Advanced optimization-based algorithms for sensor resource management have been the research focus area in multisensor tracking and fusion in the last decade. These algorithms for the most part offer the potential for automating the sensor control process in response to level 1 sensor data fusion (object or track-level) estimates. However, previous studies have indicated that these types of sensor resource management algorithms may have limited value in certain operational scenarios involving multi-platform surveillance and strike missions because the response is optimized for track maintenance without any assessment of overall situation context. In this paper, we will develop a framework for representing the expected information value of planned sensor measurements as it contributes to higher-level situation inferences. Specifically, a hierarchical target valuation model that estimates target value on the basis of not only a level 1 valuation function but also on the basis of a level 2 valuation function will be presented. These algorithms will provide for improved tracking and classification performance when identifying higher-level units such as convoys of vehicles. The valuation models rely on a computationally efficient implementation of Bayesian modeling and inference algorithms. Note that the main focus of the paper is on developing a hierarchical cost function that captures both level 1 and level 2 objectives and is not on developing sophisticated techniques for optimizing this objective. Simulation results which validate the approach are also presented.