Browsing by Subject "Hidden markov models"

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    Novel signal processing techniques for remote sensing applications
    (2021-06) Gök, Gökhan
    Remote sensing is the acquisition of information about an object or phenomenon without a direct contact. In this thesis, novel signal processing techniques are pro-posed for two different remote sensing applications. First, for the reconstruction of electron density profile in the ionosphere using the ionosonde measurements, a new technique, ISED, is proposed. By using a hidden Markov model, ISED technique first identifies actual ionosonde echoes reflected from the ionosphere. Then, vertical electron density profile is estimated as the solution to a model based convex optimization problem. Conducted experiments on real ionosonde data show that ISED overperforms the state of the art ionogram inversion tools. Second part, for Specific Emitter Identification (SEI) of radar transmitters, a new SEI technique is proposed. SEI is an important Electronic Warfare (EW) activ-ity that aims to identify unique transmitters, even the emitters of same kind, by using the subtle differences on the transmitted signals. Proposed SEI consist of two main stages. In the first stage, received radar pulses are time aligned and co-herently integrated to reveal subtle differences which could be used to distinguish different radar transmitters. In the second step, Variational Mode Decomposition (VMD) is used to decompose both the envelope and the instantaneous frequency of the received radar signal into a set of modes. Then, these mod signals are characterized by using a group of features for identification. Highly successful identification performance with the proposed method on real radar datasets is demonstrated.