A Bias-Reduced Nonlinear WLS Method for TDOA/FDOA-Based Source Localization

Document Type

Article

Publication Date

10-1-2016

Abstract

We address the source localization problem by using both time-difference-of-arrival (TDOA) and frequency-difference-of-arrival (FDOA) measurements. We solve this problem in two steps, and in each step, we formulate a nonlinear weighted least squares (WLS) problem followed by a bias reduction scheme. In the first step, we formulate a nonlinear WLS problem using TDOA measurements only and derive the bias of the WLS solution, which is then used to develop an unbiased WLS solution by subtracting the bias from the WLS solution. In the second step, we formulate another nonlinear WLS problem by combining the results in the first step and the FDOA measurements. To avoid the potential risk of local convergence, this WLS problem is reduced to an approximate WLS problem, for which the globally optimal solution can be obtained. The bias of the WLS solution is also derived and then subtracted from the WLS solution to reduce the bias. Simulation results show that the bias of the proposed method is reduced and that the Cramer-Rao lower bound accuracy is also achieved.

Identifier

85027436662 (Scopus)

Publication Title

IEEE Transactions on Vehicular Technology

External Full Text Location

https://doi.org/10.1109/TVT.2015.2508501

ISSN

00189545

First Page

8603

Last Page

8615

Issue

10

Volume

65

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