Matched field processing localization with random sensor topologies

One of the largest challenges for multichannel localization systems is developing methodologies that are robust to interference. Unlike noise, interference is not random and often has characteristics resembling the true signals of interest. Interference often originates from multipath propagation, jamming signals, or other sources. In this paper, we demonstrate that we can significantly improve localization performance in the presence of interference through the use of a random sensor topology and matched field processing. To show this, we apply concepts and results from random matrix theory and compressed sensing. We demonstrate theoretically that random sensor topologies allow us to achieve performance characteristics similar to those of random noise. Specifically, we show that the localization performance improves, with a high probability, at a rate proportional to the number of sensors in the system. We verify these results through simulation.