Browsing by Subject "Iterative technique"

Filter results by typing the first few letters
Now showing 1 - 2 of 2
  • Thumbnail Image
    ItemOpen Access
    A complexity-reduced ML parametric signal reconstruction method
    (2011) Deprem, Z.; Leblebicioglu, K.; Arkan O.; Çetin, A.E.
    The problem of component estimation from a multicomponent signal in additive white Gaussian noise is considered. A parametric ML approach, where all components are represented as a multiplication of a polynomial amplitude and polynomial phase term, is used. The formulated optimization problem is solved via nonlinear iterative techniques and the amplitude and phase parameters for all components are reconstructed. The initial amplitude and the phase parameters are obtained via time-frequency techniques. An alternative method, which iterates amplitude and phase parameters separately, is proposed. The proposed method reduces the computational complexity and convergence time significantly. Furthermore, by using the proposed method together with Expectation Maximization (EM) approach, better reconstruction error level is obtained at low SNR. Though the proposed method reduces the computations significantly, it does not guarantee global optimum. As is known, these types of non-linear optimization algorithms converge to local minimum and do not guarantee global optimum. The global optimum is initialization dependent. © 2011 Z. Deprem et al.
  • Thumbnail Image
    ItemOpen Access
    Distributed bounding of feasible sets in cooperative wireless network positioning
    (IEEE, 2013) Gholami, M. R.; Wymeersch, H.; Gezici, Sinan; Ström, E. G.
    Locations of target nodes in cooperative wireless sensor networks can be confined to a number of feasible sets in certain situations, e.g., when the estimated distances between sensors are larger than the actual distances. Quantifying feasible sets is often challenging in cooperative positioning. In this letter, we propose an iterative technique to cooperatively outer approximate the feasible sets containing the locations of the target nodes. We first outer approximate a feasible set including a target node location by an ellipsoid. Then, we extend the ellipsoid with the measured distances between sensor nodes and obtain larger ellipsoids. The larger ellipsoids are used to determine the intersections containing other targets. Simulation results show that the proposed technique converges after a small number of iterations.