BPSK modulated GPS L1 CA signal is the most widely used GNSS signal to date, and the first path detection (FPD) of the conventional GPS L1 CA signals is the most challenging problem to ensure reliable GPS positioning in multipath environments. In this paper, we propose an FPD network (FPDN) based on multi-layer perceptron (MLP)-Mixer to extract the first path from the discrete autocorrelation function (ACF) output accurately with low computational cost. In addition, the proposed FPDN is useful in practice because it is robust to noise and achieves a high FPD performance without any prior assumption on the number of total incoming multipath, which is required for conventional signal processing-based FPD techniques. We compare the performance of the proposed FPDN to that of diverse conventional techniques, such as techniques based on narrow correlator, super-resolution, and some widely used CNNs such as VGGNet, ResNet, and U-Net, through simulations and field tests. As demonstrated, the proposed FPDN outperforms all of the compared FPD techniques in terms of the computational cost and accuracy for wide range of carrier-to-noise (C/N 0 ) ratios.

中文翻译：

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基于MLP混合器的GPS第一路径检测网络


BPSK调制的GPS L1 CA信号是迄今为止使用最广泛的GNSS信号，而传统GPS L1 CA信号的第一路径检测（FPD）是确保多路径环境中可靠的GPS定位最具挑战性的问题。在本文中，我们提出了一种基于多层感知器（MLP）-混合器的FPD网络（FPDN），以较低的计算成本从离散自相关函数（ACF）输出中准确地提取第一路径。此外，所提出的 FPDN 在实践中很有用，因为它对噪声具有鲁棒性，并且无需事先假设总传入多径数量即可实现高 FPD 性能，而这是传统的基于信号处理的 FPD 技术所需要的。通过模拟和现场测试，我们将所提出的 FPDN 的性能与各种传统技术的性能进行了比较，例如基于窄相关器、超分辨率的技术以及一些广泛使用的 CNN（例如 VGGNet、ResNet 和 U-Net）。正如所证明的，所提出的 FPDN 在计算成本和大范围载噪比 (C/N 0 ) 的精度方面优于所有比较的 FPD 技术。