Terrain aided passive estimation

Abstract

An approach using stored digital terrain data as a measurable parameter for a passive ranging extended Kalman filter provides an improved solution to the problem of locating ground-based targets accurately from aircraft-referenced passive sensors. The algorithm fuses angular target measurements (azimuth and elevation) from all available sensors, along with stored digital terrain data, to obtain recursive least-square error estimates of target location. An iterative algorithm determines slant range to the intersection of the target's line of sight vector with the digital terrain database. The Kalman filter uses the derived slant range measurement to update the target location estimate as a function of terrain slope, arriving at a much quicker solution. It is the nucleus of the design providing the framework for fusion and the filtering of the measurement noise and providing triangulation automatically when owncraft maneuvers improve observability. Results from a Monte Carlo simulation of the algorithm, using real terrain data, are presented. >