采用自适应矩的改进频率跟踪算法用于GNSS中的窄带干扰抑制
Improved Frequency Tracking with Adaptive Moments for Narrowband Interference Mitigation in GNSS
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中文总结 AI 辅助
针对GNSS窄带干扰抑制的需求，提出受Adam优化启发的改进频率跟踪算法，在资源使用略高于普通自适应陷波滤波器的情况下，实现了更优的干扰抑制性能。
中文摘要 AI 辅助
个人隐私设备（PPD）通常会发射强单频信号或扫频窄带信号，对附近的全球导航卫星系统（GNSS）接收机造成干扰，故意导致其失锁。接收机采用的干扰抑制方法通过跟踪干扰的瞬时频率，在时域（如陷波滤波）或变换域（如傅里叶域抑制）中去除这些分量。为在不劣化GNSS信号的前提下实现有效抑制，抑制位置必须精准；若位置偏移，有时甚至比不抑制干扰对性能的损害更大。因此，跟踪器的评估需兼顾动态跟踪性能（尤其针对快速扫频干扰）和稳态估计抖动，二者反映了跟踪速度与方差之间的基本权衡。我们提出一种新的频率跟踪算法，在标准输出功率最小化目标下，针对复一阶IIR陷波滤波器，采用梯度的一阶和二阶矩，相比现有方法实现了更优的权衡。该算法灵感源自广泛用于优化神经网络参数的Adam优化方法，我们首先描述了该算法及其与现有自适应陷波滤波器（ANF）更新规则的关联。接下来，我们在广泛的模拟和实测干扰事件上分析性能，包括公开数据集，并以基于锁频环（FLL）的设计等最先进方法作为基准。最后，我们分析了资源利用率和延迟，并量化了量化和流水线延迟对所提方法及代表性最先进基线的影响。所提的基于Adam的方法相比普通ANF资源使用略有增加，但在大多数场景下提供了更好的抑制效果。
英文摘要
Personal privacy devices (PPDs) typically emit strong tones or swept narrowband signals to jam nearby GNSS receivers and deliberately cause loss of lock. Excision methods deployed on receivers mitigate such interferers by tracking their instantaneous frequency and removing those components in either the time domain (e.g., notch filtering) or a transform domain (e.g., Fourier-domain excision). For effective mitigation without degrading the GNSS signal, the excision location must be precise; misplacement can sometimes harm performance even more than leaving the interferer unmitigated. Trackers are therefore evaluated for both dynamic tracking performance, especially against fast-sweeping jammers, as well as steady-state estimation jitter, reflecting a fundamental tracking speed versus variance trade-off. We propose a new frequency tracking algorithm that provides a better trade-off than existing methods using first and second moments of the gradient for a complex first-order IIR notch filter under a standard output power minimization objective. We first describe the algorithm, which is inspired by the Adam optimization method widely used for optimizing neural network parameters, and its relation to existing adaptive notch filter (ANF) update rules. Next, we analyze performance over a wide range of simulated and recorded interference events, including publicly available datasets, and benchmark against state-of-the-art methods including frequency-locked-loop (FLL) based designs. Finally, we analyze resource utilization and latency, and quantify the effects of quantization and pipeline delays on the proposed method and on representative state-of-the-art baselines. Our proposed Adam-based method shows marginally higher resource usage than a vanilla ANF while providing better suppression under most scenarios.
