Recursive maximum likelihood estimation of aircraft position using multiple range and bearing measurements

In this paper an algorithm is presented that recursively computes the maximum likelihood (ML) estimates of an aircraft´s position in space. By combining an a priori ML estimate of the aircraft´s state vector and its error covariance matrix with multiple range measurements, updated estimates are obtained. This technique is particularly useful in situations where distance measuring equipment coverage or geometry is poor and VHF OMNI range (VOR) signals are available