Flight test development and evaluation of a Kalman filter state estimator for low-altitude flight

Flight operations dependent on digitized terrain elevation data for navigational reference or trajectory generation are constrained in minimum flight altitude, due to airborne navigation errors and inaccuracies of the reference terrain elevation data. This limitation is not restrictive in traditional medium-altitude implementations of such databases, such as in unmanned aerial vehicles, missiles, or high-performance, high-speed aircraft. In low-altitude, lower speed terrain hugging helicopter missions, however, such constraints on minimum flight altitudes greatly reduce the effectiveness of their missions and diminish the benefits of employing terrain elevation maps. A Kalman filter state estimator has been developed which blends airborne navigation, stored terrain elevation data, and a radar altimeter in estimating above-ground-level (AGL) altitude. This AGL state estimator was integrated in a near-terrain guidance system aboard a research helicopter and flight tested in moderately rugged terrain over a variety of flight and system conditions. The minimum operating altitude of the terrain database referenced guidance system was reduced from 300 ft to 150 ft with the addition of this Kalman filter state estimator