Development of new array signal processing techniques using swarm intelligence
Güldoğan, Mehmet Burak
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In this thesis, novel array signal processing techniques are proposed for identifi- cation of multipath communication channels based on cross ambiguity function (CAF) calculation, swarm intelligence and compressed sensing (CS) theory. First technique detects the presence of multipath components by integrating CAFs of each antenna output in the array and iteratively estimates direction-of-arrivals (DOAs), time delays and Doppler shifts of a known waveform. Second technique called particle swarm optimization-cross ambiguity function (PSO-CAF) makes use of the CAF calculation to transform the received antenna array outputs to delay-Doppler domain for efficient exploitation of the delay-Doppler diversity of the multipath components. Clusters of multipath components are identified by using a simple amplitude thresholding in the delay-Doppler domain. PSO is used to estimate parameters of the multipath components in each cluster. Third proposed technique combines CS theory, swarm intelligence and CAF computation. Performance of standard CS formulations based on discretization of the multipath channel parameter space degrade significantly when the actual channel parameters deviate from the assumed discrete set of values. To alleviate this “off-grid”problem, a novel technique by making use of the PSO, that can also be used in applications other than the multipath channel identification is proposed. Performances of the proposed techniques are verified both on sythetic and real data.
cross ambiguity function (CAF)
particle swarm optimization (PSO)
compressed sensing (CS)