With an ever worsening geopolitical situation, not least due to Russia’s offensive war in Ukraine, the need to detect enemy movement and disrupt their detection capabilities has increased in defensive value. Using real equipment to test different strategies and placements of radar systems is both costly and time consuming, and this thesis therefore investigates methods of simulating large-scale scenarios of electronic warfare in real-time. The proposed methods include using approximations, multithreading, simplified signal representations and fast convolutions. The results show that if high efficiency is required, the developed simulation structure with a simplified signal representation is able to process one million signals every 50 ms which indicates that realistic and large-scale simulations of this kind is possible. If a more realistic approach is required, a representation using sampled signals is proposed which also utilizes the GPU, though results show that the efficiency drops to around 185.1162 s per one million signals.

In radars, matched filters are used in the receiver of the system. Since the target velocity is not known a priori, degradation occurs due to mismatch of the return signal and the matched filter. The performance of the radar can be improved by using a bank of matched filters. The first topic investigated in this work is optimization of the bank of matched filter structure. Two methods are proposed for the design of the parallel filter structure and computations are performed with both methods. The output signal of a radar receiver filter consists not only of the main peak from the target but also of range sidelobes. In a multi-target radar environment, the sidelobes of one large target may appear as a smaller target at another range, or the integrated sidelobes from targets or clutter may mask all the information of another target. The second part of this thesis discusses the methods for decreasing the sidelobe level of the receiver output. Two methods are studied for this purpose. The first is the classical amplitude weighting and the second is the use of an inverse filter that minimizes total sidelobe energy. Both methods decrease the sidelobe levels while bringing a mismatch loss and main peak broadening. For the inverse filter case it is observed that the effect of inverse filter becomes evident as the filter length is increased beyond some point. Finally, the effects of quantization on video signal and the receiver filter coefficients are evaluated. It is observed that 16 bits quantization is sufficient for all kinds of receiver filters tested.