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 tra...

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Bibliographic Details
Published in:Digital signal processing 2023-05, Vol.136, p.104004, Article 104004
Main Authors: Xiong, Wenxin, Liang, Junli, Wang, Zhi, So, Hing Cheung
Format: Article
Language:English
Subjects:
Augmented Lagrange programming neural network
Balancing parameter
Cramér-Rao lower bound
Elliptic positioning
Error mitigation
Neurodynamic optimization
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Summary: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.
ISSN:1051-2004
1095-4333
DOI:10.1016/j.dsp.2023.104004