Terrain aided passive range estimation

Abstract

Stored digital terrain data are used as a parameter for a passive ranging exteded Kalman filter in a target range measurement system. The system accurately locates ground based targets using platform referenced passive sensors. The Kalman filter algorithm fuses angular target measurements (azimuth and elevation) from available sensors (FLIR, RFR, etc.) along with stored digital terrain data to obtain recursive least-square error estimates of target location. An iterative algorithm calculates the slant range to the intersection of the target's line of sight vector with the digital terrain data base. This calculated slant range is used as an input to the Kalman filter to complement the measured azimuth and elevation inputs. The Kalman filter uses the calculated range measurement to update the target location estimate as a function of terrain slope. The system arrives at a rapid solution by using the stored digital terrain data to provide estimates of range. The Kalman filter provides the framework for fusion, filtering of the measurement noise, and automatic triangulation when owncraft maneuvers improve observability. Results from a Monte Carlo simulation of the algorithm, using real terrain data, are presented. Measurement noise effects, and the more dominant terrain effects on the system estimation accuracy are analyzed.