Controlling target estimate covariance in centralized multisensor systems

Current multisensor fusion tracking systems can be easily overwhelmed by incoming data, especially as the number of targets and sensors increases. Sensor management schemes have been proposed to reduce the computational demand of these systems while minimizing the loss of tracking performance. This paper presents a system that will maintain a desired covariance level for each target while reducing the resource demands on the tracking system. Other functions performed by a sensor manager like prioritizing and scheduling are assumed to be done elsewhere, but result in delays in the execution of sensing requests made by the system. Three sensor selection algorithms are presented based on different resource and performance metrics and show a dramatic improvement over "dumb" sensing systems in simulation. Execution delay is shown to have a deleterious effect on the tracking performance of the system, but most of that performance can be restored when a prediction algorithm is used to model the delay.