Now showing items 1-7 of 7

    • Classification of leg motions by processing gyroscope signals 

      Tunçel O.; Altun, K.; Barshan, B. (2009)
      In this study, eight different leg motions are classified using two single-axis gyroscopes mounted on the right leg of a subject with the help of several pattern recognition techniques. The methods of least squares, Bayesian ...
    • Cross-Term free based bistatic radar system using sparse least squares 

      Sevimli, R.A.; Cetin, A.E. (SPIE, 2015)
      Passive Bistatic Radar (PBR) systems use illuminators of opportunity, such as FM, TV, and DAB broadcasts. The most common illuminator of opportunity used in PBR systems is the FM radio stations. Single FM channel based PBR ...
    • Fundamental limits and improved algorithms for linear least-squares wireless position estimation 

      Guvenc, I.; Gezici, S.; Sahinoglu Z. (John Wiley & Sons, 2010-09-22)
      In this paper, theoretical lower bounds on performance of linear least-squares (LLS) position estimators are obtained, and performance differences between LLS and nonlinear least-squares (NLS) position estimators are ...
    • Range based sensor node localization in the presence of unknown clock skews 

      Gholami, M.R.; Gezici, S.; Strom, E.G. (2013)
      We deal with the positioning problem based on two-way time-of-arrival (TW-TOA) measurements in asynchronous wireless sensor networks. The optimal estimator for this problem poses a difficult global optimization problem. ...
    • Static positioning using UWB range measurements 

      Gholami, M.R.; Ström, E.G.; Sottile F.; Dardari, D.; Conti, A.; Gezici, S.; Rydström, M.; Spirito, M.A. (2010)
      The performance of several existing and partly new algorithms for positioning of sensor node based on distance estimate is compared when the distance estimates are obtained from a measurement campaign. The distance estimates ...
    • Structured least squares problems and robust estimators 

      Pilanci, M.; Arikan, O.; Pinar, M. C. (IEEE, 2010-10-22)
      A novel approach is proposed to provide robust and accurate estimates for linear regression problems when both the measurement vector and the coefficient matrix are structured and subject to errors or uncertainty. A new ...
    • Structured least squares with bounded data uncertainties 

      Pilanci, M.; Arikan, O.; Oguz, B.; Pinar, M. C. (2009)
      In many signal processing applications the core problem reduces to a linear system of equations. Coefficient matrix uncertainties create a significant challenge in obtaining reliable solutions. In this paper, we present a ...