An Efficient Constrained Weighted Least Squares Method With Bias Reduction for TDOA-Based Localization

This paper addresses the source location problem by using time-difference-of-arrival (TDOA) measurements. The two-stage weighted least squares (TWLS) algorithm has been widely used in TDOA location. However, estimation accuracy of is poor and bias significant when measurement noise large. Owing to nonlinear nature system model, we reformulate localization as a constrained derive theoretical estimate from maximum-likelihood (ML) estimation. To reduce improve accuracy, novel bias-reduced method developed based on an iterative algorithm. new imposes set linear equality constraints instead quadratic suppress bias. Numerical simulations demonstrate performance improvement proposed over traditional methods. reduced significantly Cramér-Rao lower bound can also be achieved.