Passive Coherent Location (PCL) radar has proved to be feasible in a number of experimental systems, but the lack of comprehensive, published flight trials detracts somewhat from serious consideration of these PCL systems for operational applications, such as Air Traffic Control (ATC). The carrying out of flight trials is, in any case, difficult and very expensive.
This dissertation presents a method for accurately predicting the performance of a bistatic passive coherent location radar with the effects of the environment taken into account. The effect of the environment on a propagating electromagnetic wave is obtained from the Advanced Refractive Effects Prediction System (AREPS) model. The resulting performance predictions, in the form of spatial signal-to-noise ratio (SNR), signal-to-interference ratio (SIR) and signal-to-noise-plus-interference ratio (SNIR) maps, provide a powerful planning tool for the application of systems such as ATC. Furthermore, the spatial coverage maps, based on the bistatic radar equation, can be related to
a particular probability of detection and false alarm as well as to a required dynamic range of the receiver ADC. Overall, the method provides a visual, as well as a quantitative measure of radar coverage with region-specific atmospheric and terrain effects taken into account.
The method proposed in this dissertation offers a marked improvement over traditional performance prediction methods based on the bistatic radar equation within a free space or flat terrain environment.
It is understood that the direct path signal of the illuminating transmitter is the cause of some severe limitations within a PCL system. In the interest of suppressing the strong direct signal before the ADC and to complement the development of the prediction method, an antenna pattern was synthesised and applied to an array of folded dipoles in order to place a null in the direction of the strong transmitter. The synthesised antenna pattern and its improvement on the performance of the PCL system was then evaluated using the proposed prediction method presented in this dissertation.