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A computational model for visual flow-field cueing and self-motion estimation is developed and simulated. The model is predicated on the notion that the pilot makes noisy, sampled measurements on the spatially distributed visual flow-field surrounding him and, on the basis of these measurements, generates estimates of his own linear and angular terrain-relative velocities which optimally satisfy, in a least-squares sense, the visual kinematic flow constraints. The least-squares formulation is applicable to general observer motions and viewing geometries; it is projection-plane independent and rational in its treatment of redundant and noisy flow cues. A subsidiary but significant output of the model is an `impact time' map, and observer-centered spatially scaled replica of the viewed surface. Simulations are presented to demonstrate the parametric sensitivity and ability to model relevant human visual performance data. Preliminary simulation results of human visual performance are encouraging.