Target tracking using recursive pseudo linear estimator using highly aperiodic measurements

A sophisticated algorithm like the extended Kalman filter, is not much use when the exact variance of noise in input measurements is not known and when the interval between successive measurements is of the order of 15 to 30 seconds with high aperiodicity. In this situation the pseudo linear estimator (PLE) is a better solution. The PLE is converted into recursive processing to suit real-time applications. Several scenarios covering all quadrants are tried out and it is observed that the filter tracks the target closely. The results for a scenario are presented.