Fusion of ground-based sensors for optimal tracking of military targets

Abstract

Extended Kalman filtering is applied as an extension of the Position Location Reporting System (PLRS) to track a moving target in the XY plane. The application uses four sets of observables which correspond to inputs from a fused-sensor array where the sensors employed are acoustic, seismic, or radar. The nonlinearities to the Kalman filter occur through the measured observables which are: bearings to the target only, ranges to the target only, bearings and ranges to the target, and a Doppler-shifted frequency accompanied by the bearing to that frequency. The observables are nonlinear in their relationships to the Cartesian coordinate states of the filter. Filter error covariances are portrayed as error ellipsoids about the laser target estimate made by the filter. Rotation of the ellipsoids is accomplished to avoid the cross correlation of the coordinates. The ellipsoids employed are one standard of deviation in the rotated coordinate system and correspond to a constant of probability of target location about the latest Kalman target estimate. Filtering techniques are evaluated for both stationary and moving observers with arbitrarily moving targets. The objective of creating a user-friendly, personal computer based tracking algorithm is also discussed.