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Autonomous vehicle control requires knowledge of the vehicle's states that often can only be estimated using sensor measurements. Several sensor types are typically used for the estimation process and each type often has its own sensing characteristics. This paper considers a novel morphing unmanned aerial vehicle (UAV) that is capable of changing its configuration in-flight and using aerodynamic forces to perform a perching maneuver. This maneuver could allow the UAV to perform planted landings and enable the vehicle to land in new locations, such as on building rooftops. However, this task requires the system controller to have accurate knowledge of vehicle states, especially with respect to the landing location. Visual sensors are required for identification of the landing site and to provide the relative positioning information that is critical for autonomous landings when uncertainty exists in the landing coordinates. Such information is unavailable from either a global navigation satellite system (GNSS) or inertial measurements to sufficient accuracy. The key objective of this research is to develop a foundation for the control of an aircraft that is highly nonlinear. This paper investigates the use of a set of linear motion models to represent the full range of nonlinear dynamics for an aircraft performing a perching maneuver. Simulation data are presented and their results discussed.