论文标题
基于轻松的能量函数的模拟神经网络方法，用于靶向分布式MIMO雷达中的定位
A Relaxed Energy Function Based Analog Neural Network Approach to Target Localization in Distributed MIMO Radar
论文作者
论文摘要
模拟神经网络非常有效地解决了一些优化问题，并且它们已用于分布式多输入多输出（MIMO）雷达中的目标定位。在这项工作中，我们设计了一个新的基于放松的能量函数的神经网络（RNFNN），用于分布式MIMO雷达中的目标定位。我们从具有复杂的目标函数的最大可能性（ML）目标定位开始，该目标函数可以通过引入一些辅助变量来转换为具有相等限制的可拖动目标。与现有的Lagrangian编程神经网络（LPNN）方法不同，我们进一步放松了针对目标定位提出的优化问题，因此不再需要Lagrangian乘数项，从而导致具有更好凸性的放松能量功能。基于松弛的能量函数，以更简单的结构和更快的收敛速度实现了RNFNN。此外，在存在发送器和接收器位置误差的情况下，RNFNN方法扩展到定位。结果表明，提出的定位方法的性能在较大的信噪比（SNRS）范围内实现了Cramér-rao下限（CRLB）。提供了与最先进的方法的广泛比较，这证明了拟议方法在性能改善和计算复杂性（或收敛速度）方面的优势。
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).
