Bias Compensated Inertial Navigation for Venus Balloon Missions

—In this paper we investigate various strategies forreducing navigation errors for a future Venus Balloon missionequipped with a low-mass MEMS IMU (e.g. STIM300), andfocus on two of these mitigation strategies in detail. First, weproposed to mechanically rotate the IMU while collecting datathereby averaging out the bias contributions and increasingaccuracy. To this end, we experimentally provide proof-ofconcept by rotating a STIM300 IMU about a single-axis andshow that navigation performance for this idealized scenario issubstantially improved. Second, we propose to use on-boardaccelerometer measurements to exploit the projection of thegravity field along the axes of the IMU frame to provide (2 axis)attitude information. To this end, we design a Kalman Filterfor a hovering balloon scenario where the accelerometer measurements are modeled as explicit functions of attitude and areincorporated as direct measurements in the filter and show thatthis strategy leads to improved navigation performance. Theresults given here are part of a longer-term effort to understandand improve navigation performance on a Venus Balloon andshould be considered as first steps toward this goal.

本文针对未来搭载轻量化微机电系统惯性测量单元（MEMS IMU，如STIM300）的金星气球任务，展开导航误差抑制策略研究，并重点对其中两种误差缓解方案进行详细分析。其一，本文提出在数据采集过程中对惯性测量单元实施机械旋转，以此通过平均效应抵消偏置项的影响，提升导航精度。为此，本文通过单轴旋转STIM300型惯性测量单元完成原理性验证实验，结果表明该理想化场景下的导航性能得到显著提升。其二，本文提出利用机载加速度计测量值，通过提取重力场沿惯性测量单元坐标系各轴的投影，获取两轴姿态信息。为此，本文针对悬停气球场景设计卡尔曼滤波器（Kalman Filter）：将加速度计测量值建模为姿态的显式函数，并作为直接测量项融入滤波器，结果表明该方案可有效改善导航性能。本文所得结果属于理解并优化金星气球任务导航性能的长期研究计划的一部分，可视作该目标下的初步探索成果。