Electronic structure of the layered ferroelectric perovskite SrBi2Ta2O9

The band structure of the Bi layered perovskite SrBi2Ta2O9 (SBT) has been calculated by the tight binding method. We find both the valence and conduction band edges to consist of states primarily derived from the Bi-O layer rather than the perovskite Sr-Ta-O block. The valence band maximum arises from O p and some Bi s states, while the conduction band minimum consists of Bi p states, with a band gap of 5.1 eV. It is argued that the Bi-O layers largely control the electronic response of SBT while the ferroelectric response originates from the perovskite Sr-Ta-O block. Bi and Ta centered traps are calculated to be shallow, which may account in part for the excellent fatigue properties of SBT.

Electronic structure of the ferroelectric layered perovskite SrBi2Ta2O9

The band structure of the layered perovskite SrBi2Ta2O9 (SBT) was calculated by tight binding and the valence band density of states was measured by x-ray photoemission spectroscopy. We find both the valence and conduction band edges to consist of states primarily derived from the Bi-O layer rather than the perovskite Sr-Ta-O blocks. The valence band maximum arises from O p and some Bi s states, while the conduction band minimum consists of Bi p states, with a wide band gap of 5.1 eV. It is argued that the Bi-O layers largely control the electronic response whereas the ferroelectric response originates mainly from the perovskite Sr-Ta-O block. Bi and Ta centered traps are calculated to be shallow, which may account in part for its excellent fatigue properties. © 1996 American Institute of Physics.

Convergence of the auxiliary particle implementation of the PHD filter

Optimal Bayesian multi-target filtering is in general computationally impractical owing to the high dimensionality of the multi-target state. The Probability Hypothesis Density (PHD) filter propagates the first moment of the multi-target posterior distribution. While this reduces the dimensionality of the problem, the PHD filter still involves intractable integrals in many cases of interest. Several authors have proposed Sequential Monte Carlo (SMC) implementations of the PHD filter. However, these implementations are the equivalent of the Bootstrap Particle Filter, and the latter is well known to be inefficient. Drawing on ideas from the Auxiliary Particle Filter (APF), a SMC implementation of the PHD filter which employs auxiliary variables to enhance its efficiency was proposed by Whiteley et. al. Numerical examples were presented for two scenarios, including a challenging nonlinear observation model, to support the claim. This paper studies the theoretical properties of this auxiliary particle implementation. $\mathbb{L}_p$ error bounds are established from which almost sure convergence follows.

Auxiliary particle implementation of the probability hypothesis density filter

Optimal Bayesian multi-target filtering is, in general, computationally impractical owing to the high dimensionality of the multi-target state. The Probability Hypothesis Density (PHD) filter propagates the first moment of the multi-target posterior distribution. While this reduces the dimensionality of the problem, the PHD filter still involves intractable integrals in many cases of interest. Several authors have proposed Sequential Monte Carlo (SMC) implementations of the PHD filter. However, these implementations are the equivalent of the Bootstrap Particle Filter, and the latter is well known to be inefficient. Drawing on ideas from the Auxiliary Particle Filter (APF), we present a SMC implementation of the PHD filter which employs auxiliary variables to enhance its efficiency. Numerical examples are presented for two scenarios, including a challenging nonlinear observation model.