def scenario():
"""
This method is not a class member, it defines a sample scenario.
Defines a sample scenario for testing
Returns
-------
CRB : float
Examples
--------
.. plot::
:include-source:
>>> nRN = 4
>>> dim = 3 # 2 for 2D, 3 for 3D
>>> L = 20.
>>> c = 0.3
>>> BN = L*sp.rand(dim,1)
>>> BN0 = L*sp.rand(dim,1)
>>> RN_TOA = L*sp.rand(dim,nRN)
>>> RN_RSS = L*sp.rand(dim,nRN)
>>> RN_TDOA = L*sp.rand(dim,nRN)
>>> d_TOA = dist(RN_TOA,BN,0) # actual distances
>>> TOF = d_TOA/c # actual TOA
>>> TOA_std = 0.001/c*np.ones(np.shape(TOF))
>>> TOA = TOF + TOA_std
>>> rss_std = 0.001 * np.ones(nRN)
>>> rss_np = 2.645 * np.ones(nRN)
>>> pl0 = 34.7*np.ones(nRN)
>>> d0 = 1.
>>> d_RSS = dist(RN_RSS,BN,0) # actual distances
>>> X = rss_std * np.random.randn(np.shape(pl0)[0])
>>> rss_db = pl0-10*rss_np*np.log10(d_RSS/d0)+X
>>> RNr_TDOA = np.zeros((dim,nRN))#L*sp.rand(dim,nRN)
>>> d = dist(RN_TDOA,BN,0)
>>> dr = dist(RNr_TDOA,BN,0)
>>> TDOF = (d-dr)/c # actual TDOA
>>> TDOA_std = 0.001/c*np.ones(np.shape(TDOF))
>>> TDOA = TDOF + TDOA_std
>>> nodes={}
>>> nodes['BN']= BN
>>> nodes['RN_RSS']= RN_RSS
>>> nodes['RN_TOA']= RN_TOA
>>> nodes['RN_TDOA']= RN_TDOA
>>> nodes['RNr_TDOA']= RNr_TDOA
>>> ldp={}
>>> ldp['RSS'] = rss_db
>>> ldp['RSS_std'] = rss_std
>>> ldp['RSS_np'] = rss_np
>>> ldp['d0'] = d0
>>> ldp['PL0'] = pl0
>>> ldp['TOA'] = TOA
>>> ldp['TOA_std'] = TOA_std
>>> ldp['TDOA'] = TDOA
>>> ldp['TDOA_std'] = TDOA_std
>>> print 'Nodes'
>>> print nodes
>>> print 'LDPs'
>>> print ldp
>>> print 'BN0:initial guess for ML estimator'
>>> print BN0
"""
nrn = 4
dim = 3 # 2 for 2D, 3 for 3D
L = 20.
c = 0.3
bn = L * sp.rand(dim, 1)
bn0 = L * sp.rand(dim, 1)
rn_toa = L * sp.rand(dim, nrn)
rn_rss = L * sp.rand(dim, nrn)
rn_tdoa = L * sp.rand(dim, nrn)
d_toa = dist(rn_toa, bn, 0) # actual distances
tof = d_toa / c # actual TOA
toa_std = 0.001 / c * np.ones(np.shape(tof))
toa_ns = tof + toa_std
rss_std = 0.001 * np.ones(nrn)
rss_np = 2.645 * np.ones(nrn)
pl0 = 34.7 * np.ones(nrn)
d0 = 1.
d_rss = dist(rn_rss, bn, 0) # actual distances
x = rss_std * np.random.randn(np.shape(pl0)[0])
rss_db = pl0 - 10 * rss_np * np.log10(d_rss / d0) + x
rnr_tdoa = np.zeros((dim, nrn)) # L*sp.rand(dim,nRN)
d = dist(rn_tdoa, bn, 0)
dr = dist(rnr_tdoa, bn, 0)
tdof = (d - dr) / c # actual TDOA
tdoa_std = 0.001 / c * np.ones(np.shape(tdof))
tdoa_ns = tdof + tdoa_std
nodes = {}
nodes['BN'] = bn
nodes['RN_RSS'] = rn_rss
nodes['RN_TOA'] = rn_toa
nodes['RN_TDOA'] = rn_tdoa
nodes['RNr_TDOA'] = rnr_tdoa
ldp = {}
ldp['RSS'] = rss_db
ldp['RSS_std'] = rss_std
ldp['RSS_np'] = rss_np
ldp['d0'] = d0
ldp['PL0'] = pl0
ldp['TOA'] = toa_ns
ldp['TOA_std'] = toa_std
ldp['TDOA'] = tdoa_ns
ldp['TDOA_std'] = tdoa_std
return nodes, ldp, bn0
def scenario():
"""
This method is not a class member, it defines a sample scenario.
Defines a sample scenario for testing
Returns
-------
CRB : float
Examples
--------
.. plot::
:include-source:
>>> nRN = 4
>>> dim = 3 # 2 for 2D, 3 for 3D
>>> L = 20.
>>> c = 0.3
>>> BN = L*sp.rand(dim,1)
>>> BN0 = L*sp.rand(dim,1)
>>> RN_TOA = L*sp.rand(dim,nRN)
>>> RN_RSS = L*sp.rand(dim,nRN)
>>> RN_TDOA = L*sp.rand(dim,nRN)
>>> d_TOA = dist(RN_TOA,BN,0) # actual distances
>>> TOF = d_TOA/c # actual TOA
>>> TOA_std = 0.001/c*np.ones(np.shape(TOF))
>>> TOA = TOF + TOA_std
>>> rss_std = 0.001 * np.ones(nRN)
>>> rss_np = 2.645 * np.ones(nRN)
>>> pl0 = 34.7*np.ones(nRN)
>>> d0 = 1.
>>> d_RSS = dist(RN_RSS,BN,0) # actual distances
>>> X = rss_std * np.random.randn(np.shape(pl0)[0])
>>> rss_db = pl0-10*rss_np*np.log10(d_RSS/d0)+X
>>> RNr_TDOA = np.zeros((dim,nRN))#L*sp.rand(dim,nRN)
>>> d = dist(RN_TDOA,BN,0)
>>> dr = dist(RNr_TDOA,BN,0)
>>> TDOF = (d-dr)/c # actual TDOA
>>> TDOA_std = 0.001/c*np.ones(np.shape(TDOF))
>>> TDOA = TDOF + TDOA_std
>>> nodes={}
>>> nodes['BN']= BN
>>> nodes['RN_RSS']= RN_RSS
>>> nodes['RN_TOA']= RN_TOA
>>> nodes['RN_TDOA']= RN_TDOA
>>> nodes['RNr_TDOA']= RNr_TDOA
>>> ldp={}
>>> ldp['RSS'] = rss_db
>>> ldp['RSS_std'] = rss_std
>>> ldp['RSS_np'] = rss_np
>>> ldp['d0'] = d0
>>> ldp['PL0'] = pl0
>>> ldp['TOA'] = TOA
>>> ldp['TOA_std'] = TOA_std
>>> ldp['TDOA'] = TDOA
>>> ldp['TDOA_std'] = TDOA_std
>>> print 'Nodes'
>>> print nodes
>>> print 'LDPs'
>>> print ldp
>>> print 'BN0:initial guess for ML estimator'
>>> print BN0
"""
nrn = 4
dim = 3 # 2 for 2D, 3 for 3D
L = 20.
c = 0.3
bn = L * sp.rand(dim, 1)
bn0 = L * sp.rand(dim, 1)
rn_toa = L * sp.rand(dim, nrn)
rn_rss = L * sp.rand(dim, nrn)
rn_tdoa = L * sp.rand(dim, nrn)
d_toa = dist(rn_toa, bn, 0) # actual distances
tof = d_toa / c # actual TOA
toa_std = 0.001 / c * np.ones(np.shape(tof))
toa_ns = tof + toa_std
rss_std = 0.001 * np.ones(nrn)
rss_np = 2.645 * np.ones(nrn)
pl0 = 34.7 * np.ones(nrn)
d0 = 1.
d_rss = dist(rn_rss, bn, 0) # actual distances
x = rss_std * np.random.randn(np.shape(pl0)[0])
rss_db = pl0 - 10 * rss_np * np.log10(d_rss / d0) + x
rnr_tdoa = np.zeros((dim, nrn)) # L*sp.rand(dim,nRN)
d = dist(rn_tdoa, bn, 0)
dr = dist(rnr_tdoa, bn, 0)
tdof = (d - dr) / c # actual TDOA
tdoa_std = 0.001 / c * np.ones(np.shape(tdof))
tdoa_ns = tdof + tdoa_std
nodes = {}
nodes['BN'] = bn
nodes['RN_RSS'] = rn_rss
nodes['RN_TOA'] = rn_toa
nodes['RN_TDOA'] = rn_tdoa
nodes['RNr_TDOA'] = rnr_tdoa
ldp = {}
ldp['RSS'] = rss_db
ldp['RSS_std'] = rss_std
ldp['RSS_np'] = rss_np
ldp['d0'] = d0
ldp['PL0'] = pl0
ldp['TOA'] = toa_ns
ldp['TOA_std'] = toa_std
ldp['TDOA'] = tdoa_ns
ldp['TDOA_std'] = tdoa_std
return nodes, ldp, bn0
def scenario():
""" Defines a sample scenario for testing
Returns
-------
CRB : float
Examples
--------
.. plot::
:include-source:
>>> nRN = 4
>>> dim = 3 # 2 for 2D, 3 for 3D
>>> L = 20.
>>> c = 0.3
>>> BN = L*sp.rand(dim,1)
>>> BN0 = L*sp.rand(dim,1)
>>> RN_TOA = L*sp.rand(dim,nRN)
>>> RN_RSS = L*sp.rand(dim,nRN)
>>> RN_TDOA = L*sp.rand(dim,nRN)
>>> d_TOA = dist(RN_TOA,BN,0) # actual distances
>>> TOF = d_TOA/c # actual TOA
>>> TOA_std = 0.001/c*np.ones(np.shape(TOF))
>>> TOA = TOF + TOA_std
>>> RSS_std = 0.001 * np.ones(nRN)
>>> RSS_np = 2.645 * np.ones(nRN)
>>> PL0 = 34.7*np.ones(nRN)
>>> d0 = 1.
>>> d_RSS = dist(RN_RSS,BN,0) # actual distances
>>> X = RSS_std * np.random.randn(np.shape(PL0)[0])
>>> RSS = PL0-10*RSS_np*np.log10(d_RSS/d0)+X
>>> RNr_TDOA = np.zeros((dim,nRN))#L*sp.rand(dim,nRN)
>>> d = dist(RN_TDOA,BN,0)
>>> dr = dist(RNr_TDOA,BN,0)
>>> TDOF = (d-dr)/c # actual TDOA
>>> TDOA_std = 0.001/c*np.ones(np.shape(TDOF))
>>> TDOA = TDOF + TDOA_std
>>> nodes={}
>>> nodes['BN']= BN
>>> nodes['RN_RSS']= RN_RSS
>>> nodes['RN_TOA']= RN_TOA
>>> nodes['RN_TDOA']= RN_TDOA
>>> nodes['RNr_TDOA']= RNr_TDOA
>>> ldp={}
>>> ldp['RSS'] = RSS
>>> ldp['RSS_std'] = RSS_std
>>> ldp['RSS_np'] = RSS_np
>>> ldp['d0'] = d0
>>> ldp['PL0'] = PL0
>>> ldp['TOA'] = TOA
>>> ldp['TOA_std'] = TOA_std
>>> ldp['TDOA'] = TDOA
>>> ldp['TDOA_std'] = TDOA_std
>>> print 'Nodes'
>>> print nodes
>>> print 'LDPs'
>>> print ldp
>>> print 'BN0:initial guess for ML estimator'
>>> print BN0
"""
nRN = 4
dim = 3 # 2 for 2D, 3 for 3D
L = 20.
c = 0.3
BN = L * sp.rand(dim, 1)
BN0 = L * sp.rand(dim, 1)
RN_TOA = L * sp.rand(dim, nRN)
RN_RSS = L * sp.rand(dim, nRN)
RN_TDOA = L * sp.rand(dim, nRN)
d_TOA = dist(RN_TOA, BN, 0) # actual distances
TOF = d_TOA / c # actual TOA
TOA_std = 0.001 / c * np.ones(np.shape(TOF))
TOA = TOF + TOA_std
RSS_std = 0.001 * np.ones(nRN)
RSS_np = 2.645 * np.ones(nRN)
PL0 = 34.7 * np.ones(nRN)
d0 = 1.
d_RSS = dist(RN_RSS, BN, 0) # actual distances
X = RSS_std * np.random.randn(np.shape(PL0)[0])
RSS = PL0 - 10 * RSS_np * np.log10(d_RSS / d0) + X
RNr_TDOA = np.zeros((dim, nRN)) # L*sp.rand(dim,nRN)
d = dist(RN_TDOA, BN, 0)
dr = dist(RNr_TDOA, BN, 0)
TDOF = (d - dr) / c # actual TDOA
TDOA_std = 0.001 / c * np.ones(np.shape(TDOF))
TDOA = TDOF + TDOA_std
nodes = {}
nodes['BN'] = BN
nodes['RN_RSS'] = RN_RSS
nodes['RN_TOA'] = RN_TOA
nodes['RN_TDOA'] = RN_TDOA
nodes['RNr_TDOA'] = RNr_TDOA
ldp = {}
ldp['RSS'] = RSS
ldp['RSS_std'] = RSS_std
ldp['RSS_np'] = RSS_np
ldp['d0'] = d0
ldp['PL0'] = PL0
ldp['TOA'] = TOA
ldp['TOA_std'] = TOA_std
ldp['TDOA'] = TDOA
ldp['TDOA_std'] = TDOA_std
return nodes, ldp, BN0
