From the point of view of metrology, inertial sensors acting separately present undesirable performance in the measurement of angular position. In order to provide measurements with greater precision and accuracy, the measures of each of these sensors are typically fused by means of filters. The performance parameters of these filters are hard to tune and several works have been using exhaustive search algorithm or manual experimental tests to tuning these parameters. However, the exhaustive search algorithm usually requires a large computational effort and adjusting parameters manually does not guarantee that the estimated parameters are optimized. In this work, it has been proposed the tuning of the Complementary Filter (CF) and the Kalman Filter (KF) through the heuristic method Evolutionary Strategies. Experimental results have shown that our method is a useful tool that considerably reduces the time to find the tuning of the FC and the FK. In addition, the use of the tuned FC and FK improved significantly metrological characteristics of the system. The use of Bland and Altman's statistical method show that the measurements of the angular position have a good agreement with the actual angular position of a servo motor.

中文翻译：

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应用传感器融合：通过进化策略调整 CF 和 KF 的参数

从计量学的角度来看，单独作用的惯性传感器在角位置测量中表现出不理想的性能。为了提供具有更高精确度和准确度的测量，这些传感器中的每一个的测量通常通过过滤器融合。这些滤波器的性能参数很难调整，一些工作已经使用详尽的搜索算法或手动实验测试来调整这些参数。然而，穷举搜索算法通常需要大量的计算工作，手动调整参数并不能保证估计参数得到优化。在这项工作中，已经提出了通过启发式方法进化策略来调整互补滤波器（CF）和卡尔曼滤波器（KF）。实验结果表明，我们的方法是一种有用的工具，它大大减少了找到 FC 和 FK 调谐的时间。此外，使用调谐的 FC 和 FK 显着改善了系统的计量特性。使用 Bland 和 Altman 的统计方法表明，角位置的测量值与伺服电机的实际角位置具有很好的一致性。