussian noise sequences up to the fourth order are known. By mean of a suitable rewriting of the system through an output injection term, and by considering an augmented system with the second-order Kronecker power of the measurements, an optimal solution among the quadratic transformations of the output is provided. Numerical simulations show the effectiveness of the proposed method.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.