Kernel: Python 3 (Anaconda)
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latitude longitude altitude bearing gps_speed \
location_time
2018-05-14 04:00:41 40.666843 -73.949948 -6 0 40.959000
2018-05-14 04:00:42 40.666843 -73.949948 -6 0 40.959000
2018-05-14 04:00:43 40.666843 -73.949948 -6 0 45.509998
2018-05-14 04:00:44 40.666843 -73.949948 -6 0 45.509998
2018-05-14 04:00:45 40.666843 -73.949948 -6 0 48.543999
2018-05-14 04:00:46 40.666843 -73.949948 -6 0 48.543999
2018-05-14 04:00:47 40.666843 -73.949948 -6 0 60.680000
2018-05-14 04:00:48 40.666843 -73.949948 -6 0 60.680000
2018-05-14 04:00:49 40.666843 -73.949948 -6 0 74.333000
2018-05-14 04:00:50 40.666843 -73.949948 -6 0 74.333000
gps_accuracy gps_timestamp location_provider
location_time
2018-05-14 04:00:41 1.526270e+09 gps NaN
2018-05-14 04:00:42 1.526270e+09 gps NaN
2018-05-14 04:00:43 1.526270e+09 gps NaN
2018-05-14 04:00:44 1.526270e+09 gps NaN
2018-05-14 04:00:45 1.526270e+09 gps NaN
2018-05-14 04:00:46 1.526270e+09 gps NaN
2018-05-14 04:00:47 1.526270e+09 gps NaN
2018-05-14 04:00:48 1.526270e+09 gps NaN
2018-05-14 04:00:49 1.526270e+09 gps NaN
2018-05-14 04:00:50 1.526270e+09 gps NaN
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NormaltestResult(statistic=618.47623358871897, pvalue=5.0071693720945445e-135)
Since this data is normally distributed, it does not follow the Nakagami, Rice, or Rayleigh distributions. Therefore, in order to parse noise data from RSSI, will need more features, calculated more reliably.
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