arXiv 2209.007282022-09-01eess.SP

基于残差卷积神经网络的多径干扰模型阶估计

Model Order Estimation in the Presence of multipath Interference using Residual Convolutional Neural Networks

Jianyuan Yu, William W. Howard, Yue Xu, R. Michael Buehrer

摘要：

模型阶次估计（MoE）通常是波达方向（DoA）估计的先决条件。由于阵列几何形状的限制，通常不可能估计任意数量源的空间参数;通常需要估计信号模型。MoE是从多个候选信号中选择最可能的信号模型的过程。虽然经典方法在存在相干多径干扰的情况下无法达到MoE，但数据驱动的监督学习模型可以解决这个问题。代替经典的MLP（多层感知）或CNN（卷积神经网络）架构，我们提出了残差卷积神经网络（RCNN）的应用，分组对称核滤波器提供最先进的估计精度高达95.2%的相干多径的存在下，和加权损失函数，以消除模型阶数的低估误差。我们通过展示其对整体信号处理流程的影响来展示该方法的益处，该流程确定由阵列接收的总信号的数量、独立源的数量以及每个路径与这些源的关联。此外，我们表明，所提出的估计提供了准确的性能在各种阵列类型，可以识别过载的情况下，并最终提供强大的DOA估计和信号关联性能。

英文摘要：

Model order estimation (MOE) is often a pre-requisite for Direction of Arrival (DoA) estimation. Due to limits imposed by array geometry, it is typically not possible to estimate spatial parameters for an arbitrary number of sources; an estimate of the signal model is usually required. MOE is the process of selecting the most likely signal model from several candidates. While classic methods fail at MOE in the presence of coherent multipath interference, data-driven supervised learning models can solve this problem. Instead of the classic MLP (Multiple Layer Perceptions) or CNN (Convolutional Neural Networks) architectures, we propose the application of Residual Convolutional Neural Networks (RCNN), with grouped symmetric kernel filters to deliver state-of-art estimation accuracy of up to 95.2\% in the presence of coherent multipath, and a weighted loss function to eliminate underestimation error of the model order. We show the benefit of the approach by demonstrating its impact on an overall signal processing flow that determines the number of total signals received by the array, the number of independent sources, and the association of each of the paths with those sources . Moreover, we show that the proposed estimator provides accurate performance over a variety of array types, can identify the overloaded scenario, and ultimately provides strong DoA estimation and signal association performance.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

摘要：

英文摘要：

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