广义2-D Roesser模型的状态滤波器

State Filter of Singular 2-D Roesser Models

研究了状态和测量同时受白噪声干扰时广义随机2-D Roesser模型的状态滤波器设计问题。将Kalman滤波器的设计推广至广义2-D Roesser模型。通过逐行扫描法,模能重构的广义2-D Roesser模型的滤波问题得到了解决,获得了状态向量的最优滤波的计算公式。计算步骤和例子说明了设计的滤波器的有效性。

The problem of state estimator design for stochastic singular 2-D Roesser models subjected to white noises in both the state and measurement equations was studied. The well-known Kalman filter design was extended to singular 2-D Roesser models that minimizes the variance of the estimation error of state vectors. The filtering problem of jump-mode reconstructable singular 2-D Roesser models was solved through the method of ＂scanning row by row＂. Explicit formulas of the estimator was derived and based on which a numerical procedure was proposed to demonstrate the validity of the designed filter.