Lack of high-resolution observations at the inner-core region of tropical cyclones introduces uncertainty into the structure’s true initial state. More accurate measurements at the inner-core are essential for accurate tropical cyclone forecasts. This study seeks to improve the estimates of the inner-core structure by utilizing background information from prior assimilated conventional observations. We provide a scheme for targeted high-resolution observations for platforms such as the Coyote sUAS. In an effort to identify potential locations of high uncertainty, an exploratory investigation of the background information of the state variables pressure, temperature, wind speed, and a combined representation of the state variables given by their linear weighted average is presented. A sampling-based path planning algorithm that considers the Coyote’s energy usage then locates the regions of high uncertainties along a Coyote’s flight, allowing us to maximize the removal of uncertainties. The results of a data assimilation analysis of a typical Coyote flight mission using the proposed deployment scheme shows significant improvements in estimates of the tropical cyclone structure after the resolution of uncertainties at targeted locations.