In this article, the multistatic localization problem with unknown propagation speed is investigated using differential delays and Doppler shifts between the signals from direct and indirect paths. A series of pseudolinear equations is formulated via the transformation of measurement models. A weighted least squares (WLS) formulation is then proposed after ignoring the second-order error terms, which can be rewritten as a nonconvex optimization problem with the relationships among variables included as constraints. To deal with the nonconvexity of the problem, semidefinite relaxation is applied, resulting in a convex semidefinite program (SDP). Several reasonable second-order cone constraints constructed via basic inequality and Cauchy–Schwarz inequality are added to tighten the relaxed SDP problem. By preserving the second-order error terms in equations, the bias of the estimate from the WLS formulation is also derived and then subtracted to nearly eliminate the bias and reach a bias-reduced solution. Simulation results show that the mean square error of the proposed method approaches the Cramer–Rao lower bound, and the bias is reduced significantly.


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    Titel :

    A Bias-Reduced Solution for Multistatic Localization Using Differential Delays and Doppler Shifts


    Beteiligte:
    Qi, Qinke (Autor:in) / Li, Youming (Autor:in) / Guo, Qiang (Autor:in)


    Erscheinungsdatum :

    01.10.2023


    Format / Umfang :

    1239406 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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