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Kutluyil Dogancay

7 accepted papers

2018

Computationally Efficient Iv-Based Bias Reduction for Closed-Form Tdoa Localization

ICASSP 2018accepted

This paper develops a new computationally efficient bias reduction method for the well-known algebraic closed-form solution for time-difference-of-arrival (TDOA) localization developed by Chan and Ho. The noise correlation between the regressor and regressand in the formulation of the linearized lea…

Cited by 0SourceScholar
2018

Improved Weighted Instrumental Variable Estimator for Doppler-Bearing Source Localization in Heavy Noise

ICASSP 2018accepted

In Doppler-bearing source localization, pseudolinear estimators are appealing alternatives to the divergence-prone and computationally-demanding iterative maximum likelihood estimator. Among the existing pseudolinear estimators, the weighted instrumental variable estimator (WIVE) is the most attract…

Cited by 0SourceScholar
2016

3D pseudolinear Kalman filter with own-ship path optimization for AOA target tracking

ICASSP 2016accepted

This paper investigates the problem of how to optimize the path of a single moving own-ship for angle-of-arrival (AOA) target tracking in three-dimensional (3D) space. First, a novel 3D pseudolinear Kalman filter (PLKF) is proposed to reduce computational complexity and to improve stability of an ex…

Cited by 0SourceScholar
2016

Algebraic solution for stationary emitter geolocation by a LEO satellite using Doppler frequency measurements

ICASSP 2016accepted

This paper presents a new algebraic solution for the Doppler positioning problem where the position of a stationary emitter is estimated from Doppler frequency measurements collected by a single low-earth-orbit (LEO) satellite. The proposed algebraic solution can be used for effective initialization…

Cited by 0SourceScholar
2015

Model-distributed solution of regularized least-squares problem over sensor networks

ICASSP 2015accepted

We develop a fully-distributed iterative algorithm for finding a model-distributed least-squares solution of systems of linear equations over sensor networks. Here, model-distributed means the solution vector is distributed across the network rather than being replicated at each node. For this purpo…

Cited by 0SourceScholar