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Comparison of a sensor fusion algorithm implementation on a C674X DSP and a Cortex A8 core

机译:比较C674X DSP和Cortex A8内核上的传感器融合算法实现

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Inertial sensors used for real time motion capturing of human movements usually consist of triads of gyroscopes, accelerometers, and magnetometers. Using these sensors for motion capturing therefore requires computational intensive sensor fusion algorithms, as drift free orientation in 3D space is desired rather than sensor RAW data. Kalman filters, comprising numerous floating point matrix operations, are a common algorithmic approach for this sensor fusion. Filter algorithms in literature heavily vary in achieved accuracy and computational effort. Performing sensor fusion on the TI Integra processor allows filter partitioning, either to the RISC core or to the DSP core. Therefore, either the Cortex A8 processor with its SIMD unit NEON or the C6A816x DSP could be used for efficient calculation. The paper presents implementation results for utilizing the TI Integra in a challenging biomedical application for movement sonification.
机译:用于实时捕捉人体运动的惯性传感器通常由陀螺仪,加速度计和磁力计三联体组成。因此,将这些传感器用于运动捕捉需要计算量大的传感器融合算法,因为需要3D空间中的无漂移方向而不是传感器RAW数据。包含众多浮点矩阵运算的卡尔曼滤波器是这种传感器融合的常用算法方法。文献中的过滤器算法在实现的准确性和计算工作上差异很大。在TI Integra处理器上执行传感器融合,可以将滤波器划分到RISC内核或DSP内核。因此,可以使用带有SIMD单元NEON的Cortex A8处理器或C6A816x DSP进行有效的计算。本文介绍了在具有挑战性的生物医学应用中利用TI Integra进行运动超声的实施结果。

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