Inertial localization is regarded as a promising positioning solution for consumer-grade IoT devices due to its cost-effectiveness and independence from external infrastructure. However, data-driven inertial localization methods often rely on increasingly complex network architectures to improve accuracy, which challenges the limited computational resources of IoT devices. Moreover, these methods frequently overlook the importance of modeling long-term dependencies in inertial measurements - a critical factor for accurate trajectory reconstruction - thereby limiting localization performance. To address these challenges, we propose a reparameterized inertial localization network that uses a multi-branch structure during training to enhance feature extraction. At inference time, this structure is transformed into an equivalent single-path architecture to improve parameter efficiency. To further capture long-term dependencies in motion trajectories, we introduce a temporal-scale sparse attention mechanism that selectively emphasizes key trajectory segments while suppressing noise. Additionally, a gated convolutional unit is incorporated to effectively integrate long-range dependencies with local fine-grained features. Extensive experiments on public benchmarks demonstrate that our method achieves a favorable trade-off between accuracy and model compactness. For example, on the RoNIN dataset, our approach reduces the Absolute Trajectory Error (ATE) by 2.59% compared to RoNIN-ResNet while reducing the number of parameters by 3.86%.

翻译：惯性定位因其成本效益高且无需依赖外部基础设施，被视为消费级物联网设备极具前景的定位解决方案。然而，数据驱动的惯性定位方法通常依赖日益复杂的网络架构来提高精度，这对物联网设备有限的计算资源构成了挑战。此外，这些方法常常忽视对惯性测量中长期依赖性建模的重要性——这是精确轨迹重建的关键因素——从而限制了定位性能。为应对这些挑战，我们提出一种重参数化惯性定位网络，该网络在训练阶段采用多分支结构以增强特征提取能力。在推理阶段，该结构被转换为等效的单路径架构以提高参数效率。为进一步捕捉运动轨迹中的长期依赖性，我们引入了一种时序尺度稀疏注意力机制，该机制选择性地强调关键轨迹段，同时抑制噪声。此外，网络还集成了一个门控卷积单元，以有效整合长程依赖性与局部细粒度特征。在公开基准上的大量实验表明，我们的方法在精度与模型紧凑性之间实现了良好的平衡。例如，在RoNIN数据集上，与RoNIN-ResNet相比，我们的方法将绝对轨迹误差降低了2.59%，同时参数数量减少了3.86%。