Optimal fusion of sensor data for discrete Kalman Dynamic Systems and Applications

ABSTRACT. In this paper we consider the question of optimal fusion of sensor data in discrete time. The basic problem is to design a linear lter whose output provides an unbiased minimum variance estimate of a signal process whose noisy measurements from multiple sensors are available for input to the lter. The problem is to assign weights to each of the sources (sensor data) dynamically so as to minimize estimation errors. We formulate the problem as an optimal control problem where the weight given to each of the sensor data is considered as one of the control variables satisfying certain constraints. There are as many controls as there are sensors. We develop an ecient method for determining the optimal fusion strategy and gives a numerical result for illustration