# Skyfall: Signal Fusion from a Smartphone Falling from the Stratosphere
## 3 Materials and Methods
### Smartphone
#### 3.3.2 Barometer  
The specifications for the station barometer obtained through the Android framework are listed in Table 6. The barometer, accelerometer, gyroscope, and magnetometer sensors are sampled unevenly. The sampling strategy is to poll the sensor for changes, and only record if there is change. The primary metric is the sample interval between data points, which is averaged over the Skyfall data window to obtain a mean sample interval with its standard deviation. This can then be converted to a mean sample rate in Hz (Table 6). The barometer magnitude at sea level should have a static steady-state value of ~101 kPa. The expected static DC and AC offsets of the various on-board sensors can serve as a first-order quality check for a sensor's state of health.  
The pressure range of STMicroelectronics LPS22 HH [66] is 260-1260 hPa (26-126 kPa), in contrast to the 0.8-120 kPa Bounder range [59]. The LPS22 HH accuracy is 0.05 kPa, with a low-pressure sensor noise of 0.65 Pa. The digital output is specified as 24 bit and its sensitivity as 4096 LSB/hPa.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Description** & **Field** & **Epoch s** & **Human UTC** \\ \hline Station Start Date 1 & station\_start\_date() & 1603719281 & 2020-10-26 13:34:41 \\ \hline Event Start Date & station\_first\_data\_timestamp() & 1603806314 & 2020-10-27 13:45:14 \\ \hline Event End Date & station\_last\_data\_timestamp() & 1603808114 & 2020-10-27 14:15:14 \\ \hline \hline \end{tabular} 1  
Not available before v.2.6.3.  
\end{table}
Table 4: Station and Event Date.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Description** & **Field** & **Value** \\ \hline Sensor Name & station\_audio\_sensor().name & 1/INTERNAL MIC \\ \hline Nominal Rate Hz & station\_audio\_sample\_rate\_nominal\_hz() & 8000.0 \\ \hline Sample Rate Hz & station\_audio\_sensor().sample\_rate\_hz() & 8000.043426 \\ \hline Sample Interval s & station\_audio\_sensor().sample\_interval\_s0 & 0.000124999 \\ \hline Interval Dev s & station\_audio\_sensor().sample\_interval\_std\_s0 & 0.00000052 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Audio Sensor.  
It is known that the height above the ellipsoid estimated from GNSS constellations has relatively large errors, and we can use the barometer as a secondary estimate for height. We expect the Bounder barometer to perform much better at high elevation as it can go down to 0.8 kPa. Barotmetric pressure is transitory and depends strongly on the specific time and location. Since the Bounder provides independent measures of height and pressure, we can construct an empirical function to compute height from pressure for the balloon height profile. Since pressure is expected to drop as the exponential of height, we performed a polynomial fit to the natural logarithm of scaled pressure. The method for the polynomial fit is provided in the redvox-pandas repository [54] as the function \(\text{bounder\_model\_height\_from\_pressure}\).  
Figure 4 compares the Bounder and S10 barometer data, showing a very good match below 20 km but divergence above 20 km heights. We postulate the Bounder barometer will be the most accurate of the two at high altitudes (>20 km) as its low-pressure specifications are better matched to the \(\sim\)36 km height of this case study. Note the sample rate of the Bounder is \(\sim\)1 Hz, whereas the sample rate of the S10 barometer is \(\sim\)24 Hz.