Filter methods realize a projection from superposed quantum state onto target state, which can be efficient if two states have sufficient overlap. Here we propose Gaussian filter (QGF) with the operator being function of system Hamiltonian. A hybrid quantum-classical algorithm feasible on near-term computers is developed, implements as linear combination Hamiltonian evolution at various times. ...

The Kalman filter is widely applied in target tracking problems. It is known to be the linear optimal filter in the white Gaussian noise environment. In some radar applications [1–9], the measurement noise may deviate from the Gaussian assumption. For instance, complex targets can cause irregular electromagnetic wave reflection. This phenomenon varies the target center in a radar and gives rise...

in this paper, we deal with the problem of adaptive coherent signal detection in gaussian interference (clutter plus noise) for surveillance pulse radars. some of the adaptive radar detectors exploit the ar model for clutter. most of these detectors have been obtained using the glr test. this test relies on the maximum likelihood estimation whose accuracy depends on the number of data. whereas,...

For dynamic systems, sequential Bayesian estimation requires updating of the filtering and predictive densities. For nonlinear and non-Gaussian models, sequential updating is not as straightforward as in the linear Gaussian model. In this paper, densities are approximated as finite mixture models as is done in the Gaussian sum filter. A novel method is presented, whereby sequential updating of ...

Several efficient component and vector filters applicable for processing multichannel and color images are compared quantitatively. The cases of Gaussian and mixed Gaussian and impulsive noise are considered. It is shown that in situations typical for practice the best results are provided by a vector doublewindow modified trimmed mean filter and by a modified sigma filter applied to each compo...