We design a solution to achieve coordinated localization between two unmanned aerial vehicles (UAVs) using radio and camera perception. We achieve the localization between the UAVs in the context of solving the problem of UAV Global Positioning System (GPS) failure or its unavailability. Our approach allows one UAV with a functional GPS unit to coordinate the localization of another UAV with a compromised or missing GPS system. Our solution for localization uses a sensor fusion and coordinated wireless communication approach.
Dataset used for "A Machine Learning Approach for Wi-Fi RTT Ranging" paper (ION ITM 2019). The dataset includes almost 30,000 Wi-Fi RTT (FTM) raw channel measurements from real-life client and access points, from an office environment. This data can be used for Time of Arrival (ToA), ranging, positioning, navigation and other types of research in Wi-Fi indoor location. The zip file includes a README file, a CSV file with the dataset and several Matlab functions to help the user plot the data and demonstrate how to estimate the range.
Copyright (C) 2018 Intel Corporation
Welcome to the Intel WiFi RTT (FTM) 40MHz dataset.
The paper and the dataset can be downloaded from:
To cite the dataset and code, or for further details, please use:
Nir Dvorecki, Ofer Bar-Shalom, Leor Banin, and Yuval Amizur, "A Machine Learning Approach for Wi-Fi RTT Ranging," ION Technical Meeting ITM/PTTI 2019
For questions/comments contact:
The zip file contains the following files:
1) This README.txt file.
2) LICENSE.txt file.
3) RTT_data.csv - the dataset of FTM transactions
4) Helper Matlab files:
O mainFtmDatasetExample.m - main function to run in order to execute the Matlab example.
O PlotFTMchannel.m - plots the channels of a single FTM transaction.
O PlotFTMpositions.m - plots user and Access Point (AP) positions.
O ReadFtmMeasFile.m - reads the RTT_data.csv file to numeric Matlab matrix.
O SimpleFTMrangeEstimation.m - execute a simple range estimation on the entire dataset.
O Office1_40MHz_VenueFile.mat - contains a map of the office from which the dataset was gathered.
Running the Matlab example:
In order to run the Matlab simulation, extract the contents of the zip file and call the mainFtmDatasetExample() function from Matlab.
Contents of the dataset:
The RTT_data.csv file contains a header row, followed by 29581 rows of FTM transactions.
The first column of the header row includes an extra "%" in the begining, so that the entire csv file can be easily loaded to Matlab using the command: load('RTT_data.csv')
Indexing the csv columns from 1 (leftmost column) to 467 (rightmost column):
O column 1 - Timestamp of each measurement (sec)
O columns 2 to 4 - Ground truth (GT) position of the client at the time the measurement was taken (meters, in local frame)
O column 5 - Range, as estimated by the devices in real time (meters)
O columns 6 to 8 - Access Point (AP) position (meters, in local frame)
O column 9 - AP index/number, according the convention of the ION ITM 2019 paper
O column 10 - Ground truth range between the AP and client (meters)
O column 11 - Time of Departure (ToD) factor in meters, such that: TrueRange = (ToA_client + ToA_AP)*3e8/2 + ToD_factor (eq. 7 in the ION ITM paper, with "ToA" being tau_0 and the "ToD_factor" lumps up both nu initiator and nu responder)
O columns 12 to 467 - Complex channel estimates. Each channel contains 114 complex numbers denoting the frequency response of the channel at each WiFi tone:
O columns 12 to 125 - Complex channel estimates for first antenna from the client device
O columns 126 to 239 - Complex channel estimates for second antenna from the client device
O columns 240 to 353 - Complex channel estimates for first antenna from the AP device
O columns 354 to 467 - Complex channel estimates for second antenna from the AP device
The tone frequencies are given by: 312.5E3*[-58:-2, 2:58] Hz (e.g. column 12 of the csv contains the channel response at frequency fc-18.125MHz, where fc is the carrier wave frequency).
Note that the 3 tones around the baseband DC (i.e. around the frequency of the carrier wave), as well as the guard tones, are not included.
Data (Non-Gaussian Noises and Measurement Information Loss)
The odometric model is simulated herein. We described the trajectory of such one odometric model, with the delta of the heading angle given as one parameter of the simulation. The iterations show that the trajectory is well in the continuity of the variations of the heading angle. Moreover the distance in X and in Y are shown for the vehicle to be driven in the trajectory of the odometric model.
Please take the odometric model in the context.