By the term short-time motion tracking we refer to motions with durations up to few minutes, in which relative changes of altitude are to be tracked. Conversely, long-time motion tracking involves accurate tracking of absolute altitude over long time intervals. In human-centric applications, both tracking modes are involved: short-time tracking, e.g., fall detection; long-time tracking, e.g., personal navigation. Under conditions of short-time motion tracking, the time-varying mean is expected to vary very slowly, contributing an offset in the measured pressure altitude with no practical effect on the tracking accuracy. In those applications where accurate absolute altitude is needed, barometric altimeters in differential mode are perhaps the best option available to deal with slow changes of atmospheric pressure [11].

The proposed method of analysis decomposes the noise in three additive components with different physical origin and properties: a deterministic time-varying mean, correlated with slow pressure changes that can be involved in, e.g., local weather forecasting [13] and long-time tracking [14], whose modeling is beyond the scope of this paper; an exponentially time-correlated random process, modeled as a first-order Gauss-Markov (GM) process, that accounts for short-term, local environment changes, whose effect is prominent for short-time tracking; an uncorrelated random process, mainly due to wideband electronic noise, including quantization noise. We take the novel approach of using Gauss Markov (GM) random processes for modeling the short-time correlated component of altimeter noise.

The reason for this choice Carfilzomib is that GM random processes are mathematically simple, with only a limited number of parameters that need to be estimated [15]. Moreover, they seem to fit a wide variety of physical phenomena with reasonable accuracy [16]. 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.Experimental results obtained when the barometric altimeter is either motionless (for model identification) or moving (for tracking performance assessment) are presented. To this aim M-point moving average filters are applied to time-correlated pressure altitude signals, alone or cascaded with whitening filters learnt from ARMA model parameters. Advantages and disadvantages of removing the short-term correlation of the GM component by whitening filters are discussed.2.?Methods2.1. Measurement ProcessThe International Standard Atmosphere (ISA) model can be used to calculate the altitude from the output of an air pressure sensor [17].