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Multi-sensor management is concerned with utilising the available sensor resource in the most effective way possible to detect, classify or track targets. We are primarily concerned with utilising the sensor resource in order to track a target as closely as possible. Previous work in this area has focused on tracking targets whose motion is either governed by a pre-specified model, or manoeuvre at pre-specified times. In particular, targets do not adapt their behaviour in order to make tracking them more difficult. In this paper, we apply state of the art sensor management techniques to a scenario in which the target is actively trying to avoid being tracked. This creates a conflict between the aims of sensor network and the target, which these previous techniques are unable to resolve. We formulate the action (e.g. manoeuvre) of the sensor resource (the pursuer) and the target (the evader) as a two-player game. The "reward" each player receives is then dependent on the actions chosen and the ensuing tracking accuracy. We also allow multi-step planning, in which the action of each player takes into account the impact this will have on future expected rewards (i.e. future tracking performance). We show that, form the pursuer's perspective, tracking performance is significantly improved by multi-step planning. Conversely, the evader can substantially degrade tracking performance by following the strategies we recommend, when compared to either performing random manoeuvres or moving with constant velocity.
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