Target Motion Analysis Algorithms for Rapid Localization Methods

摘要

Several least-squares linear regression Target Motion Analysis (TMA) algorithms were derived for use when target range as well as bearing measurements are available from Rapid Localization underwater passive acoustic sensor systems, including the Wide Aperture Array for attack submarines. Simulation error analyses, for a number of typical target encounter geometries, were conducted for each of the range-bearing TMA algorithms and for the bearings-only TMA algorithm, which must be used whenever range data is unavailable. The results showed that one particular algorithm, (K sub OPT (R - B) + (B - O)), was significantly more accurate than any of the others against non-maneuvering targets and another algorithm, (2, 2 x 2; R - B), performed best against maneuvering targets. Both of these TMA algorithms require range as well as bearing measurement inputs. The optimum weighting factor, K sub OPT, and weighting coefficient, a, which minimize the TMA errors were also derived. Least-squares TMA algorithms were derived for the special case that the target's down-range velocity component is either known or assumed to be known. One of these is the least-squares linear regression algorithm for the Ekelund ranging method.