# UWB-LOS-NLOS-Data-Set

**Repository Path**: daitole/UWB-LOS-NLOS-Data-Set

## Basic Information

- **Project Name**: UWB-LOS-NLOS-Data-Set
- **Description**: Repository with UWB data traces representing LOS and NLOS channel conditions in 7 different indoor locations.
- **Primary Language**: Python
- **License**: CC-BY-4.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 2
- **Forks**: 2
- **Created**: 2019-08-22
- **Last Updated**: 2022-06-20

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# UWB LOS and NLOS Data Set
Data set was created using [SNPN-UWB](http://www.log-a-tec.eu/mtc.html) board with DecaWave [DWM1000](http://www.decawave.com/sites/default/files/resources/dwm1000-datasheet-v1.3.pdf) UWB radio module. 

## Data Set Description
Measurements were taken on 7 different indoor locations:
* Office1
* Office2
* Small appartment
* Small workshop
* Kitchen with a living room
* Bedroom
* Boiler room.

In every indoor location 3000 LOS samples and 3000 NLOS samples were taken. Different locations were choosen to prevent building of location-specific LOS and NLOS models. All together 42000 samples were taken: 21000 for LOS and 21000 for NLOS channel condition. To make data set ready for building LOS and NLOS models, samples are randomized to prevent overfitting of a model to particular places. 
For measurements two UWB nodes were used: one node as an anchor and the second node as a tag. Only traces of LOS and NLOS channel measurements were taken without any reference positioning (this data set is not appropriate for localization evaluation).

## Data Set Structure
Folder with data set is organized as follows:

	+ code
		|____ uwb_dataset.py
	+ dataset
		|____ uwb_dataset_part1.csv
		|____ uwb_dataset_part2.csv
		|____ uwb_dataset_part3.csv
		|____ uwb_dataset_part4.csv
		|____ uwb_dataset_part5.csv
		|____ uwb_dataset_part6.csv
		|____ uwb_dataset_part7.csv

Whole data set is randomized and later split into 7 smaller files.

## File Structure
First line in every data set file is a header with column names. Elements of every sample are:
* NLOS (1 if NLOS, 0 if LOS)
* Measured range (time of flight)
* FP_IDX (index of detected first path element in channel impulse response (CIR) accumulator: in data set it can be accessed by **first_path_index+15**)
* FP_AMP1 (first path amplitude - part1) [look in user manual](http://thetoolchain.com/mirror/dw1000/dw1000_user_manual_v2.05.pdf)
* FP_AMP2 (first path amplitude - part2) [look in user manual](http://thetoolchain.com/mirror/dw1000/dw1000_user_manual_v2.05.pdf) 
* FP_AMP3 (first path amplitude - part3) [look in user manual](http://thetoolchain.com/mirror/dw1000/dw1000_user_manual_v2.05.pdf)
* STDEV_NOISE (standard deviation of noise)
* CIR_PWR (total channel impulse response power)
* MAX_NOISE (maximum value of noise)
* RXPACC (received RX preamble symbols)
* CH (channel number)
* FRAME_LEN (length of frame)
* PREAM_LEN (preamble length)
* BITRATE
* PRFR (pulse repetition frequency rate in MHz)
* CIR (absolute value of channel impulse response: 1016 samples with 1 nanosecond resolution)

## Importing Data Set in Python
To import data set data into Python environment, **uwb_dataset.py** script from folder **code** can be used. The CIR data still needs to be divided by number of acquired RX preamble samples (RX_PACC).

	import uwb_dataset
	
	# import raw data
	data = uwb_dataset.import_from_files()
	
	# divide CIR by RX preable count (get CIR of single preamble pulse)
	# item[2] represents number of acquired preamble symbols
	for item in data:
		item[15:] = item[15:]/float(item[2])
	
	print(data)

## Citation
If you are using our data set in your research, citation of the following paper would be greatly appreciated.

[Klemen Bregar, Andrej Hrovat, Mihael Mohorčič, “NLOS Channel Detection with Multilayer Perceptron in Low-Rate Personal Area Networks for Indoor Localization Accuracy Improvement”. Proceedings of the 8th Jožef Stefan International Postgraduate School Students’ Conference, Ljubljana, Slovenia, May 31-June 1, 2016.](https://www.researchgate.net/publication/308986067_NLOS_Channel_Detection_with_Multilayer_Perceptron_in_Low-Rate_Personal_Area_Networks_for_Indoor_Localization_Accuracy_Improvement)

## Author and license
Author of UWB LOS and NLOS Data Set and corresponding Python scripts is Klemen Bregar, **klemen.bregar@ijs.si**. 

Copyright (C) 2017 SensorLab, Jožef Stefan Institute http://sensorlab.ijs.si

## Acknowledgement
The research leading to these results has received funding from the European Horizon 2020 Programme project eWINE under grant agreement No. 688116.