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.

翻译：低成本惯性导航系统（INS）易受传感器偏差和测量噪声影响，导致全球定位系统（GPS）中断期间导航精度迅速下降。为应对这一挑战并提升GPS拒止环境下的定位连续性，本文提出一种基于脉冲神经网络（SNNs）的受大脑启发的GPS/INS融合网络（BGFN）。BGFN架构将脉冲Transformer与脉冲编码器相结合，以同时从惯性测量单元（IMU）信号中提取空间特征并捕获其时序动态特性。通过建模车辆姿态、比力、角速率与GPS衍生的位置增量之间的关系，该网络利用当前及历史的IMU数据来估计车辆运动。所提方法的有效性通过真实场景实地测试及公开数据集实验进行评估。与传统深度学习方法相比，结果表明BGFN在导航性能上实现了更高的精度与增强的可靠性，尤其在长时间GPS中断条件下。