Bearings-Only Tracking in the Plane

Abstract

It is known that the conditional probability density function for the general filtering problem can be represented as a path integral. This representation is the basis of a new finite dimensional recursive filter which is applied to bearings only tracking. The filter computes an approximation to the density for the target position conditional on the measurements. The approximation is accurate provided only the most recent measurements are used. Two different models for the target motion are considered. In the first the model is a known motion subject to a perturbation by an Ornstein-Uhlenbeck process. In the second the acceleration of the target is a Brownian motion. Numerical experiments show that for the first model the bearing angle measurements add little or no range information, but for the second model they do add significant range information, especially if the noise level in the model is high.

Passive Tracking and Information Theory

“…The tracking community has long recognized the lack of observability inherent in angleonly track, and various techniques have been developed to compensate, e.g., Kalman Filter coordinate systems which decouple the range (unobservable) dynamics from the azimuth and elevation axes [6]. Recent efforts have focused on developing recursive finite dimensional filters as an approximation to the conditional target position probability density [7].…”