We define information gathering as the task of both deciding what sensor data to gather and process, and fusing those observations into a common representation. In most such systems there is a tradeoff between the complexity of a computational technique and its efficiency at gathering and using information. In some cases, it turns out that computationally simple technique, though it may make poor use of information, outperforms a, more accurate but, more complex technique simply because of its ability to process more observations in less time. Naturally, the correct tradeoff between speed and complexity varies from situation to situation. In this paper, we discuss and compare the grid-based techniques developed in our previous work, classic minimum mean-square estimation techniques (MMSE), and a, modification of MMSE which is robust, to nonlinear systems and system description errors with respect to their efficiency at using sensor information. We then discuss the ability of these techniques to improve their performance by choosing favorable sensor observations, and what effect this has on their overall complexity/efficiency tradeoff.