In an effort to increase situational awareness, the aviation industry is investigating technologies that allow pilots to visualize what is outside of the aircraft during periods of low-visibility. One of these technologies, referred to as Synthetic Vision Systems (SVS), provides the pilot with real-time computer-generated images of obstacles, terrain features, runways, and other aircraft regardless of weather conditions. To help ensure the integrity of such systems, methods of verifying the accuracy of synthetically-derived display elements using onboard remote sensing technologies are under investigation. One such method is based on a shadow detection and extraction (SHADE) algorithm that transforms computer-generated digital elevation data into a reference domain that enables direct comparison with radar measurements. This paper describes machine vision techniques for making this comparison and discusses preliminary results from application to actual flight data.
The Global Positioning System (GPS) consists of a constellation of Earth orbiting satellites that transmit continuous electromagnetic signals to users on or near the Earth surface. At any moment of time, at least four GPS satellites, and sometimes nine or more, are visible from any point. The electromagnetic signal transmitted from the satellites is reflected to at least some degree from virtually every place on the Earth. When this signal is received by a specially constructed receiver, its characteristics can be used to determine information about the reflected surface. One piece of information collected is the time delay encountered by the reflected signal versus the direct signal. This time delay can be used to determine the altitude (or height) above the local terrain when the terrain in the reflection area is level. However, given the potential of simultaneously using multiple reflections, it should be possible to also determine the elevation above even terrains where the reflecting area is not level. Currently an effort is underway to develop the technology to characterize the reflected signal that is received by the GPS Surface Reflection Experiment (GSRE) instrument. Recent aircraft sorties have been flown to collect data that can be used to refine the technology. This paper provides an update on the status of the instrument development to enable determination of terrain proximity using the GPS Reflected signal. Results found in the data collected to date are also discussed.
To enable safe use of Synthetic Vision Systems at low altitudes, real-time range-to-terrain measurements may be required to ensure the integrity of terrain models stored in the system. This paper reviews and extends previous work describing the application of x-band radar to terrain model integrity monitoring. A method of terrain feature extraction and a transformation of the features to a common reference domain are proposed. Expected error distributions for the extracted features are required to establish appropriate thresholds whereby a consistency-checking function can trigger an alert. A calibration-based approach is presented that can be used to obtain these distributions. To verify the approach, NASA's DC-8 airborne science platform was used to collect data from two mapping sensors. An Airborne Laser Terrain Mapping (ALTM) sensor was installed in the cargo bay of the DC-8. After processing, the ALTM produced a reference terrain model with a vertical accuracy of less than one meter. Also installed was a commercial-off-the-shelf x-band radar in the nose radome of the DC-8. Although primarily designed to measure precipitation, the radar also provides estimates of terrain reflectivity at low altitudes. Using the ALTM data as the reference, errors in features extracted from the radar are estimated. A method to estimate errors in features extracted from the terrain model is also presented.
Synthetic Vision Systems (SVS) provide pilots with displays of stored geo-spatial data representing terrain, obstacles, and cultural features. As comprehensive validation is impractical, these databases typically have no quantifiable level of integrity. Futher, updates to the databases may not be provided as changes occur. These issues limit the certification level and constrain the operational context of SVS for civil aviation. Previous work demonstrated the feasibility of using a real-time monitor to bound the integrity of Digital Elevation Models (DEMs) by using radar altimeter measurements during flight. This paper describes an extension of this concept to include X-band Weather Radar (WxR) measurements. This enables the monitor to detect additional classes of DEM errors and to reduce the exposure time associated with integrity threats. Feature extraction techniques are used along with a statistical assessment of similarity measures between the sensed and stored features that are detected. Recent flight-testing in the area around Juneau, Alaska Airport (JNU) has resulted in a comprehensive set of sensor data that is being used to assess the feasibility of the proposed monitor technology. Initial results of this assessment are presented.
This paper discusses flight test results of a Digital Elevation Model (DEM) integrity monitor. The DEM Integrity Monitor Experiment (DIME) was part of the NASA Synthetic Vision System (SVS) flight trials at Eagle-Vail, Colorado (EGE) in August/September, 2001. SVS provides pilots with either a Heads-down Display (HDD) or a Heads-up Display (HUD) containing aircraft state, guidance and navigation information, and a virtual depiction of the terrain as viewed 'from the cockpit'. SVS has the potential to improve flight safety by increasing the situational awareness (SA) in low to near zero-visibility conditions to a level of awareness similar to daytime clear-weather flying. This SA improvement not only enables low-visibility operations, but may also reduce the likelihood of Controlled Flight Into Terrain (CFIT). Because of the compelling nature of SVS displays high integrity requirements may be imposed on the various databases used to generate the imagery on the displays even when the target SVS application does not require an essential or flight-critical integrity level. DIME utilized external sensors (WAAS and radar altimeter) to independently generate a 'synthesized' terrain profile. A statistical assessment of the consistency between the synthesized profile and the profile as stored in the DEM provided a fault-detection capability. The paper will discuss the basic DIME principles and will show the DIME performance for a variety of approaches to Runways 7 and 25 at EGE. The monitored DEMs are DTED Level 0, USGS with a 3-arcsec spatial resolution, and a DEM provided by NASA Langley. The test aircraft was a Boeing 757-200.
This paper discusses the flight test results of a real-time Digital Elevation Model (DEM) integrity monitor for Civil Aviation applications. Providing pilots with Synthetic Vision displays containing terrain information has the potential to improve flight safety by improving situational awareness and thereby reducing the likelihood of Controlled Flight Into Terrain. Utilization of DEMs, such as the digital terrain elevation data, requires a DEM integrity check and timely integrity alerts to the pilots when used for flight-critical terrain-displays, otherwise the DEM may provide hazardous misleading terrain information. The discussed integrity monitor checks the consistency between a terrain elevation profile synthesized from sensor information, and the profile given in the DEM. The synthesized profile is derived from DGPS and radar altimeter measurements. DEMs of various spatial resolutions are used to illustrate the dependency of the integrity monitor's performance on the DEMs spatial resolution. The paper will give a description of proposed integrity algorithms, the flight test setup, and the results of a flight test performed at the Ohio University airport and in the vicinity of Asheville, NC.