3D Terrain Mapping and Filtering From Coarse-Resolution Data Cubes Extracted From Real-Aperture 94-GHz Radar

Accurate, high-resolution 3-D mapping of environmental terrain is critical in a range of disciplines. In this study, we develop a new technique, called the PCFilt-94 algorithm, to extract 3-D point clouds from coarse-resolution millimeter-wave radar data cubes and quantify their associated uncertainties. A technique to noncoherently average neighboring waveforms surrounding each AVTIS2 range profile was developed to reduce speckles and was found to reduce point cloud uncertainty by 13% at long range and 20% at short range. Furthermore, a Voronoi-based point cloud outlier removal algorithm was implemented, which iteratively removes outliers in a point cloud until the process converges to the removal of 0 points. Taken together, the new processing methodology produces a stable point cloud, which means that: 1) it is repeatable even when using different point cloud extraction and filtering parameter values during preprocessing and 2) is less sensitive to overfiltering through the point cloud processing workflow. Using an optimal number of ground control points (GCPs) for georeferencing, which was determined to be 3 at close ranges (< 1.5 km) and 5 at long ranges (>3 km), point cloud uncertainty was estimated to be approximately 1.5 m at 1.5 km to 3 m at 3 km and followed a Lorentzian distribution. These uncertainties are smaller than those reported for other close-range radar systems used for terrain mapping. The results of this study should be used as a benchmark for future application of millimeter-wave radar systems for 3-D terrain mapping.