DeepUWB
DOI10.5281/zenodo.6611037Zenodo6611037MaRDI QIDQ6720423
Dataset published at Zenodo repository.
Author name not available (Why is that?)
Publication date: 25 November 2020
Copyright license: No records found.
A dataset for UWB ranging error mitigation in indoor environments, built using Decawave EVB1000 devices and the firmwarecontiki-uwb.Additional information can be found in the attached file readme.txt or in the paperRobust Ultra-wideband Range Error Mitigation with Deep Learning at the Edge. [ readme.txt ] * Every sample has the following structure: || CIR (157 float values)|| || Error [m]|| || Room (int)|| || Obstacle (10 bool values)|| || Measured Range (UWB) [m]|| * Room encoding: 0 - cross-room measurements 1 - big room 2 - medium room 3 - small room 4 - outdoor * Obstacle encoding: (1-hot encoding) 1000000000 - wall 0100000000 - polystyrene plate 0010000000 - plastic (trash bin and chair) 0001000000 - plywood plate 0000100000 - cardboard box 0000010000 - LCD TV 0000001000 - metal plate 0000000100 - wood door 0000000010 - glass plate 0000000001 - metal window * Reading Code: # Import libraries import pandas as pd import numpy as np # Extract dataset dataset = pd.read_pickle(dataset.pkl) # Select specific obstacle configurations ds = np.asarray(dataset.loc[dataset[Objects]==011111111]CIR,Error) # Select specific rooms ds = np.asarray(dataset.loc[dataset[Room]==1]CIR,Error) # Select all samples ds = np.asarray(datasetCIR,Error) # Get X,y for training X = np.vstack(ds[:,0]) y= np.array(ds[:,1])
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