Sürü zekası kullanılarak yeni dizilim sinyal işleme tekniklerinin geliştirilmesi

In this thesis, novel array signal processing techniques are proposed for identificationof multipath communication channels based on cross ambiguity function(CAF) calculation, swarm intelligence and compressed sensing (CS) theory. Firsttechnique detects the presence of multipath components by integrating CAFs ofeach antenna output in the array and iteratively estimates direction-of-arrivals(DOAs), time delays and Doppler shifts of a known waveform. Second techniquecalled particle swarm optimization-cross ambiguity function (PSO-CAF) makesuse of the CAF calculation to transform the received antenna array outputs todelay-Doppler domain for efficient exploitation of the delay-Doppler diversity ofthe multipath components. Clusters of multipath components are identified byusing a simple amplitude thresholding in the delay-Doppler domain. PSO isused to estimate parameters of the multipath components in each cluster. Thirdproposed technique combines CS theory, swarm intelligence and CAF computation.Performance of standard CS formulations based on discretization of the multipathchannel parameter space degrade significantly when the actual channelparameters 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 beused in applications other than the multipath channel identification is proposed.Performances of the proposed techniques are verified both on sythetic and realdata.