This article presents a newly invented emergency positioning system deployed on a satellite communication on-the-move (SOTM) platform, which can extend the SOTM communication function to the positioning function. Inherent to the SOTM system’s high reliability, this emergency positioning system can effectively estimate the vehicle’s position by fusing a geosynchronous orbit (GEO) satellite Doppler frequency observation and inertial navigation system (INS) of a SOTM-mounted moving vehicle when the global navigation satellite system (GNSS) is unavailable. It is a great challenge to use GEO Doppler frequency measurement with weak observability to correct the INS’s cumulative error to estimate the position of the vehicle. In this article, the traditional inertial measurement unit (IMU) pre-integration model is improved to meet the needs of large-scale and high-precision navigation. In addition, we use graph optimization (GO) to estimate all states in the trajectory, which can effectively improve the observability of the error and reduce the nonlinear influence. We evaluated the system on a Ku-band SOTM platform, and the experimental results indicate that the system can achieve a convergent positioning accuracy of tens of meters. This method can provide emergency positioning in the case of GNSS interference.

An Emergency Positioning System Fusing GEO Satellite Doppler Observation and INS for SOTM

Current orientation estimation system (OES) approaches can meet the Satcom-on-the-move (SOTM) accuracy requirement in low dynamics using inertial measurement unit (IMU) without Global Navigation Satellite System (GNSS). However, the estimation accuracy will decrease significantly due to the influence of the high dynamics of the vehicle. This article addresses this issue by constructing a multi-state constraint optimization-based OES (MSCO-OES). Our first contribution is a high-precision incremental inertial constraint, which adds the influence of the earth’s rotation to the rotation part of the pre-integration theory to maintain the integral gyro accuracy for a longer time. The second contribution is that the nonlinear optimization method is applied to orientation estimation, making better use of historical information and having higher estimation accuracy and robustness through batch optimization and iterating. In addition, we use the lie group theory to extend the weighted least square estimation algorithm to the manifold, which can update multiple historical states to the current state in real-time through incremental inertial constraints. Compared with state-of-the-art methods, the proposed orientation estimation method has higher estimation precision and a reasonable calculation amount. The performance of the proposed method is demonstrated by comparing tests and SOTM deploying tests. Several high-dynamic and long-range tests show that the proposed OES has acceptable orientation estimation precision and reliability, which meets the high-reliable SOTM system requirements.

Ding Yuan

Papers

An Emergency Positioning System Fusing GEO Satellite Doppler Observation and INS for SOTM

A Robust Multi-State Constraint Optimization-Based Orientation Estimation System for Satcom-on-the-Move