Efficient Location Estimators in NLOS Environments

In the paper we consider location estimation in an non-line-of- sight (NLOS) environment. A constrained optimization based location algorithm is proposed to jointly estimate the unknown location and bias by using the sequential quadratic programming (SQP) algorithm. This method does not rely on any prior statistics information, and simulation results show that the proposed method outperforms the existing related methods considerably. To reduce the complexity of the SQP based algorithm, we further propose a Taylor-series expansion based linear quadratic programming (TS-LQP) algorithm. Simulation results demonstrate that the computational complexity of the TS-LQP algorithm is only a fraction of that of the SQP algorithm while the accuracy loss is marginal.