Abstract We present an algorithm that uses a sparse collection of noisy sensors to characterize the observed behavior of a mobile agent. Our approach models the agent's behavior using a collection of randomized simulators called implicit agent models and seeks to classify the agent according to which of these models is governing its motions. To accomplish this, we introduce an algorithm whose input is an observation sequence generated by the agent, represented as sensor label-time pairs, along with an observation sequence generated by one of our implicit agent models and whose output is a measure of the similarity between the two observation sequences. Using this similarity measure, we propose two algorithms for the model classification problem: one based on a weighted voting scheme and one that uses intermediate resampling steps. We have implemented these algorithms in simulation, and present results demonstrating their effectiveness in correctly classifying mobile agents.