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

Abstract

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 least-squares computation is the main cause of bias problems associated with this TDOA localization method. The bias reduction method proposed in this paper, which we call IV- BiasRed, exploits the use of instrumental variables (IV) to eliminate the troublesome noise correlation between the regressor and regressand. The IV- BiasRed method is demonstrated by way of simulations to achieve a significant bias reduction and mean-squared error performance close to the Cramer-Rae lower bound. While producing an estimation performance on par with the maximum likelihood estimator and a recently proposed bias reduction method, the proposed IV-BiasRed method is computationally much more efficient than existing bias reduction methods.