Multi-static passive receiver location optimization in alpine terrain using a parallelized genetic algorithm

Passive Coherent Location (PCL) as `gap fillers´ of active surveillance is attractive due to their cost-effectiveness and covert operation. Computing optimal PCL receiver position has to take Line-of-Sight (LoS) into account, which is computationally challenging in highly mountainous terrain. Also the antenna characteristics and the bistatic geometry have to be accounted for. We propose a simulation and optimization tool implementing a parallelized multi-objective genetic algorithm to compute optimal receiver locations. We employ opensource libraries for genetic computing, inter-process communication and 3D graphics. The parallel approach enables scaling to geographical areas of any size. Our approach computes solutions with maximum SNR and high enough bistatic Doppler shifts and LoS conditions. FM transmitters in an alpine region are used to test our approach. We derive an extension of the Cramer-Rao Lower Bound (CRLB) for our problem and demonstrate the optimality of our results by comparing them to the accuracy prediction of the CRLB.