校准传感器提升TDOA/FDOA定位精度:降低噪声影响的方法

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本文探讨了在基于时间差分到达(Time Difference of Arrival, TDOA)和频率差分到达(Frequency Difference of Arrival, FDOA)的源定位技术中,校准传感器的应用。随着传感器位置不确定性对源定位精度的影响日益显著,引入校准传感器成为了提高定位准确性的关键策略。 校准传感器的核心理念在于,它们不仅能够接收源信号以及来自其他校准传感器的信号,还能主动发送校准信号,以减少由于传感器位置的随机噪声引起的定位误差。这种设计考虑到了实际环境中常见的位置漂移和测量偏差问题,有助于提升定位系统的稳健性和可靠性。 论文首先从理论上分析了使用校准传感器对源定位性能的影响,通过计算Cramer-Rao下界(Cramer-Rao Lower Bound, CRLB),这是一种衡量估计误差的理论极限,来评估系统性能。CRLB的减小表明,当校准传感器被有效地集成到系统中时,定位精度可以得到显著提升。 TDOA和FDOA技术依赖于多个接收节点测量信号到达时间或频率差异来确定信号源的位置。然而,这些方法对测量精确度的微小变化非常敏感,特别是在没有严格校准的情况下。校准传感器的存在可以提供额外的信息,如传感器间的相对位置校准,从而改善多径效应和同步误差的处理,进而优化定位算法的性能。 本文可能包括了校准传感器的设计、信号模型建立、校准信号的传播特性分析、校准过程中的误差模型以及如何将这些校准信息融入到TDOA和FDOA的源定位算法中。此外,可能还讨论了不同应用场景下校准传感器的有效性,比如无线通信网络、无人机导航或地震监测等,以及与传统无校准方法相比,采用校准传感器后的性能改进和优势。 这篇文章深入研究了校准传感器在提高源定位精度方面的应用,通过CRLB分析展示了其潜在的理论价值,并可能提供了实际操作和实施的建议,对于追求高精度、鲁棒性更强的无线通信和定位系统设计具有重要意义。

The human visual cortex is biased towards shape components while CNNs produce texture biased features. This fact may explain why the performance of CNN significantly degrades with low-labeled input data scenarios. In this paper, we propose a frequency re-calibration U-Net (FRCU-Net) for medical image segmentation. Representing an object in terms of frequency may reduce the effect of texture bias, resulting in better generalization for a low data regime. To do so, we apply the Laplacian pyramid in the bottleneck layer of the U-shaped structure. The Laplacian pyramid represents the object proposal in different frequency domains, where the high frequencies are responsible for the texture information and lower frequencies might be related to the shape. Adaptively re-calibrating these frequency representations can produce a more discriminative representation for describing the object of interest. To this end, we first propose to use a channel-wise attention mechanism to capture the relationship between the channels of a set of feature maps in one layer of the frequency pyramid. Second, the extracted features of each level of the pyramid are then combined through a non-linear function based on their impact on the final segmentation output. The proposed FRCU-Net is evaluated on five datasets ISIC 2017, ISIC 2018, the PH2, lung segmentation, and SegPC 2021 challenge datasets and compared to existing alternatives, achieving state-of-the-art results.请详细介绍这段话中的技术点和实现方式

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