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

Fusion-based impairment modelling for an intelligent radar sensor architecture

An intelligent radar sensor concept has been developed using a modelling approach for prediction of sensor performance, based on application of sensor and environment models. Land clutter significantly impacts on the operation of radar sensors operating at low-grazing angles. The clutter modelling technique developed in this thesis for the prediction of land clutter forms the clutter model for the intelligent radar sensor. Fusion of remote sensing data is integral to the clutter modelling approach and is addressed by considering fusion of radar remote sensing data, and mitigation of speckle noise and data transmission impairments. The advantages of the intelligent sensor approach for predicting radar performance are demonstrated for several applications using measured data. The problem of predicting site-specific land radar performance is an important task which is complicated by the peculiarities and characteristics of the radar sensor, electromagnetic wave propagation, and the environment in which the radar is deployed. Airborne remote sensing data can provide information about the environment and terrain, which can be used to more accurately predict land radar performance. This thesis investigates how fusion of remote sensing data can be used in conjunction with a sensor modelling approach to enable site-specific prediction of land radar performance. The application of a radar sensor model and a priori information about the environment, gives rise to the notion of an intelligent radar sensor which can adapt to dynamically changing environments through intelligent processing of this a priori knowledge. This thesis advances the field of intelligent radar sensor design, through an approach based on fusion of a priori knowledge provided by remote sensing data, and application of a modelling approach to enable prediction of radar sensor performance. Original contributions are made in the areas of intelligent radar sensor development, improved estimation of land surface clutter intensity for site-specific low-grazing angle radar, and fusion and mitigation of sensor and data transmission impairments in radar remote sensing data.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Fusion-based impairment modelling for an intelligent radar sensor architecture

An intelligent radar sensor concept has been developed using a modelling approach for prediction of sensor performance, based on application of sensor and environment models. Land clutter significantly impacts on the operation of radar sensors operating at low-grazing angles. The clutter modelling technique developed in this thesis for the prediction of land clutter forms the clutter model for the intelligent radar sensor. Fusion of remote sensing data is integral to the clutter modelling approach and is addressed by considering fusion of radar remote sensing data, and mitigation of speckle noise and data transmission impairments. The advantages of the intelligent sensor approach for predicting radar performance are demonstrated for several applications using measured data. The problem of predicting site-specific land radar performance is an important task which is complicated by the peculiarities and characteristics of the radar sensor, electromagnetic wave propagation, and the environment in which the radar is deployed. Airborne remote sensing data can provide information about the environment and terrain, which can be used to more accurately predict land radar performance. This thesis investigates how fusion of remote sensing data can be used in conjunction with a sensor modelling approach to enable site-specific prediction of land radar performance. The application of a radar sensor model and a priori information about the environment, gives rise to the notion of an intelligent radar sensor which can adapt to dynamically changing environments through intelligent processing of this a priori knowledge. This thesis advances the field of intelligent radar sensor design, through an approach based on fusion of a priori knowledge provided by remote sensing data, and application of a modelling approach to enable prediction of radar sensor performance. Original contributions are made in the areas of intelligent radar sensor development, improved estimation of land surface clutter intensity for site-specific low-grazing angle radar, and fusion and mitigation of sensor and data transmission impairments in radar remote sensing data