MAXIMUM-LIKELIHOOD COORDINATE REGISTRATION FOR OVER-THE-HORIZON RADAR

Over-the-horizon radar exploits the refractive and multipath nature of high-frequency propagation through the ionosphere to achieve wide-are a surveillance, The coordinate registration process converts the group delays and azimuths (i,e., slant coordinates) from a set of multipath target returns to an estimate of its location (i,e,, ground coordinat es). This is performed by associating the target returns with raymodes determined using a computational electromagnetic propagation model. N ot surprisingly, errors in the estimates of down-range ionosphere para meters can seriously degrade the accuracy of the target location estim ate. The coordinate registration method presented here is designed to achieve improved accuracy by employing a statistical model for uncerta inties in the ionosphere. Modeling down-range ionospheric parameters a s random variables with known statistics facilitates maximum likelihoo d (ML) target location estimation, which is more robust to errors in t he measured ionospheric conditions. The statistics of down-range ionos pheric parameters can be determined using current and historical sound ings of the ionosphere, ML target localization consists of determining the most likely target ground coordinates over an ensemble of ionosph eric conditions consistent with the data, For greater computational ef ficiency, the likelihood function is approximated by a hidden Markov m odel (HMM) for the probability of a sequence of observed slant coordin ates given a hypothesized target location. The parameters of the HMM a re determined via Monte Carlo execution of a raytracing propagation mo del for random realizations of the ionosphere. A simulation study perf ormed using a random ionospheric model derived from ionogram measureme nts made at Wallops Island suggests that the ML method can potentially achieve average absolute miss distances as much as five times better than a conventional coordinate registration technique.