Terrain database integrity monitors and terrain referenced navigation systems are both based on performing a comparison between stored terrain elevation data and data obtained from airborne sensors such as radar altimeters, inertial measurement units (IMUs), Global Positioning System (GPS) receivers etc. The concept of consistency checking as used for the integrity monitor function originated from terrain referenced navigation systems. This paper discusses the extension to a previously proposed method of improving the perfonnance of a spatial integrity monitor for terrain elevation databases. Furthennore, this paper discusses an improvement of the terrain-referenced aircraft position estimation for aircraft navigation using only the infonnation from downward-looking sensors, GPS and the terrain databases, and not the infonnation from the IMU. Horizontal failures have been characterized based on the sensed terrain infonnation. Kalman fi lter methods have been designed to achieve the integrity monitor and terrain navigation performance improvements.

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One goal in the development of a Synthetic Vision System (SVS) is to create a system that can be certified by the Federal Aviation Administration (FAA) for use at various flight criticality levels. As part of NASA's Aviation Safety Program, Ohio University and NASA Langley have been involved in the research and development of real-time terrain database integrity monitors for SVS. Integrity monitors based on a consistency check with onboard sensors may be required if the inherent terrain database integrity is not sufficient for a particular operation. Sensors such as the radar altimeter and weather radar, which are available on most commercial aircraft, are currently being investigated for use in a real-time terrain database integrity monitor. This paper introduces the concept of using a Light Detection And Ranging (LiDAR) sensor as part of a real-time terrain database integrity monitor. A LiDAR system consists of a scanning laser ranger, an inertial measurement unit (IMU), and a Global Positioning System (GPS) receiver. Information from these three sensors can be combined to generate synthesized terrain models (profiles), which can then be compared to the stored SVS terrain model. This paper discusses an initial performance evaluation of the LiDAR-based terrain database integrity monitor using LiDAR data collected over Reno, Nevada. The paper will address the consistency checking mechanism and test statistic, sensitivity to position errors, and a comparison of the LiDAR-based integrity monitor to a radar altimeter-based integrity monitor.

In vision‐aided navigation, images from natural scenes are processed to produce feature measurements for navigation aiding. However, feature measurements are subject to non‐Gaussian errors and, in particular, to outliers in feature extraction and matching. If not properly accounted for, the errors are likely to lead to inconsistent, usually optimistic, estimation. To model feature matching errors, we study the circumstances in which outliers occur by checking the extracted features and detected outliers against the original images so as to verify spatial and temporal assumptions about the feature errors and their distributions. These error models are used in the probabilistic data association filter (PDAF) that updates an inertial navigation solution with feature measurements using the probability that an outlier is undetected and the corresponding feature measurement and the estimation error covariance are weighted down accordingly. Ground vehicle data show consistent estimation of this approach in the presence of real‐world image processing errors.

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