ABSTRACT Sensor fusion is the art of estimating accurate information from noisy multi-sensor data. Due to the complexity of stochastic sensor errors, design and testing of sensor fusion algorithms have been always challenging. Existing design approaches are mainly mission specific with fixed system models that do not verify if the filter can estimate hidden errors. To address these challenges, this paper presents a flexible design and simulation environment for sensor fusion. The environment utilizes symbolic engine as a flexible representation of system models to enable flexible and accurate generation of linearized error models. Inverse kinematic is used to generate pseudo-error-free inertial data to test the ability of the filte to estimate sensor errors. The developed environment is demonstrated on an Attitude and Heading Reference System using Extended Kalman Filter. The demonstration includes both simulation and experimental tests. The designed filter supports both loosely and tightly coupled filtering approaches.

中文翻译：

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使用符号引擎的传感器融合设计与仿真

摘要 传感器融合是从嘈杂的多传感器数据中估计准确信息的艺术。由于随机传感器误差的复杂性，传感器融合算法的设计和测试一直具有挑战性。现有的设计方法主要是针对特定任务的固定系统模型，这些模型不验证滤波器是否可以估计隐藏错误。为了应对这些挑战，本文提出了一种灵活的传感器融合设计和仿真环境。该环境利用符号引擎作为系统模型的灵活表示，从而能够灵活准确地生成线性化误差模型。逆运动学用于生成伪无误差惯性数据，以测试滤波器估计传感器误差的能力。使用扩展卡尔曼滤波器在姿态和航向参考系统上演示开发环境。演示包括模拟和实验测试。设计的过滤器支持松散和紧耦合过滤方法。