An Improved Particle Filter Based on Gravity Measurement Feature in Gravity-Aided Inertial Navigation System

The existing gravity matching algorithms are affected by the initial position error of the inertial navigation system (INS), the gravity measurement error, and the similarity of the gravity background map. Aiming at the above problems, an improved particle filter based on the gravity measurement feature (IPFBGMF) is proposed in this article. In the IPFBGMF, both the value and change characteristic of gravity measurements are considered, and a novel position acquisition method based on the gravity measurement feature is proposed, which can reduce the influence of the initial position error of INS. In addition, a new concept called direction measurement using the heading angle of INS is proposed to optimize the weight of particles in the PF. The PF with direction measurement can reduce the influence of the gravity measurement error and the similarity of the gravity background map. Furthermore, the robustness of the improved PF with the precise position is proven. Finally, a navigation strategy is designed to apply the proposed algorithms. Simulations show that IPFBGMF has the highest positioning accuracy compared with the traditional gravity matching algorithms.

Automated summary

In this article, an improved particle filter based on the gravity measurement feature (IPFBGMF) is proposed to address the issues caused by the initial position error of the inertial navigation system (INS), gravity measurement error, and gravity background map similarity in existing gravity matching algorithms. The IPFBGMF takes into account both the value and change characteristic of gravity measurements and proposes a novel position acquisition method based on the gravity measurement feature, which reduces the impact of the initial position error of INS. Additionally, a new concept of direction measurement using the heading angle of INS is introduced to optimize the weight of particles in the particle filter (PF), mitigating the influence of gravity measurement error and gravity background map similarity. Furthermore, the robustness of the improved PF with precise position is demonstrated. A navigation strategy is designed for the application of the proposed algorithms. Simulation results show that IPFBGMF achieves the highest positioning accuracy compared to traditional gravity matching algorithms.