Abstract
Low-cost inertial navigation systems (INS) are prone to sensor biases and measurement noise, which lead to rapid degradation of navigation accuracy during global positioning system (GPS) outages. To address this challenge and improve positioning continuity in GPS-denied environments, this paper proposes a brain-inspired GPS/INS fusion network (BGFN) based on spiking neural networks (SNNs). The BGFN architecture integrates a spiking Transformer with a spiking encoder to simultaneously extract spatial features from inertial measurement unit (IMU) signals and capture their temporal dynamics. By modeling the relationship between vehicle attitude, specific force, angular rate, and GPS-derived position increments, the network leverages both current and historical IMU data to estimate vehicle motion. The effectiveness of the proposed method is evaluated through real-world field tests and experiments on public datasets. Compared to conventional deep learning approaches, the results demonstrate that BGFN achieves higher accuracy and enhanced reliability in navigation performance, particularly under prolonged GPS outages.
Abstract (translated)
低成本惯性导航系统（INS）容易受到传感器偏差和测量噪声的影响，这会导致全球定位系统（GPS）中断期间导航精度迅速下降。为了解决这一挑战并提高在无GPS环境下定位的连续性，本文提出了一种基于脉冲神经网络（SNNs）的脑启发式GPS/INS融合网络（BGFN）。BGFN架构结合了脉冲Transformer和脉冲编码器，同时从惯性测量单元（IMU）信号中提取空间特征并捕捉其时间动态。通过建模车辆姿态、特定力、角速率与基于GPS的位置增量之间的关系，该网络利用当前及历史IMU数据来估计车辆运动。通过实地测试和公开数据集上的实验评估了所提出方法的有效性。相比传统的深度学习方法，结果显示BGFN在导航性能上实现了更高的精度和增强的可靠性，尤其是在长时间GPS中断的情况下。
URL
https://arxiv.org/abs/2601.08244
