# A Novel FECAM-iTransformer Algorithm for Assisting INS/GNSS Navigation System during GNSS Outages
## 1 Introduction
For time-series data rich in motion and noise features, such as IMU data, LSTM and GRU may struggle to learn effective features. The attention-based Transformer [19] model has become a popular research direction in time-series prediction. Its well-designed attention mechanism can effectively learn both temporal and spatial characteristics. However, like most time-series prediction models, the Transformer is sensitive to inconsistencies in the timing of events. The Transformer treats each variable at the same timestamp as a token, which can introduce noise if the timestamps are misaligned or if there is a shift in the statistical distribution of the data [24]. The iTransformer [25] addresses this issue effectively by inputting the entire time series for each input channel into the model as a token, thereby avoiding the noise introduced by misaligned timestamps or non-stationary information. Additionally, the inclusion of the attention mechanism makes iTransformer more effective in capturing variable relationships between sequences. This model is particularly suitable for modeling multi-channel IMU data in INS/GNSS combined systems. Navigation data possess unique characteristics. In addition to their rich kinematic features, they also contain a significant amount of noise, which can mislead AI algorithms into learning invalid features. Therefore, a critical challenge is to enable AI to correctly understand and analyze the features of navigation data to achieve optimal prediction performance. The core sensor of the INS is IMU, which includes gyroscopes and accelerometers. Numerous studies have demonstrated that frequency analysis is an effective method for analyzing data from these devices. [PERSON] et al. analyzed the relationship between IMU measurement noise and inertial guidance system noise from a frequency-domain perspective to improve the positioning accuracy of the INS/DVL integrated navigation system [26]. [PERSON] et al. used Gaussian process regression, combining both time-domain and frequency-domain features of the IMU to estimate a human's walking speed [27]. [PERSON] et al. proposed a low-cost calibration method for IMUs, utilizing fast Fourier transform (FFT) to obtain frequency-domain data and find the frequency, amplitude, and phase at the spectrum's peak [28]. Frequency analysis methods are also widely used in time-series forecasting to extract compact and rich temporal features, such as FNet, frequency-enhanced decomposed transformer (FEDformer), and a frequency interpolation time-series analysis baseline (FITS) [29; 30; 31]. In this paper, a novel AI-assisted INS/GNSS integrated navigation system is proposed to address the rapid accuracy degradation of the INS/GNSS integrated navigation system during a GNSS outage. The iTransformer is introduced and optimized based on considerations of computational accuracy and data characteristics to make it more suitable for navigation. The main contributions of this paper are as follows:  
* Proposing FECAM-iTransformer: To extract more comprehensive information from IMU data, we introduce the frequency-enhanced channel attention mechanism (FECAM) [32] into the iTransformer. This mechanism applies attention across multiple frequency-domain channels to capture more compact, richer, and more comprehensive time features in navigation data. This enhancement broadens the iTransformer's perspective and improves its ability to model multi-channel IMU data effectively. * Proposing INS/FECAM-iTransformer Integrated Navigation System: This system provides pseudo-GNSS signals to the extended Kalman filter in case of a GNSS outage. * Conducting Extensive Experiments: We conduct experiments using both a high-precision public dataset and a low-precision personal dataset to evaluate the model's performance in two different scenarios: professional integrated navigation sensors and low-cost, small sensors, and the effectiveness of the proposed method is verified.