Datasets:
Tasks:
Tabular Classification
Sub-tasks:
tabular-single-column-regression
Size Categories:
100K<n<1M
Language Creators:
expert-generated
License:
The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 13 new columns (-79, -46, -91, -59, -65, -57, -30, -73, -83, -47, 0.25, 1.25, -69) and 13 missing columns (0, -48, -61, 1, -80, -51, -54, -76, -32, -53, -75, -95, -66).
This happened while the csv dataset builder was generating data using
hf://datasets/Brosnan/WIFI_RSSI_Indoor_Positioning_Dataset/Nexus4_Data/Long_Traj_Full.csv (at revision 48e546938ba5143a4af5c62ac868fa4d357b557b)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Error code: UnexpectedError
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1
float64 | 0
float64 | -32
int64 | -54
int64 | -48
int64 | -51
int64 | -66
int64 | -53
int64 | -61
int64 | -76
int64 | -75
int64 | -95
int64 | -80
int64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
2.4087 | 0.011361 | -33 | -55 | -47 | -43 | -69 | -54 | -66 | -70 | -82 | -90 | -79 |
3.9252 | 0.020731 | -33 | -52 | -46 | -48 | -70 | -64 | -61 | -71 | -76 | -94 | -80 |
5.25 | 0 | -35 | -46 | -46 | -47 | -52 | -60 | -59 | -63 | -77 | -83 | -81 |
5.1841 | -0.6951 | -41 | -47 | -46 | -52 | -72 | -58 | -56 | -64 | -75 | -86 | -81 |
5.1962 | -1.4167 | -38 | -47 | -42 | -62 | -58 | -61 | -64 | -61 | -72 | -79 | -85 |
5.2009 | -2.1747 | -45 | -44 | -47 | -59 | -54 | -62 | -61 | -61 | -72 | -83 | -88 |
5.1705 | -2.9514 | -39 | -41 | -56 | -56 | -52 | -69 | -70 | -64 | -76 | -78 | -89 |
5.1552 | -3.7281 | -40 | -43 | -56 | -59 | -53 | -67 | -100 | -58 | -71 | -70 | -87 |
5.1775 | -4.4401 | -49 | -38 | -54 | -62 | -54 | -67 | -70 | -58 | -68 | -73 | -89 |
5.1917 | -5.1733 | -48 | -35 | -56 | -62 | -54 | -68 | -69 | -52 | -70 | -81 | -92 |
5.1961 | -5.9329 | -42 | -34 | -61 | -66 | -54 | -76 | -68 | -56 | -70 | -72 | -91 |
5.1711 | -6.7009 | -44 | -33 | -54 | -61 | -51 | -67 | -70 | -56 | -72 | -74 | -92 |
5.1884 | -7.4524 | -47 | -38 | -61 | -66 | -49 | -74 | -73 | -61 | -71 | -71 | -94 |
5.2032 | -8.1684 | -47 | -43 | -59 | -61 | -47 | -76 | -69 | -62 | -78 | -68 | -90 |
5.1903 | -8.9568 | -46 | -47 | -58 | -66 | -43 | -78 | -79 | -59 | -72 | -65 | -94 |
5.174 | -9.693 | -48 | -42 | -61 | -63 | -41 | -77 | -72 | -67 | -73 | -74 | -96 |
5.2257 | -10.442 | -54 | -47 | -62 | -65 | -40 | -77 | -78 | -71 | -71 | -65 | -95 |
5.2195 | -11.173 | -57 | -45 | -63 | -66 | -42 | -76 | -74 | -61 | -75 | -63 | -93 |
5.2349 | -11.957 | -53 | -43 | -61 | -67 | -39 | -75 | -77 | -63 | -69 | -54 | -93 |
5 | -11.75 | -56 | -45 | -63 | -65 | -41 | -78 | -73 | -68 | -75 | -63 | -95 |
6.415 | -11.758 | -54 | -45 | -69 | -81 | -31 | -85 | -75 | -77 | -83 | -53 | -95 |
7.8699 | -11.822 | -64 | -50 | -59 | -85 | -26 | -89 | -75 | -83 | -69 | -58 | -98 |
9.3938 | -11.765 | -61 | -52 | -64 | -83 | -34 | -90 | -76 | -86 | -67 | -48 | -98 |
10.857 | -11.741 | -65 | -53 | -67 | -86 | -30 | -84 | -72 | -87 | -72 | -57 | -94 |
12.381 | -11.828 | -62 | -57 | -66 | -82 | -33 | -76 | -71 | -83 | -63 | -62 | -89 |
13.861 | -11.781 | -62 | -60 | -72 | -91 | -38 | -88 | -75 | -94 | -64 | -56 | -92 |
15.337 | -11.793 | -64 | -58 | -70 | -91 | -42 | -88 | -68 | -92 | -65 | -54 | -90 |
16.873 | -11.82 | -73 | -57 | -75 | -91 | -51 | -94 | -69 | -95 | -57 | -61 | -93 |
18.382 | -11.832 | -67 | -60 | -69 | -96 | -43 | -91 | -62 | -98 | -59 | -66 | -82 |
19.905 | -11.827 | -68 | -65 | -68 | -93 | -44 | -86 | -61 | -91 | -55 | -67 | -76 |
20.684 | -11.887 | -66 | -63 | -66 | -91 | -43 | -80 | -61 | -83 | -48 | -60 | -68 |
20.724 | -10.369 | -74 | -68 | -64 | -88 | -53 | -83 | -61 | -88 | -55 | -67 | -73 |
20.67 | -8.9234 | -73 | -66 | -67 | -93 | -51 | -85 | -56 | -89 | -42 | -80 | -68 |
20.672 | -7.3857 | -72 | -61 | -64 | -93 | -56 | -83 | -51 | -94 | -38 | -85 | -66 |
20.654 | -5.9016 | -71 | -64 | -65 | -87 | -59 | -79 | -50 | -92 | -52 | -82 | -60 |
20.701 | -4.4163 | -65 | -61 | -68 | -89 | -62 | -79 | -57 | -82 | -36 | -86 | -66 |
20.688 | -2.928 | -62 | -62 | -67 | -86 | -62 | -78 | -46 | -94 | -50 | -86 | -60 |
20.618 | -1.489 | -73 | -70 | -63 | -84 | -65 | -75 | -42 | -94 | -50 | -91 | -60 |
20.667 | 0.12384 | -63 | -67 | -55 | -81 | -70 | -78 | -41 | -93 | -43 | -90 | -56 |
20.728 | 1.6463 | -62 | -72 | -56 | -81 | -60 | -65 | -35 | -93 | -53 | -87 | -51 |
20.641 | 3.0994 | -55 | -69 | -46 | -76 | -70 | -58 | -39 | -95 | -49 | -85 | -54 |
20.934 | 4.2221 | -61 | -72 | -44 | -78 | -68 | -66 | -39 | -97 | -52 | -88 | -55 |
19.446 | 4.2144 | -59 | -76 | -49 | -83 | -77 | -62 | -33 | -97 | -57 | -96 | -52 |
17.938 | 4.1889 | -52 | -75 | -47 | -70 | -84 | -59 | -28 | -96 | -59 | -93 | -51 |
16.431 | 4.2242 | -57 | -72 | -44 | -77 | -75 | -57 | -33 | -92 | -64 | -97 | -51 |
15 | 4.25 | -56 | -71 | -47 | -70 | -100 | -59 | -35 | -93 | -63 | -97 | -58 |
13.401 | 3.995 | -53 | -71 | -33 | -65 | -100 | -50 | -46 | -88 | -64 | -97 | -54 |
11.907 | 3.9855 | -46 | -66 | -32 | -64 | -66 | -54 | -43 | -91 | -76 | -94 | -69 |
10.431 | 3.9378 | -41 | -60 | -28 | -59 | -70 | -45 | -49 | -81 | -69 | -91 | -65 |
9 | 4 | -41 | -64 | -29 | -61 | -64 | -49 | -51 | -81 | -67 | -85 | -71 |
7.6982 | 4.2019 | -40 | -61 | -33 | -58 | -75 | -54 | -48 | -86 | -77 | -95 | -71 |
6.2041 | 4.1638 | -41 | -61 | -38 | -56 | -67 | -54 | -53 | -85 | -70 | -90 | -77 |
5.1901 | 3.7113 | -38 | -55 | -35 | -54 | -67 | -47 | -50 | -82 | -69 | -83 | -73 |
5.2049 | 2.1871 | -47 | -51 | -45 | -50 | -60 | -58 | -60 | -77 | -73 | -86 | -78 |
5.1901 | 0.80391 | -39 | -49 | -37 | -49 | -60 | -58 | -58 | -83 | -72 | -86 | -75 |
4.6147 | 0.24589 | -38 | -54 | -45 | -53 | -65 | -61 | -58 | -68 | -75 | -84 | -83 |
3.1601 | 0.2261 | -34 | -54 | -40 | -45 | -68 | -59 | -60 | -80 | -87 | -87 | -81 |
1.6686 | 0.27602 | -32 | -55 | -49 | -46 | -68 | -57 | -62 | -87 | -75 | -94 | -79 |
1.25 | 0.25 | -32 | -59 | -46 | -51 | -71 | -57 | -57 | -82 | -82 | -92 | -81 |
2.6639 | 0.21569 | -32 | -53 | -44 | -53 | -66 | -61 | -63 | -83 | -82 | -92 | -82 |
4.1644 | 0.25332 | -34 | -52 | -44 | -46 | -68 | -56 | -64 | -74 | -78 | -90 | -79 |
5.25 | 0.25 | -35 | -50 | -39 | -49 | -55 | -59 | -65 | -68 | -74 | -82 | -83 |
5.2862 | 1.5333 | -40 | -57 | -37 | -48 | -62 | -58 | -57 | -81 | -76 | -84 | -80 |
5.2134 | 3.1619 | -36 | -53 | -36 | -50 | -62 | -55 | -51 | -69 | -74 | -81 | -77 |
5.6555 | 4.2214 | -41 | -58 | -35 | -53 | -72 | -55 | -55 | -78 | -75 | -90 | -72 |
7.1857 | 4.2434 | -38 | -64 | -44 | -58 | -74 | -53 | -57 | -80 | -79 | -91 | -79 |
8.7367 | 4.1576 | -37 | -65 | -33 | -60 | -69 | -50 | -51 | -80 | -74 | -89 | -66 |
9.9203 | 3.9699 | -47 | -55 | -33 | -63 | -69 | -47 | -48 | -81 | -68 | -89 | -66 |
11.416 | 4.0115 | -50 | -65 | -38 | -65 | -65 | -45 | -44 | -81 | -78 | -95 | -61 |
12.913 | 4.0452 | -48 | -64 | -37 | -74 | -71 | -57 | -40 | -94 | -65 | -95 | -60 |
14.419 | 3.9874 | -47 | -66 | -37 | -74 | -100 | -59 | -38 | -98 | -63 | -100 | -57 |
15.911 | 4.2211 | -52 | -70 | -44 | -77 | -72 | -58 | -27 | -92 | -68 | -97 | -51 |
17.405 | 4.2199 | -57 | -70 | -41 | -70 | -80 | -61 | -27 | -96 | -65 | -98 | -51 |
18.956 | 4.1877 | -59 | -72 | -48 | -74 | -77 | -60 | -33 | -100 | -64 | -87 | -51 |
20.488 | 4.235 | -57 | -76 | -42 | -75 | -74 | -63 | -38 | -95 | -60 | -90 | -54 |
20.753 | 3.5027 | -56 | -76 | -45 | -77 | -70 | -59 | -36 | -97 | -49 | -91 | -48 |
20.634 | 2.0787 | -58 | -75 | -58 | -79 | -64 | -66 | -38 | -96 | -54 | -84 | -50 |
20.662 | 0.55859 | -67 | -65 | -53 | -80 | -73 | -64 | -45 | -97 | -58 | -90 | -55 |
20.671 | -0.91105 | -74 | -69 | -59 | -86 | -70 | -78 | -46 | -95 | -39 | -86 | -55 |
20.691 | -2.4391 | -67 | -73 | -61 | -90 | -60 | -77 | -48 | -97 | -37 | -83 | -57 |
20.629 | -3.9983 | -70 | -63 | -64 | -87 | -57 | -79 | -50 | -83 | -39 | -87 | -64 |
20.695 | -5.3705 | -68 | -57 | -67 | -88 | -57 | -79 | -57 | -86 | -40 | -82 | -61 |
20.659 | -6.9519 | -72 | -72 | -67 | -91 | -61 | -82 | -58 | -89 | -40 | -87 | -73 |
20.689 | -8.4162 | -70 | -60 | -70 | -91 | -54 | -83 | -55 | -88 | -42 | -80 | -69 |
20.626 | -9.9114 | -75 | -64 | -71 | -89 | -59 | -86 | -54 | -90 | -46 | -69 | -69 |
20.679 | -11.423 | -68 | -58 | -72 | -92 | -47 | -88 | -63 | -91 | -47 | -65 | -73 |
20.404 | -10.583 | -74 | -77 | -75 | -94 | -56 | -87 | -65 | -97 | -53 | -77 | -78 |
20.5 | -12 | -71 | -66 | -74 | -94 | -46 | -86 | -64 | -93 | -58 | -67 | -72 |
18.883 | -11.821 | -74 | -61 | -72 | -91 | -46 | -89 | -61 | -95 | -55 | -68 | -83 |
17.379 | -11.807 | -73 | -66 | -75 | -90 | -53 | -93 | -73 | -96 | -61 | -60 | -85 |
15.882 | -11.799 | -68 | -61 | -69 | -91 | -47 | -93 | -70 | -94 | -58 | -57 | -90 |
14.37 | -11.759 | -70 | -63 | -70 | -87 | -36 | -90 | -73 | -90 | -66 | -57 | -91 |
12.909 | -11.737 | -60 | -60 | -69 | -85 | -35 | -79 | -75 | -89 | -67 | -65 | -93 |
11.363 | -11.76 | -64 | -56 | -61 | -84 | -37 | -79 | -71 | -89 | -69 | -52 | -92 |
9.8655 | -11.78 | -65 | -56 | -64 | -88 | -26 | -87 | -74 | -90 | -66 | -53 | -97 |
8.3643 | -11.811 | -62 | -52 | -66 | -86 | -27 | -88 | -75 | -83 | -69 | -58 | -96 |
6.897 | -11.754 | -59 | -50 | -69 | -86 | -34 | -86 | -78 | -86 | -82 | -54 | -96 |
5.2349 | -11.957 | -53 | -44 | -61 | -65 | -37 | -75 | -73 | -64 | -66 | -54 | -94 |
5.2195 | -11.173 | -56 | -47 | -63 | -67 | -41 | -76 | -72 | -62 | -76 | -63 | -93 |
5.2257 | -10.442 | -52 | -46 | -64 | -65 | -40 | -77 | -70 | -71 | -71 | -65 | -94 |
End of preview.
WIFI RSSI Indoor Positioning Dataset
A reliable and comprehensive public WiFi fingerprinting database for researchers to implement and compare the indoor localization’s methods.The database contains RSSI information from 6 APs conducted in different days with the support of autonomous robot.
We use an autonomous robot to collect the WiFi fingerprint data. Our 3-wheel robot has multiple sensors including wheel odometer, an inertial measurement unit (IMU), a LIDAR, sonar sensors and a color and depth (RGB-D) camera. The robot can navigate to a target location to collect WiFi fingerprints automatically. The localization accuracy of the robot is 0.07 m ± 0.02 m. The dimension of the area is 21 m × 16 m. It has three long corridors. There are six APs and five of them provide two distinct MAC address for 2.4- and 5-GHz communications channels, respectively, except for one that only operates on 2.4-GHz frequency. There is one router can provide CSI information.
Data Format
X Position (m), Y Position (m), RSSI Feature 1 (dBm), RSSI Feature 2 (dBm), RSSI Feature 3 (dBm), RSSI Feature 4 (dBm), ...
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