As the profusion of different sensors improves the capabilities of tracking platforms, tracking objectives can move from simply trying to achieve the most with a limited sensor suite to developing the ability to achieve more specific tracking goals, such as reducing the uncertainty in a target estimate enough to accurately fire a weapon at a target or to ensure that a mobile robot does not collide with an obstacle. Multisensor manager systems that balance tracking performance with system resources have traditionally been ill-suited for achieving such specific control objectives. This work extends the methods developed in single-sensor management schemes to a multisensor application using an approach known as covariance control, which selects sensor combinations based on the difference between the desired covariance matrix and that of the predicted covariance of each target.

This paper provides an introduction to sensor fusion techniques for target tracking. It presents an overview of common filtering techniques that are effective for moving targets as well as methods of overcoming problems specific to target tracking, such as measurement-to-track association and sensor registration. The computational demand of such algorithms is discussed and various practices, including distributed processing of target tracks and sensor management, are proposed to help reduce this demand. Final comments include a discussion of applications and implementation issues specific to the presented scenarios.

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