Elliptic target positioning based on balancing parameter estimation and augmented Lagrange programming neural network

Elliptic positioning (EP) has been receiving increasing attention in recent years with the development of multistatic systems. This article considers mitigating the negative effects of biased measurements on the location estimation performance of EP, by introducing a balancing parameter into the traditional non-outlier-resistant least squares type formulation. The resulting problem is then solved by exploiting the augmented Lagrange programming neural network (ALPNN), which is a generally applicable and asymptotically stable nonlinear constrained neurodynamic optimization framework. Moreover, the Cramér-Rao lower bound for EP in non-Gaussian noise is derived. The superiority of the proposed ALPNN approach over a number of existing EP estimators is demonstrated through computer simulations.