Site specific radar coverage and land clutter modelling

The objectives of this minor dissertation were to investigate relevant theory, models and processes required for the development of a site specific radar coverage and land clutter modelling tool. Various sources of digital elevation model (DEM) and land cover (LC) data were investigated. It was found that the ASTER GDEM and SRTM 30 m DEM datasets can be used to characterise land topography for all intended areas of interest. It was also found that two LC datasets, namely the National Land Cover 2009, and GlobeLand30 m data sources can be used to characterise land cover for all intended areas of interest. For each terrain type found in the GlobeLand30 or NLC 2009 datasets, a decision was made as to which of the terrain types for each land clutter model matches the land cover data terrain type the closest. These classifications were presented in the form of tables. It was concluded that the SRTM 30 m DEM dataset and the GlobeLand30 LC dataset should be used as they are currently the highest quality DEM and LC datasets that are freely available that covers all intended areas of interest. Numerous monostatic land clutter models exist in literature that address specific cases of clutter types and behaviours. Nine such land clutter models were investigated. Measured land clutter data collected over various terrain types in the Western Cape region of South Africa are compared to simulated backscatter data from these land clutter models. From insights gained from the literature study as well as the analysis of these comparisons, a classification was made on each model's compatibility and validity for different grazing angles and frequency ranges. A classification table was presented indicating the appropriate land clutter models to use in order of their validity, with respect to different grazing angle regions and frequency ranges.

Grantor dc:publisher.institution

Department of Electrical Engineering

Year dc:date.issued

2016

Author dc:creator

• Salie, Sulayman

Advisor dc:contributor.advisor

• Inggs, Michael

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/11427/20466

OAI identifier oai:identifier

oai:open.uct.ac.za:11427/20466