of Aircraft Position
J.H. Clements · 1996
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 and bearing measurements, updated estimates are obtained. This technique is particularly useful in situations where distance measuring equipment (DME) coverage or geometry is poor and VHF OWI1\JI range (VOR) signals are available. with a priori ML estimates of the aircraft's position and velocity. It then produces updated estimates of the aircraft's position and velocity. In the derivation of the formulas that produce the estimates, it is assumed that the aircraft's forward speed, heading and pitch can be modeled as independent Markov processes. Finally, we show that the estimates obtained using this algorithm coincide with those obtained using the extended Kalman filter.