Abstract: Inertial tracking (IT), independent of the environment and external infrastructure, has long been the ideal solution for providing location services to humans. Despite significant strides in inertial tracking empowered by deep learning, prevailing neural inertial tracking predominantly utilizes conventional spatial-temporal features from inertial measurements. Unfortunately, the frequency domain dimension is usually overlooked in the current literature. To this end, in this paper, we propose a Multi-Domain Mixture of Experts model for Neural Inertial Tracking, named M$^2$EIT. Specifically, M$^2$EIT first leverages ResNet as a spatial decomposition expert to capture spatial relationships between multivariate timeseries, and State Space Model (SSM)-based Bi-Mamba, the other expert to focus on learning temporal correlations. In the frequency domain mapping, we then introduce the Wavelet-based frequency decomposition expert, which decomposes IMU samples into low-frequency bands and high-frequency bands using the Haar wavelet transform for simulating motion patterns at different temporal scales. To bridge the semantic gap across multiple domains and integrate them adaptively, we design the Multi-Representation Alignment Router (MAR), which consists of a dual cross-domain translation layer, followed by a dynamic router, to achieve multi-domain semantic alignment and optimize expert contributions. Extensive experiments conducted on three real-world datasets demonstrate that the proposed M$^2$EIT can achieve SOTA results in neural inertial tracking.