Abstract
Classical localization methods use Cartesian or Polar coordinates,which require a priori range information to deter-mine whether to estimate position or to only find bearings.The modified polar representation(MPR)unifies near-field and far-field models,alleviating the thresholding effect.Current localiza-tion methods in MPR based on the angle of arrival(AOA)and time difference of arrival(TDOA)measurements resort to semidefinite relaxation(SDR)and Gauss-Newton iteration,which are computationally complex and face the possible diverge problem.This paper formulates a pseudo linear equation between the measurements and the unknown MPR position,which leads to a closed-form solution for the hybrid TDOA-AOA localization problem,namely hybrid constrained optimization(HCO).HCO attains Cramér-Rao bound(CRB)-level accuracy for mild Gaussian noise.Compared with the existing closed-form solutions for the hybrid TDOA-AOA case,HCO provides compa-rable performance to the hybrid generalized trust region sub-problem(HGTRS)solution and is better than the hybrid succes-sive unconstrained minimization(HSUM)solution in large noise region.Its computational complexity is lower than that of HGTRS.Simulations validate the performance of HCO achieves the CRB that the maximum likelihood estimator(MLE)attains if the noise is small,but the MLE deviates from CRB earlier.
基金项目
国家自然科学基金(62101359)
Sichuan University and Yibin Municipal People's Government University and City Strategic Cooperation Special Fund Project(2020CDYB-29)
Science and Technology Plan Transfer Payment Project of Sichuan Province(2021ZYSF007)
Key Research and Development Program of Science and Technology Department of Sichuan Province(2020YFS05752021KJT0012-2021YFS-0067)
NETL
NSTL
万方数据