AOA geolocation for fast-movers using nonlinear optimization

When observing an object of interest (herein simply called “object”), determining the geo-location of the object as accurately as possible, together with an accurate estimate of the geo-location uncertainty, is a key step in many remote sensing applications. To compute the geo-location of an object, Angle of Arrival (AoA) measurements can be used from multiple different sensor platforms such as aerostats, aircraft, and satellites. In a previous paper, nonlinear optimization (NLO) was used to provide an optimal geolocation estimate which converges to the minimum mean squared error (MMSE) for the problem of a stationary object and simultaneous measurements. This paper describes a generalization of the same (NLO method applied to fast-moving objects and nonsimultaneous measurements. For some sensor platforms, the object may be so far from the sensor that it moves significantly by the time the sensor measures the AoA. This is especially true for satellites in GEO or HEO. The time-delay from the Earth to such satellites may be as much as 200 ms, meaning the sensors produce measurements of where the object was rather than where it is at the time of measurement. In addition, sensors rarely produce measurements at the same time. This temporal incongruence of measurements can significantly complicate the geo-location problem for a rapidly moving object. Thus a scheme which can produce an optimal geolocation estimate and a confidence estimate for this scenario is desirable. Nonlinear optimization is proposed and examined as a means of satisfying this objective.