Department of Industrial Engineering, University of Naples Federico II, Piazzale Tecchio 80, 80125 Naples, Italy.
STMicroelectronics, Analog, MEMS and Sensor Group R&D, 80022 Arzano, Italy.
Sensors (Basel). 2023 Jul 3;23(13):6127. doi: 10.3390/s23136127.
In this paper, the authors investigate the possibility of applying artificial intelligence algorithms to the outputs of a low-cost Kalman filter-based navigation solution in order to achieve performance similar to that of high-end MEMS inertial sensors. To further improve the results of the prototype and simultaneously lighten filter requirements, different AI models are compared in this paper to determine their performance in terms of complexity and accuracy. By overcoming some known limitations (e.g., sensitivity on the dimension of input data from inertial sensors) and starting from Kalman filter applications (whose raw noise parameter estimates were obtained from a simple analysis of sensor specifications), such a solution presents an intermediate behavior compared to the current state of the art. It allows the exploitation of the power of AI models. Different Neural Network models have been taken into account and compared in terms of measurement accuracy and a number of model parameters; in particular, Dense, 1-Dimension Convolutional, and Long Short Term Memory Neural networks. As can be excepted, the higher the NN complexity, the higher the measurement accuracy; the models' performance has been assessed by means of the root-mean-square error () between the target and predicted values of all the navigation parameters.
本文研究了将人工智能算法应用于低成本卡尔曼滤波导航解算输出的可能性，以实现类似高端 MEMS 惯性传感器的性能。为了进一步提高原型的结果，并同时减轻滤波器的要求，本文比较了不同的 AI 模型，以确定它们在复杂性和准确性方面的性能。通过克服一些已知的局限性（例如，对来自惯性传感器的输入数据的维度的敏感性），并从卡尔曼滤波器应用（其原始噪声参数估计是从对传感器规格的简单分析中获得的）开始，这样的解决方案与当前的技术水平相比呈现出一种中间行为。它允许利用人工智能模型的力量。本文考虑了不同的神经网络模型，并根据测量精度和多个模型参数对其进行了比较；特别是密集型、一维卷积和长短期记忆神经网络。可以预期的是，神经网络的复杂性越高，测量精度越高；通过目标值与所有导航参数的预测值之间的均方根误差（）来评估模型的性能。
