A Novel Vehicle Localization Method Based on Adaptive Singular Spectrum Analysis Using Low-Cost INS/GNSS

A low-cost inertial navigation system (INS) and global navigation satellite system (GNSS) fusion position estimator is affected by accuracy limitations and multiple noises, leading to significant errors in positioning estimation. This paper proposes a new fusion algorithm, SSA-ESKF, which combines singular spectrum analysis (SSA) and an error-state Kalman filter (ESKF). The low-cost inertial measurement unit (IMU) and GNSS data obtained from the GNSS receivers are separately subjected to SSA noise reduction. The SSA noise-reduced data is then utilized in the ESKF. Consequently, the SSA-ESKF demonstrates superior performance in terms of lower state errors compared to the conventional ESKF. This approach helps minimize the impact caused by neglecting higher-order terms in the Taylor expansion and enhances the linearization of the ESKF, thereby achieving improved positioning accuracy. However, the SSA typically relies on empirically selecting constant singular values, which may result in an incomplete or excessive separation of signal and noise. To address this limitation, we further propose an adaptive spectral singularity analysis (ASSA) that yields better results when integrating the noise-reduced data into the ESKF. To verify the proposed method, the KITTI dataset experiments and real vehicle experiments with low-cost INS/GNSS were designed. The comparison of experimental results between the KF, ESKF and the SSA-ESKF, ASSA-ESKF indicates the superiority of the ASSA-ESKF. In addition, the ablation experiments were conducted to verify the effectiveness of the SSA on IMU data and GPS data independently, and the results showed the effectiveness of the SSA on GPS data.