Angle only target tracking using a continuous-valued Bayesian network

In this paper, we apply a continuous-valued Bayesian network to the problem of tracking a maneuvering target using only bearing data from a single observer. The resulting tracking algorithm computes an approximate posterior probability density of the target position and velocity given the observations. This algorithm is more robust than typical approaches based on the extended Kalman filter and provides a framework in which side information, such as bounds on target velocity, can be incorporated directly into the estimate. The algorithm's performance is characterized using Monte Carlo simulation.