Statistics

Mathematical analysis

Psychology

Upper and lower bounds

Social psychology

Estimation theory

Cramér–Rao bound

Multiplicative function

Mathematics

Mathematical optimization

Estimator

Semidefinite programming

Operating system

Relaxation (psychology)

Computer science

Algorithm

IEEE Transactions on Vehicular Technology

A new cooperative received signal strength-based localization algorithm is proposed which employs relative error estimation and semidefinite programming (SDP). First, the log-normal shadowing RSS measurement model is transformed into an equivalent multiplicative model. Then, a relative error estimation criterion is used with this model to develop a nonconvex estimator to approximate the maximum likelihood solution. Finally, semidefinite relaxation is applied to the nonconvex estimator to obtain an SDP estimator. The proposed algorithm is first derived for noncooperative RSS-based localization and then extended to the cooperative case. The Cramer-Rao lower bound is derived for cooperative RSS-based localization. Performance results are presented, which demonstrate that the proposed SDP estimator provides a significant improvement over existing localization methods.

Cooperative RSS-Based Localization in Wireless Sensor Networks Using Relative Error Estimation and Semidefinite Programming