UWBMeasure

class pylayers.measures.mesuwb.UWBMeasure(nTx=1, h=1, display=False)

Bases: pylayers.util.project.PyLayers

UWBMeasure class

tdd

Time domain deconv data

fdd

Freq domain deconv data

Date_Time LQI Operators RAW_DATA CAL_DATAip Tx_height Tx_position

info() : show() emax() etot() TDoA() Fingerprint() fp() : calculate fingerprint TOA()

Methods Summary

Efirst([Tint, sym, dB])	calculate energy in first path
Emax([Tint, sym, dB])	calculate maximum energy
Epercent()
Etau0([Tint, sym, dB])	calculate the energy around delay tau0
Etot([toffns, tdns, dB])	Calculate total energy for the 4 channels
ecdf([Tnoise, rem_noise, in_positivity, …])	calculate energy cumulative density function
fp([alpha])	build fingerprint
info()
outlatex(S)	measurement output latex
show([fig, delay, display, col, xmin, xmax, …])	show measurement in time domain
tau_Emax()	calculate the delay of energy peak
tau_moy([display])	calculate mean excess delay
tau_rms([display])	calculate the rms delay spread
taumax()
tdelay()	build an array with delay values
toa_cum(n[, display])	threshold based toa estimation using cumulative energy
toa_max([n, display])	descendant threshold based toa estimation
toa_max2()	calculate toa_max (meth2)
toa_new([display])	descendant threshold based toa estimation
toa_th(r, k[, display])	threshold based toa estimation using energy peak
toa_win([n, display])	descendant threshold based toa estimation

Methods Documentation

Efirst(Tint=1, sym=0.25, dB=True)

calculate energy in first path

Tint : float sym : float dB : boolean

Emax(Tint=1, sym=0.25, dB=True)

calculate maximum energy

Tint : float sym :float dB : boolean

Epercent()

Etau0(Tint=1, sym=0.25, dB=True)

calculate the energy around delay tau0

Etot(toffns=0.7, tdns=75, dB=True)

Calculate total energy for the 4 channels

toffnsfloat

time offset for selecting time window

tdnsfloat

time duration of the window

This function gets the total energy of the channel

from [tau_0 + tofffset , tau_0 + toffset +tduration ]

ecdf(Tnoise=10, rem_noise=True, in_positivity=False, display=False, normalize=True, delay=0)

calculate energy cumulative density function

Tnoise rem_noise in_positivity display normalize delay

fp(alpha=0.1)

build fingerprint

alphafloat

alpha is a quantile parameter for taum and taurms calculation

info()

outlatex(S)

measurement output latex

M.outlatex(S)

S : a Simulation object

show(fig=[], delay=array([[0], [0], [0], [0]]), display=True, col=['k', 'b', 'g', 'c'], xmin=0, xmax=100, C=0, NC=1, typ='v')

show measurement in time domain

delay : np.array(1,4) display

optional

col

optional

xmin

optional

xmax

optional

C

optional

NC

optional

tau_Emax()

calculate the delay of energy peak

tau_moy(display=False)

calculate mean excess delay

tau_rms(display=False)

calculate the rms delay spread

taumax()

tdelay()

build an array with delay values

t2np.array

[tau0 , tau_th , toa_cum , toa_max , tau_moy ,tau_rms, tau_moy+tau_rms]

toa_cum(n, display=False)

threshold based toa estimation using cumulative energy

n : int display : boolean

toa_max(n=6, display=False)

descendant threshold based toa estimation

ninteger

(default 6)

toa_max2()

calculate toa_max (meth2)

toa_new(display=False)

descendant threshold based toa estimation

toa_th(r, k, display=False)

threshold based toa estimation using energy peak

rfloat

threshold los

kfloat

threshold nlos

toa_win(n=9, display=False)

descendant threshold based toa estimation

n : key parameter n = 9 display : False