The BioRobotics Institute, Scuola Superiore Sant'Anna/P.zza Martiri della Libertà, 33, 56124 Pisa, Italy.
Sensors (Basel). 2013 Nov 18;13(11):15692-707. doi: 10.3390/s131115692.
The question whether barometric altimeters can be applied to accurately track human motions is still debated, since their measurement performance are rather poor due to either coarse resolution or drifting behavior problems. As a step toward accurate short-time tracking of changes in height (up to few minutes), we develop a stochastic model that attempts to capture some statistical properties of the barometric altimeter noise. The barometric altimeter noise is decomposed in three components with different physical origin and properties: a deterministic time-varying mean, mainly correlated with global environment changes, and a first-order Gauss-Markov (GM) random process, mainly accounting for short-term, local environment changes, the effects of which are prominent, respectively, for long-time and short-time motion tracking; an uncorrelated random process, mainly due to wideband electronic noise, including quantization noise. Autoregressive-moving average (ARMA) system identification techniques are used to capture the correlation structure of the piecewise stationary GM component, and to estimate its standard deviation, together with the standard deviation of the uncorrelated component. M-point moving average filters used alone or in combination with whitening filters learnt from ARMA model parameters are further tested in few dynamic motion experiments and discussed for their capability of short-time tracking small-amplitude, low-frequency motions.
气压高度计是否可以用于准确跟踪人体运动仍存在争议，因为其测量性能较差，原因是分辨率低或存在漂移问题。为了实现对高度变化（长达几分钟）的准确短期跟踪，我们开发了一个随机模型，试图捕捉气压高度计噪声的一些统计特性。气压高度计噪声可以分解为三个具有不同物理起源和特性的分量：一个时变的确定性均值，主要与全球环境变化有关，以及一个一阶高斯-马尔可夫（GM）随机过程，主要用于短期、局部环境变化，其影响在长时间和短时间运动跟踪中分别显著；一个不相关的随机过程，主要是由于宽带电子噪声，包括量化噪声。自回归移动平均（ARMA）系统识别技术用于捕捉分段平稳 GM 分量的相关结构，并估计其标准差，以及不相关分量的标准差。单独使用或与从 ARMA 模型参数学习的白化滤波器组合使用的 M 点移动平均滤波器在一些动态运动实验中进行了进一步测试，并讨论了它们对小幅度、低频运动的短期跟踪能力。
