A state space approach to joint AOA and period estimation for a class of periodic discrete event processes

We examine the utility of a state space approach to pulse train signal modelling and address the problem of jointly estimating the angle of arrival (AOA) and periodicity of an intercepted radar pulse train signal. Kalman filter based techniques are developed and the mean square error performance of the estimators evaluated for two separate state space models. Robust methods are also suggested and implemented for dealing with the problem of false and missing pulses.