Abstract
Passive Bistatic Radar (PBR) receives high interest because of exploiting existing signals of opportunity from the surrounding environment such as TV and Radio signals. It reduces the pollution and the interference since it doesn't require a dedicated transmitter. However, PBR needs a novel algorithm to detect the target accurately since the RF transmitted signals is not under the control of the radar designer and has a variable structure of the ambiguity function. So, novel adaptive cancellation filters such as Extensive Cancellation Algorithm (ECA) was designed which has proven to detect the target accurately. However, ECA is a computationally intensive algorithm. This work involves transformation of ECA by exploring opportunities of any computation and storage that can be eliminated. ECA algorithm also has been implemented on GPU by exploiting parallel and pipelining approaches. The computation time of our transformed algorithm has improved by a factor of 3.8. Also, the achieved speed-up of GPU over our sequentially transformed algorithm is improved by up to 20.8.
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