Abstract: This paper present an information-theoretic approach to sensor management for multitarget tracking using a sensor that operates in one of two modes: a fast, low-resolution mode and a slow, high-resolution mode. The error correlations between nearby target pairs, the sensor rates, the sensor resolutions and the target plant noise all play a role in the optimum choice of mode. The error correlations occur in the target location estimates even when the individual measurement errors are uncorrelated, as in the model considered here. When a filter that models these error correlations is used, such as event-averaged maximum likelihood estimation, a sensor management strategy can be developed to reduce them. This is illustrated with a model two- target problem. In the model problem, the target plant noise is such that the low resolution mode produces the optimum result when the targets are widely separated, due to its higher report rate. If the error correlations are not modeled, then over a certain parameter range the low resolution mode would be selected for all target separations. When the effect of error correlations is included, it is shown the slow, high resolution mode produces a better result when the targets are close together. This suggests that systems that must track closely spaced targets could benefit from adaptively adjusting their integration times based on target plant noise and separation. !12
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