Efficient and robust convex relaxation methods for hybrid TOA/AOA indoor localization

Abstract

In this chapter, we proposed a hybrid positioning method based on convex relaxation with time of arrival (TOA) and angle of arrival (AOA) measurements. Traditional maximum likelihood (ML) formulation for indoor localization is a nonconvex optimization problem. We exploit the relaxation methods to provide efficient convex solution. Besides, we apply this method to localization with hybrid TOA/AOA measurements firstly and the linear Cramer-Rao Bounds in the scenarios of error-free and erroneous location of sensor nodes are deduced, respectively. Simulations based on TC-OFDM signal system show that the proposed method is efficient and more robust as compared to the existing ML estimation and TOA or AOA based convex relaxation with or without error of sensor nodes location.