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A Relaxed Energy Function Based Analog Neural Network Approach to Target Localization in Distributed MIMO Radar | | Xiaoyu Zhao
; Jun Li
; Qinghua Guo
; | | Date: |
1 Jan 2022 | | Abstract: | Analog neural networks are highly effective to solve some optimization
problems, and they have been used for target localization in distributed
multiple-input multiple-output (MIMO) radar. In this work, we design a new
relaxed energy function based neural network (RNFNN) for target localization in
distributed MIMO radar. We start with the maximum likelihood (ML) target
localization with a complicated objective function, which can be transformed to
a tractable one with equality constraints by introducing some auxiliary
variables. Different from the existing Lagrangian programming neural network
(LPNN) methods, we further relax the optimization problem formulated for target
localization, so that the Lagrangian multiplier terms are no longer needed,
leading to a relaxed energy function with better convexity. Based on the
relaxed energy function, a RNFNN is implemented with much simpler structure and
faster convergence speed. Furthermore, the RNFNN method is extended to
localization in the presence of transmitter and receiver location errors. It is
shown that the performance of the proposed localization approach achieves the
Cramér-Rao lower bound (CRLB) within a wider range of signal-to-noise ratios
(SNRs). Extensive comparisons with the state-of-the-art approaches are
provided, which demonstrate the advantages of the proposed approach in terms of
performance improvement and computational complexity (or convergence speed). | | Source: | arXiv, 2201.00122 | | Services: | Forum | Review | PDF | Favorites | |
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