A modern flying-wing aircraft design featuring a highly flexible airframe is typically susceptible to dynamic coupling between rigid body and structural modes, and this poses challenges in aircraft design, modeling, and control. Active structural-mode control is required to ensure dynamic stability across desired flight envelope as well as closed-loop shape control which takes advantage of the airframe flexibility to optimize aerodynamic shape for minimal drag. One of the critical technologies required for structural mode control is accurate shape estimation of the structure in real-time, which can serve as a feedback signal for reference-command based shape control. In this paper, a Kalman-filterbased shape estimation approach for a small flying wing UAV is described. The UAV features a set of distributed sensors including accelerometers, gyros and strain gauges, as well as cameras that record and process visual information corresponding to wing-tip deflections. The shape estimation is achieved via a linear, steady-state Kalman filter, where the accelerometer/gyro data is suitably blended/updated with state propagation via a linear structural model. The concept is demonstrated and validated initially via ground test. A single-rate filter is constructed that uses accelerometer/gyro data to estimate the wing-shape, while data from cameras mounted on the aircraft are used to validate the estimation. The cameras specifically track wing-tip deformations, which are extracted via image-processing. The filter is constructed using a ground-vibration-test-updated linear structural model of the aircraft. It is planned to evolve this approach into a dual-rate filter that blends the higher rate accelerometer/gyro data and the lower rate camera data.