def azimuthCompression(Srx, h):
"""
Method to apply matched filter h on azimuth dimension
"""
# pad received and emitted signals on common time stemp
# to obtain common fft
n1 = len(h)
n2 = Srx.shape[0]
Nsa = n1 + n2 - 1
## Nsa2 = int(np.power(2.0, np.ceil(np.log2( Nsa ))))
Nsa2 = Nsa if Nsa % 2 == 0 else Nsa+1
SrxMF=np.zeros((Nsa2, Srx.shape[1]),dtype=complex)
hMF = np.concatenate((np.zeros(np.floor((Nsa2-n1)*0.5)), h, np.zeros(np.ceil((Nsa2-n1)*0.5))))
HMF = GC.fft(hMF)
for i in range(Srx.shape[1]):
col = Srx[:,i]
colMF = np.concatenate((np.zeros(np.floor((Nsa2-len(col))*0.5)), col, np.zeros(np.ceil((Nsa2-len(col))*0.5))))
ColMF = GC.fft(colMF)
ColMF = ColMF * HMF
SrxMF[:,i] = GC.ifft(ColMF)
return SrxMF
def rangeCompression(Srx, h):
"""
Method to apply matched filter h on range dimension
"""
# pad received and emitted signals on common time stemp
# to obtain common fft
n1 = len(h)
n2 = Srx.shape[1]
Nsa = n1 + n2 - 1
## Nsa2 = int(np.power(2.0, np.ceil(np.log2( Nsa ))))
Nsa2 = Nsa if Nsa % 2 == 0 else Nsa+1
SrxMF=np.zeros((Srx.shape[0], Nsa2),dtype=complex)
hMF = np.concatenate((np.zeros(np.floor((Nsa2-n1)*0.5)), h, np.zeros(np.ceil((Nsa2-n1)*0.5))))
HMF = GC.fft(hMF)
for i in range(Srx.shape[0]):
row = Srx[i,:]
rowMF = np.concatenate((np.zeros(np.floor((Nsa2-len(row))*0.5)), row, np.zeros(np.ceil((Nsa2-len(row))*0.5))))
RowMF = GC.fft(rowMF)
RowMF = RowMF * HMF
SrxMF[i,:] = GC.ifft(RowMF)
return SrxMF
def rangeCompression(Srx, HMF):
"""
Range compression with freq-domain filter H(f)
"""
assert Srx.shape[1] == len(HMF), logPrint("Range dimension of the Srx is not equal to length of HMF")
SrxMF=np.zeros(Srx.shape,dtype=complex)
for i in range(Srx.shape[0]):
data = Srx[i,:]
DataMF = GC.fft(data) * HMF
SrxMF[i,:] = GC.ifft(DataMF)
return SrxMF
def rangeCompression(Srx, HMF):
"""
Range compression with freq-domain filter H(f)
"""
assert Srx.shape[1] == len(HMF), logPrint(
"Range dimension of the Srx is not equal to length of HMF")
SrxMF = np.zeros(Srx.shape, dtype=complex)
for i in range(Srx.shape[0]):
data = Srx[i, :]
DataMF = GC.fft(data) * HMF
SrxMF[i, :] = GC.ifft(DataMF)
return SrxMF
def generateRefPhase(fAzimuthArray,
fRangeArray,
f0,
Rref,
Vref,
K,
Tp,
tZero=0,
etaZero=0):
"""
Phi_ref(f_eta, f_tau) = 4*pi*Rref/c * sqrt( (f0+f_tau)^2 - c^2 * f_eta^2 / (4*Vref^2) )
- tZero corresponds to the 'zero' range time of the signal range time sampling. tZero = (tmax+tmin)/2
- etaZero corresponds to the 'zero' azimuth time of the signal azimuth time sampling. etaZero = (etaMax+etaMin)/2
"""
a = 4.0 * np.pi * Rref / GC.C
fRangeMax = np.abs(K) * Tp
## fRangeMax = fRangeArray[-1]
phase = np.zeros((len(fAzimuthArray), len(fRangeArray)), dtype=np.complex)
for i, f in enumerate(fAzimuthArray):
b = np.sqrt((f0 + fRangeArray)**2 - (0.5 * GC.C * f / Vref)**2)
c = np.pi * fRangeArray**2 / K
d = 2.0 * np.pi * tZero * fRangeArray
d2 = 2.0 * np.pi * etaZero * f
phase[i, :] = GC.rect(
fRangeArray / fRangeMax) * np.exp(1j * a * b[:] + 1j * c[:] -
1j * d[:] - 1j * d2)
return phase
def idealWa(x,x_c,deltaX):
"""
Antenna Beam pattern
ideal : Wa(x) = rect( (x - x_c)/deltaX ) where
deltaX - azimuth flight distance covered by satellite during elimination and reception
deltaX = deltaT * V_{sat}, V_{sat} - satellite velocity
"""
return GC.rect( (x-x_c) / deltaX )
def received2DSignal(t, x, dist, tau_p, f_c, K_r, Wa, sigma=1) :
"""
SAR received signal at range time t and azimuth position x
S_{out}(t,x) = rect( (t-2*d(x)/c) / tau_p) * Wa(x) * sigma
* exp( -4*pi*1j* f_c * d(x)/c + 1j*pi*K_r*(t-2*d(x)/c)^2 )
"""
tt = t - 2.0*dist(x) / GC.C
phase = -4.0*f_c*dist(x) / GC.C + K_r*(tt**2)
return GC.rect( tt/tau_p ) * Wa(x) * sigma * np.exp(1j*np.pi*phase)
def received2DSignal(t, x, dist, tau_p, f_c, K_r, Wa, sigma=1):
"""
SAR received signal at range time t and azimuth position x
S_{out}(t,x) = rect( (t-2*d(x)/c) / tau_p) * Wa(x) * sigma
* exp( -4*pi*1j* f_c * d(x)/c + 1j*pi*K_r*(t-2*d(x)/c)^2 )
"""
tt = t - 2.0 * dist(x) / GC.C
phase = -4.0 * f_c * dist(x) / GC.C + K_r * (tt**2)
return GC.rect(tt / tau_p) * Wa(x) * sigma * np.exp(1j * np.pi * phase)
def compression(Srx, h, dim0):
"""
Matched filter alogorith with filter 'h' is applied on
the image 'Srx' on the dimension 'dim'
dim is 'range' or 'azimuth'
"""
dim = 1 if dim0 is 'range' else 0 if dim0 is 'azimuth' else -1
assert dim >= 0, logPrint("Parameter dim should be 'range' or 'azimuth'")
# inverse of dim
idim = np.abs(dim-1)
# pad received and emitted signals on common time stemp
# to obtain common fft
n1 = len(h)
n2 = Srx.shape[dim]
Nsa = n1 + n2 - 1
## Nsa2 = int(np.power(2.0, np.ceil(np.log2( Nsa ))))
Nsa2 = Nsa if Nsa % 2 == 0 else Nsa+1
## shape = (Srx.shape[0], Nsa2) if dim == 1 else (Nsa2, Srx.shape[1])
## SrxMF=np.zeros(shape,dtype=complex)
SrxMF=np.zeros(Srx.shape,dtype=complex)
hMF = np.concatenate((np.zeros(np.floor((Nsa2-n1)*0.5)), h, np.zeros(np.ceil((Nsa2-n1)*0.5))))
HMF = GC.fft(hMF)
m1 = np.floor((Nsa2-n2)*0.5)
m2 = np.ceil((Nsa2-n2)*0.5)
for i in range(Srx.shape[idim]):
data = Srx[i,:] if dim == 1 else Srx[:,i]
dataMF = np.concatenate((np.zeros(m1), data, np.zeros(m2)))
DataMF = GC.fft(dataMF)
DataMF = DataMF * HMF
if dim == 1:
SrxMF[i,:] = GC.ifft(DataMF)[n1/2:n1/2+n2]
else:
SrxMF[:,i] = GC.ifft(DataMF)[n1/2:n1/2+n2]
return SrxMF
def compression(Srx, h, dim0):
"""
Matched filter alogorith with filter 'h' is applied on
the image 'Srx' on the dimension 'dim'
dim is 'range' or 'azimuth'
"""
dim = 1 if dim0 is 'range' else 0 if dim0 is 'azimuth' else -1
assert dim >= 0, logPrint("Parameter dim should be 'range' or 'azimuth'")
# inverse of dim
idim = np.abs(dim - 1)
# pad received and emitted signals on common time stemp
# to obtain common fft
n1 = len(h)
n2 = Srx.shape[dim]
Nsa = n1 + n2 - 1
## Nsa2 = int(np.power(2.0, np.ceil(np.log2( Nsa ))))
Nsa2 = Nsa if Nsa % 2 == 0 else Nsa + 1
## shape = (Srx.shape[0], Nsa2) if dim == 1 else (Nsa2, Srx.shape[1])
## SrxMF=np.zeros(shape,dtype=complex)
SrxMF = np.zeros(Srx.shape, dtype=complex)
hMF = np.concatenate((np.zeros(np.floor(
(Nsa2 - n1) * 0.5)), h, np.zeros(np.ceil((Nsa2 - n1) * 0.5))))
HMF = GC.fft(hMF)
m1 = np.floor((Nsa2 - n2) * 0.5)
m2 = np.ceil((Nsa2 - n2) * 0.5)
for i in range(Srx.shape[idim]):
data = Srx[i, :] if dim == 1 else Srx[:, i]
dataMF = np.concatenate((np.zeros(m1), data, np.zeros(m2)))
DataMF = GC.fft(dataMF)
DataMF = DataMF * HMF
if dim == 1:
SrxMF[i, :] = GC.ifft(DataMF)[n1 / 2:n1 / 2 + n2]
else:
SrxMF[:, i] = GC.ifft(DataMF)[n1 / 2:n1 / 2 + n2]
return SrxMF
def sarResp(t,y,d_a,E_y):
"""
The demodulated received data for a single point at slant-range and azimuth position of
r_a and y_a, respectively is given as
s_a(t,y) = E_t(t - 2*d_a(y)/c) * E_y(y - Y0) * exp( -4*pi*1j * f_c * d_a(y) / c + 1j * pi * K_r * (t - 2*d_a(y) / c)**2 )
E_t(t) = rect(t/T_p)
T_p - chirp duration time
E_y - antenna beam pattern
"""
tt = t - 2.0*d_a(y) / GC.C
return GC.rect(tt / T_p) * E_y(y - Y0) * np.exp(-4.0*1j*np.pi * TSX.Freq * d_a(y) / GC.C + 1j*np.pi * TSX.K * (tt)**2 )
def sarResp(t, y, d_a, E_y):
"""
The demodulated received data for a single point at slant-range and azimuth position of
r_a and y_a, respectively is given as
s_a(t,y) = E_t(t - 2*d_a(y)/c) * E_y(y - Y0) * exp( -4*pi*1j * f_c * d_a(y) / c + 1j * pi * K_r * (t - 2*d_a(y) / c)**2 )
E_t(t) = rect(t/T_p)
T_p - chirp duration time
E_y - antenna beam pattern
"""
tt = t - 2.0 * d_a(y) / GC.C
return GC.rect(tt / T_p) * E_y(y - Y0) * np.exp(
-4.0 * 1j * np.pi * TSX.Freq * d_a(y) / GC.C + 1j * np.pi * TSX.K *
(tt)**2)
def showEmittedSignal(tArray, Stx, signalName=""):
## Stx = emittedSignal(tArray,Sat.Tp, Sat.Freq, Sat.K)
StxFFT = GC.fft(Stx)
fArray = Sat.Freq + Sat.K * tArray
plt.subplot(221)
plt.title(signalName + " signal, Re")
plt.plot(tArray, np.real(Stx))
plt.subplot(222)
plt.title(signalName + " signal, Im")
plt.plot(tArray, np.imag(Stx))
plt.subplot(223)
plt.title(signalName + " signal FFT, module")
plt.plot(fArray, np.absolute(StxFFT))
plt.subplot(224)
plt.title(signalName + " signal FFT, phase")
plt.plot(fArray, np.sign(np.absolute(StxFFT)) * np.angle(StxFFT))
plt.show()
def generateRefPhase(fAzimuthArray, fRangeArray, f0, Rref, Vref, K, Tp, tZero=0, etaZero=0):
"""
Phi_ref(f_eta, f_tau) = 4*pi*Rref/c * sqrt( (f0+f_tau)^2 - c^2 * f_eta^2 / (4*Vref^2) )
- tZero corresponds to the 'zero' range time of the signal range time sampling. tZero = (tmax+tmin)/2
- etaZero corresponds to the 'zero' azimuth time of the signal azimuth time sampling. etaZero = (etaMax+etaMin)/2
"""
a = 4.0*np.pi*Rref/GC.C
fRangeMax = np.abs(K)*Tp
## fRangeMax = fRangeArray[-1]
phase = np.zeros((len(fAzimuthArray),len(fRangeArray)), dtype=np.complex)
for i, f in enumerate(fAzimuthArray):
b = np.sqrt( (f0 + fRangeArray)**2 - ( 0.5 * GC.C * f / Vref)**2 )
c = np.pi * fRangeArray**2 / K
d = 2.0*np.pi* tZero * fRangeArray
d2 = 2.0*np.pi* etaZero * f
phase[i,:] = GC.rect(fRangeArray / fRangeMax) * np.exp(1j * a * b[:] + 1j*c[:] - 1j*d[:] - 1j*d2)
return phase
def chirpSignal(t,tau_p,K_r):
"""
Emitted signal :
S_{tx}(t) = rect(t/tau_p) * exp(1j*pi*K_r*t^2)
"""
return GC.rect(t / tau_p) * np.exp(1j*np.pi*K_r * t**2)
h=chirpSignal(tArray0, Sat.Tp, -Sat.K)
Srx_RC = rangeCompression(Srx,h)
## Nsa = SrxMF.shape[1]
## tmin = tArrayR[0] - len(h)*0.5*dt
## tmax = tmin + dt*Nsa
## tArrayMF = np.arange(tmin,tmax,dt)
## tArrayMF = np.resize(tArrayMF,Nsa)
showResponse(Srx_RC)
# 4) Azimuth FFT
SrxAF_RC = np.zeros(Srx_RC.shape, dtype=complex)
for i in range(SrxAF_RC.shape[1]):
# get range fixed column
SrxAF_RC[:,i] = GC.fft(Srx_RC[:,i])
showResponse(SrxAF_RC)
exit()
# 5) Range cell migration Compensation (RCMC)
# from range time to range distance :
dist = lambda x : distSST(x, Sat.H, x_c, y_c)
dR = deltaR(xArray, x_c, dist)
dRI = np.round(dR).astype(np.int)
Srx_RC_RCMC = rangeMigration(Srx_RC, dRI)
# 6) Azimuth compression :
def chirp(t):
"""
SAR emitting pulse
"""
return GC.rect(t / T_p) * np.exp(1j * np.pi *
(2.0 * TSX.Freq * t + TSX.K * t**2))
def compressRawData(self, showProgress=True):
"""
Apply RDA on raw data
1) Range compression
2) Range Cell Migration Correction
3) Azimuth compression
Returns (compressed 2D array, azimuth positions, range time)
"""
rawData = self._rawData
assert rawData is not None, logPrint(
"Raw data should be computed ( using computeRawData() ) or provided as argument"
)
assert self._dt is not None, logPrint(
"Error : Simulator badly configured, dt is None")
assert self._dx is not None, logPrint(
"Error : Simulator badly configured, dx is None")
assert self._xArray is not None, logPrint(
"Error : Simulator badly configured, xArray is None")
assert self._tArray is not None, logPrint(
"Error : Simulator badly configured, tArray is None")
Tp = self._platform.Tp
Vsat = self._platform.Vsat
K = self._platform.K
PRF = self._platform.PRF
H = self._platform.H
Lambda = self._platform.Lambda
# 1) Range compression :
if showProgress:
logPrint(" - Range compression ...")
fRangeArray = GC.freq(rawData.shape[1], 1.0 / self._dt)
HMF = GC.rect(fRangeArray / (np.abs(K) * Tp)) * np.exp(
1j * np.pi * fRangeArray**2 / K)
# Multiply by a phase due to different range zero time
## tZero = self._tArray[len(self._tArray)/2]
## HMF = HMF * np.exp( -1j* 2.0 * np.pi * fRangeArray * tZero)
procData = rangeCompression(rawData, HMF)
HMF = None
if self.verbose:
showResponse(procData, None, 'Range compression')
# 2) Azimuth FFT :
if showProgress:
logPrint(" -- Azimuth FFT ...")
procDataAF = np.zeros(procData.shape, dtype=complex)
for i in range(procDataAF.shape[1]):
# get range fixed column
procDataAF[:, i] = GC.fft(procData[:, i])
procData = procDataAF
procDataAF = None
fArray = GC.freq(procData.shape[0], self._platform.Vsat / self._dx)
if self.verbose:
## showResponse(procData, [self._tArray[0], self._tArray[-1], fArray[0], fArray[-1]], 'Azimuth FFT')
showResponse(procData, None, 'Azimuth FFT')
# 3) Range migration :
if showProgress:
logPrint(" --- Range migration ...")
Dfreq = np.sqrt(1.0 - (0.5 * Lambda * fArray / Vsat)**2)
procData = rangeMigration2(procData, self._tArray, Dfreq)
if self.verbose:
## showResponse(procData, [self._tArray[0], self._tArray[-1], fArray[0], fArray[-1]], 'RCMC')
showResponse(procData, None, 'RCMC')
# 4) Azimuth compression :
if showProgress:
logPrint(" ---- Azimuth compression ...")
etaZero = self._xArray[len(self._xArray) / 2] / Vsat
for i in range(procData.shape[1]):
# Use H(f) = exp( 1j * 4 * pi * R0 * D(f) * f0 / c )
#
HMF = np.exp(2 * np.pi * 1j * GC.C * self._tArray[i] / Lambda *
Dfreq)
# Multiply by a phase due to different azimuth zero time
HMF = HMF * np.exp(-1j * 2.0 * np.pi * fArray * etaZero)
procData[:, i] = procData[:, i] * HMF
# 5) Azimuth IFFT
if showProgress:
logPrint(" ----- Azimuth IFFT ...")
procDataAF = np.zeros(procData.shape, dtype=complex)
for i in range(procDataAF.shape[1]):
# get range fixed column
procDataAF[:, i] = GC.ifft(procData[:, i])
procData = procDataAF
procDataAF = None
if self.verbose:
showResponse(
procData,
[self._tArray[0], self._tArray[-1], fArray[0], fArray[-1]],
'Compressed image', False)
return procData, self._xArray, self._tArray
def chirp(t):
"""
SAR emitting pulse
"""
return GC.rect(t / T_p) * np.exp(1j*np.pi*( 2.0* TSX.Freq * t + TSX.K * t**2 ) )
## plt.title("Chirp module")
## plt.plot(tArray,np.absolute(emitSig))
## plt.subplot(122)
## plt.title("Chirp phase")
## plt.plot(tArray,np.angle(emitSig))
## plt.show()
# Radar-Target distance plot
## dArray = d_a_0(yArray)
## plt.plot(yArray,dArray,'-b')
## plt.show()
# SAR raw data
respSig = np.zeros((len(tArray), len(yArray)), dtype=complex)
E_y = lambda y : GC.rect(y/Y_d)
for index, t in enumerate(tArray):
respSig[index,:] = sarResp(t, yArray[:],d_a_0,E_y)
plt.subplot(121)
plt.imshow(np.absolute(respSig))
plt.colorbar()
plt.subplot(122)
plt.imshow(np.angle(respSig))
plt.colorbar()
plt.show()
import Sensors.GlobalVariablesCSK_HI as Sensor
# Test
xc = 0.0
yc = Sensor.H * np.tan(55.0 * np.pi / 180.0)
# Absolute reference distance
Rref = np.sqrt(Sensor.H**2 + yc**2)
# Relative reference distance to Rmin
ymin = Sensor.H * np.tan(54.995 * np.pi / 180.0)
Rref2 = Rref - np.sqrt(Sensor.H**2 + ymin**2)
Vref = Sensor.Vsat
dt = 1.0/(1.4*np.abs(Sensor.K) * Sensor.Tp)
dx = Vref/Sensor.PRF
fRangeArray = GC.freq(2*1024, 1.0/dt)
fAzimuthArray = GC.freq(2*1024, Vref/dx)
phaseRef = generateRefPhase(fAzimuthArray, fRangeArray, Sensor.Freq, Rref2, Vref, Sensor.K, Sensor.Tp)
Sref = GC.ifft2(phaseRef)
showResponse(Sref)
exit()
## filename = "C:\\VFomin_folder\\PISE_project\\SAR-GMTI\\Data\\CSK_RAW_0340_B001_2689_15971_3028_3027.npy"
## pt = [2689,15971]
## filename = "C:\\VFomin_folder\\PISE_project\\SAR-GMTI\\Data\\CSK_RAW_0340_B001_10255_8742_3195_3195.npy"
## pt = [10255,8742]
## win = [1842, 1500, 150, data.shape[0]-1500]
## win = [1742, 0, 250, data.shape[0]]
## index = 134
## data = data[win[1]:win[1]+win[3], win[0]:win[0]+win[2]]
## yArray = yArray[win[0]:win[0]+win[2]]
SARSim.showResponse(data, None, 'Compressed image - Ground range')
# Decompress image at azimuth
dataAF = azimuthFFT(data)
fArray = GC.freq(dataAF.shape[0], Vsat/dx)
dataAF = azimuthTransform(dataAF, 'decompress', fArray, yArray, Lambda, Vsat)
# Compress at different velocities
vset = np.arange(0.0, 10.0, .2)
stack3D = np.zeros((data.shape[0], data.shape[1], len(vset)), dtype=np.float32)
for i, v in enumerate(vset):
print "Compress at velocity:", v
dataAF2 = azimuthTransform(dataAF, 'compress', fArray, yArray, Lambda, Vsat, v)
outData = azimuthIFFT(dataAF2)
stack3D[:,:,i] = np.abs(outData[:,:])
# User interaction :
data0 = stack3D[:,:,0]
fig, ax = plt.subplots(1,2)
def compressRawData(self, showProgress=True):
"""
Apply RDA on raw data
1) Range compression
2) Range Cell Migration Correction
3) Azimuth compression
Returns (compressed 2D array, azimuth positions, range time)
"""
rawData = self._rawData
assert rawData is not None, logPrint(
"Raw data should be computed ( using computeRawData() ) or provided as argument"
)
assert self._dt is not None, logPrint(
"Error : Simulator badly configured, dt is None")
assert self._dx is not None, logPrint(
"Error : Simulator badly configured, dx is None")
assert self._xArray is not None, logPrint(
"Error : Simulator badly configured, xArray is None")
assert self._xSwath is not None, logPrint(
"Error : Simulator badly configured, xSwath is None")
assert self._tArray is not None, logPrint(
"Error : Simulator badly configured, tArray is None")
Tp = self._platform.Tp
Vsat = self._platform.Vsat
K = self._platform.K
PRF = self._platform.PRF
H = self._platform.H
Lambda = self._platform.Lambda
# 1) Range compression :
if showProgress:
logPrint(" - Range compression ...")
## # !!! VERY VERY SLOW !!!
## tArray0=np.arange(-Tp*0.5,Tp*0.5,self._dt)
## h = GC.chirpSignal(tArray0, Tp, -K)
## procData = compression(rawData, h, 'range')
fRangeArray = GC.freq(rawData.shape[1], 1.0 / self._dt)
HMF = GC.rect(fRangeArray / (np.abs(K) * Tp)) * np.exp(
1j * np.pi * fRangeArray**2 / K)
procData = rangeCompression(rawData, HMF)
HMF = None
if self.verbose:
showResponse(procData, None, 'Range compression')
## # TEMP
## yc = self._params[2][1]
## yArray = np.sqrt( (0.5 * GC.C * self._tArray)**2 - H**2 )
## indices = [1050]
## for index in indices:
## f = plt.figure()
## f.suptitle("Range compression")
## plt.subplot(211)
## plt.plot(yArray-yc, np.abs(rawData[index,:]))
## plt.subplot(212)
## plt.plot(yArray-yc, np.abs(procData[index,:]))
## plt.show()
## # TEMP
# 2) Azimuth FFT :
if showProgress:
logPrint(" -- Azimuth FFT ...")
procDataAF = np.zeros(procData.shape, dtype=complex)
for i in range(procDataAF.shape[1]):
# get range fixed column
procDataAF[:, i] = GC.fft(procData[:, i])
procData = procDataAF
procDataAF = None
## fArray = np.fft.fftshift(np.fft.fftfreq( procData.shape[0], self._dx / self._platform.Vsat))
fArray = GC.freq(procData.shape[0], self._platform.Vsat / self._dx)
if self.verbose:
## showResponse(procData, [self._tArray[0], self._tArray[-1], fArray[0], fArray[-1]], 'Azimuth FFT')
showResponse(procData, None, 'Azimuth FFT')
# 3) Range migration :
if showProgress:
logPrint(" --- Range migration ...")
Dfreq = np.sqrt(1.0 - (0.5 * Lambda * fArray / Vsat)**2)
procData = rangeMigration2(procData, self._tArray, Dfreq)
if self.verbose:
## showResponse(procData, [self._tArray[0], self._tArray[-1], fArray[0], fArray[-1]], 'RCMC')
showResponse(procData, None, 'RCMC')
# 4) Azimuth compression :
if showProgress:
logPrint(" ---- Azimuth compression ...")
## xc = self._params[2][0]
## yc = self._params[2][1]
## Rc = np.sqrt(H**2 + yc**2)
## Ka = 2.0 / Lambda * 1.0 / Rc
## h = GC.rect((self._xArray-xc)/self._xSwath) * np.exp(1j * np.pi * Ka * (self._xArray - xc)**2)
## H = GC.fft(h)
xc = self._xArray[len(self._xArray) / 2]
for i in range(procData.shape[1]):
# Small squint approximation
## Ka = 4.0 * Vsat**2 / Lambda / GC.C / self._tArray[i]
## hMF = GC.rect((self._xArray-xc)/self._xSwath) * np.exp(1j * np.pi * Ka/Vsat**2 * (self._xArray - xc)**2)
## HMF = GC.fft(hMF)
# Use H(f) = exp( 1j * 4 * pi * R0 * D(f) * f0 / c )
HMF = np.exp(2 * np.pi * 1j * GC.C * self._tArray[i] / Lambda *
Dfreq)
procData[:, i] = procData[:, i] * HMF
# 5) Azimuth IFFT
if showProgress:
logPrint(" ----- Azimuth IFFT ...")
procDataAF = np.zeros(procData.shape, dtype=complex)
for i in range(procDataAF.shape[1]):
# get range fixed column
procDataAF[:, i] = GC.ifft(procData[:, i])
procData = procDataAF
procDataAF = None
if self.verbose:
showResponse(
procData,
[self._tArray[0], self._tArray[-1], fArray[0], fArray[-1]],
'Compressed image', False)
return procData, self._xArray, self._tArray
import Sensors.GlobalVariablesCSK_HI as Sensor
# Test
xc = 0.0
yc = Sensor.H * np.tan(55.0 * np.pi / 180.0)
# Absolute reference distance
Rref = np.sqrt(Sensor.H**2 + yc**2)
# Relative reference distance to Rmin
ymin = Sensor.H * np.tan(54.995 * np.pi / 180.0)
Rref2 = Rref - np.sqrt(Sensor.H**2 + ymin**2)
Vref = Sensor.Vsat
dt = 1.0 / (1.4 * np.abs(Sensor.K) * Sensor.Tp)
dx = Vref / Sensor.PRF
fRangeArray = GC.freq(2 * 1024, 1.0 / dt)
fAzimuthArray = GC.freq(2 * 1024, Vref / dx)
phaseRef = generateRefPhase(fAzimuthArray, fRangeArray, Sensor.Freq, Rref2,
Vref, Sensor.K, Sensor.Tp)
Sref = GC.ifft2(phaseRef)
showResponse(Sref)
exit()
## filename = "C:\\VFomin_folder\\PISE_project\\SAR-GMTI\\Data\\CSK_RAW_0340_B001_2689_15971_3028_3027.npy"
## pt = [2689,15971]
## filename = "C:\\VFomin_folder\\PISE_project\\SAR-GMTI\\Data\\CSK_RAW_0340_B001_10255_8742_3195_3195.npy"
## pt = [10255,8742]
def compressRawData(self, showProgress=True):
"""
Apply RDA on raw data
1) Range compression
2) Range Cell Migration Correction
3) Azimuth compression
Returns (compressed 2D array, azimuth positions, range time)
"""
rawData = self._rawData
assert rawData is not None, logPrint("Raw data should be computed ( using computeRawData() ) or provided as argument")
assert self._dt is not None, logPrint("Error : Simulator badly configured, dt is None")
assert self._dx is not None, logPrint("Error : Simulator badly configured, dx is None")
assert self._xArray is not None, logPrint("Error : Simulator badly configured, xArray is None")
assert self._tArray is not None, logPrint("Error : Simulator badly configured, tArray is None")
Tp = self._platform.Tp
Vsat = self._platform.Vsat
K = self._platform.K
PRF = self._platform.PRF
H = self._platform.H
Lambda = self._platform.Lambda
# 1) Range compression :
if showProgress:
logPrint(" - Range compression ...")
fRangeArray = GC.freq(rawData.shape[1], 1.0/self._dt)
HMF = GC.rect(fRangeArray / (np.abs(K)*Tp)) * np.exp(1j*np.pi*fRangeArray**2 / K)
# Multiply by a phase due to different range zero time
## tZero = self._tArray[len(self._tArray)/2]
## HMF = HMF * np.exp( -1j* 2.0 * np.pi * fRangeArray * tZero)
procData = rangeCompression(rawData, HMF)
HMF = None
if self.verbose:
showResponse(procData, None, 'Range compression')
# 2) Azimuth FFT :
if showProgress:
logPrint(" -- Azimuth FFT ...")
procDataAF = np.zeros(procData.shape, dtype=complex)
for i in range(procDataAF.shape[1]):
# get range fixed column
procDataAF[:,i] = GC.fft(procData[:,i])
procData = procDataAF
procDataAF = None
fArray = GC.freq(procData.shape[0], self._platform.Vsat/self._dx)
if self.verbose:
## showResponse(procData, [self._tArray[0], self._tArray[-1], fArray[0], fArray[-1]], 'Azimuth FFT')
showResponse(procData, None, 'Azimuth FFT')
# 3) Range migration :
if showProgress:
logPrint(" --- Range migration ...")
Dfreq = np.sqrt(1.0 - (0.5 * Lambda * fArray / Vsat)**2)
procData = rangeMigration2(procData, self._tArray, Dfreq)
if self.verbose:
## showResponse(procData, [self._tArray[0], self._tArray[-1], fArray[0], fArray[-1]], 'RCMC')
showResponse(procData, None, 'RCMC')
# 4) Azimuth compression :
if showProgress:
logPrint(" ---- Azimuth compression ...")
etaZero = self._xArray[len(self._xArray)/2] / Vsat
for i in range(procData.shape[1]):
# Use H(f) = exp( 1j * 4 * pi * R0 * D(f) * f0 / c )
#
HMF = np.exp( 2*np.pi*1j * GC.C * self._tArray[i] / Lambda * Dfreq)
# Multiply by a phase due to different azimuth zero time
HMF = HMF * np.exp( -1j* 2.0 * np.pi * fArray * etaZero)
procData[:,i] = procData[:,i] * HMF
# 5) Azimuth IFFT
if showProgress:
logPrint(" ----- Azimuth IFFT ...")
procDataAF = np.zeros(procData.shape, dtype=complex)
for i in range(procDataAF.shape[1]):
# get range fixed column
procDataAF[:,i] = GC.ifft(procData[:,i])
procData = procDataAF
procDataAF = None
if self.verbose:
showResponse(procData, [self._tArray[0], self._tArray[-1], fArray[0], fArray[-1]], 'Compressed image', False)
return procData, self._xArray, self._tArray
def compressRawData2(self, showProgress=True):
"""
Omega-K compressing algorithm
1) FFT 2D
2) Reference Function Multiplication
3) Stolt interpolation
4) IFFT 2D
Returns (compressed 2D array, azimuth positions, range time)
"""
rawData = self._rawData
assert rawData is not None, logPrint("Raw data should be computed ( using computeRawData() ) or provided as argument")
assert self._dt is not None, logPrint("Error : Simulator badly configured, dt is None")
assert self._dx is not None, logPrint("Error : Simulator badly configured, dx is None")
assert self._xArray is not None, logPrint("Error : Simulator badly configured, xArray is None")
assert self._tArray is not None, logPrint("Error : Simulator badly configured, tArray is None")
Tp = self._platform.Tp
Vsat = self._platform.Vsat
K = self._platform.K
PRF = self._platform.PRF
H = self._platform.H
Lambda = self._platform.Lambda
f0 = self._platform.Freq
# 1) 2D freq domain
if showProgress:
logPrint(" - 2D FFT ...")
fRangeArray = GC.freq(rawData.shape[1], 1.0/self._dt)
fAzimuthArray = GC.freq(rawData.shape[0], Vsat/self._dx)
procData = rawData
procData = GC.fft2(procData)
if self.verbose:
showResponse(procData, None, '2D FFT')
# 2) RFM
if showProgress:
logPrint(" -- RMF ...")
# Reference distance
tZero = self._tArray[len(self._tArray)/2]
etaZero = self._xArray[len(self._xArray)/2] / Vsat
Rref = 0.5 * GC.C * tZero
Vref = Vsat
phaseRef = generateRefPhase(fAzimuthArray, fRangeArray, f0, Rref, Vref, K, Tp, tZero, etaZero)
procData = procData * phaseRef
if self.verbose:
## showReImAbsPhaseResponse(phaseRef, None, 'Reference Phase')
showResponse(procData, None, 'RFM')
# 3) Stolt mapping
if showProgress:
logPrint(" --- Stolt mapping ...")
# For azimuth line :
for i, f in enumerate(fAzimuthArray):
#
# Here we need analytical inverse of Stolt mapping :
# f' + f0 = sqrt( (f0 + f)^2 - (c * fEta / (2*Vsat))^2 )
#
# f + f0 = + sqrt( (f0 + f')^2 + (c * fEta / (2*Vsat))^2 )
#
nfRangeArray = -f0 + np.sqrt( (f0 + fRangeArray)**2 + ( 0.5 * GC.C * f / Vsat)**2 )
yRe = np.real(procData[i,:])
yIm = np.imag(procData[i,:])
yRe = np.interp(nfRangeArray, fRangeArray, yRe)
yIm = np.interp(nfRangeArray, fRangeArray, yIm)
procData[i,:] = yRe + 1j * yIm
if self.verbose:
showReImAbsPhaseResponse(procData, None, 'Stolt mapping')
# 4) 2D IFFT
if showProgress:
logPrint(" ---- 2D IFFT ...")
procData = GC.ifft2(procData)
if self.verbose:
showResponse(procData, None, '2D IFFT')
return procData, self._xArray, self._tArray
tArray = np.arange(-T_p * 0.6, T_p * 0.8, T_p * 0.001)
# Chirp signal
## emitSig=chirp(tArray)
## plt.subplot(121)
## plt.title("Chirp module")
## plt.plot(tArray,np.absolute(emitSig))
## plt.subplot(122)
## plt.title("Chirp phase")
## plt.plot(tArray,np.angle(emitSig))
## plt.show()
# Radar-Target distance plot
## dArray = d_a_0(yArray)
## plt.plot(yArray,dArray,'-b')
## plt.show()
# SAR raw data
respSig = np.zeros((len(tArray), len(yArray)), dtype=complex)
E_y = lambda y: GC.rect(y / Y_d)
for index, t in enumerate(tArray):
respSig[index, :] = sarResp(t, yArray[:], d_a_0, E_y)
plt.subplot(121)
plt.imshow(np.absolute(respSig))
plt.colorbar()
plt.subplot(122)
plt.imshow(np.angle(respSig))
plt.colorbar()
plt.show()
def emittedSignal(t,tau_p, f_c, K_r):
"""
Emitted signal :
S_{tx}(t) = rect(t/tau_p) * exp(2*pi*1j* f_c * t + 1j*pi*K_r*t^2)
"""
return GC.rect(t / tau_p) * np.exp(1j*np.pi*( 2.0* f_c * t + K_r * t**2 ) )
def compressRawData2(self, showProgress=True):
"""
Omega-K compressing algorithm
1) FFT 2D
2) Reference Function Multiplication
3) Stolt interpolation
4) IFFT 2D
Returns (compressed 2D array, azimuth positions, range time)
"""
rawData = self._rawData
assert rawData is not None, logPrint(
"Raw data should be computed ( using computeRawData() ) or provided as argument"
)
assert self._dt is not None, logPrint(
"Error : Simulator badly configured, dt is None")
assert self._dx is not None, logPrint(
"Error : Simulator badly configured, dx is None")
assert self._xArray is not None, logPrint(
"Error : Simulator badly configured, xArray is None")
assert self._tArray is not None, logPrint(
"Error : Simulator badly configured, tArray is None")
Tp = self._platform.Tp
Vsat = self._platform.Vsat
K = self._platform.K
PRF = self._platform.PRF
H = self._platform.H
Lambda = self._platform.Lambda
f0 = self._platform.Freq
# 1) 2D freq domain
if showProgress:
logPrint(" - 2D FFT ...")
fRangeArray = GC.freq(rawData.shape[1], 1.0 / self._dt)
fAzimuthArray = GC.freq(rawData.shape[0], Vsat / self._dx)
procData = rawData
procData = GC.fft2(procData)
if self.verbose:
showResponse(procData, None, '2D FFT')
# 2) RFM
if showProgress:
logPrint(" -- RMF ...")
# Reference distance
tZero = self._tArray[len(self._tArray) / 2]
etaZero = self._xArray[len(self._xArray) / 2] / Vsat
Rref = 0.5 * GC.C * tZero
Vref = Vsat
phaseRef = generateRefPhase(fAzimuthArray, fRangeArray, f0, Rref, Vref,
K, Tp, tZero, etaZero)
procData = procData * phaseRef
if self.verbose:
## showReImAbsPhaseResponse(phaseRef, None, 'Reference Phase')
showResponse(procData, None, 'RFM')
# 3) Stolt mapping
if showProgress:
logPrint(" --- Stolt mapping ...")
# For azimuth line :
for i, f in enumerate(fAzimuthArray):
#
# Here we need analytical inverse of Stolt mapping :
# f' + f0 = sqrt( (f0 + f)^2 - (c * fEta / (2*Vsat))^2 )
#
# f + f0 = + sqrt( (f0 + f')^2 + (c * fEta / (2*Vsat))^2 )
#
nfRangeArray = -f0 + np.sqrt((f0 + fRangeArray)**2 +
(0.5 * GC.C * f / Vsat)**2)
yRe = np.real(procData[i, :])
yIm = np.imag(procData[i, :])
yRe = np.interp(nfRangeArray, fRangeArray, yRe)
yIm = np.interp(nfRangeArray, fRangeArray, yIm)
procData[i, :] = yRe + 1j * yIm
if self.verbose:
showReImAbsPhaseResponse(procData, None, 'Stolt mapping')
# 4) 2D IFFT
if showProgress:
logPrint(" ---- 2D IFFT ...")
procData = GC.ifft2(procData)
if self.verbose:
showResponse(procData, None, '2D IFFT')
return procData, self._xArray, self._tArray
def azimuthIFFT(dataAF):
procData = np.zeros(dataAF.shape, dtype=complex)
for i in range(procData.shape[1]):
# get range fixed column
procData[:,i] = GC.ifft(dataAF[:,i])
return procData
def compressRawData(self, showProgress=True):
"""
Apply RDA on raw data
1) Range compression
2) Range Cell Migration Correction
3) Azimuth compression
Returns (compressed 2D array, azimuth positions, range time)
"""
rawData = self._rawData
assert rawData is not None, logPrint("Raw data should be computed ( using computeRawData() ) or provided as argument")
assert self._dt is not None, logPrint("Error : Simulator badly configured, dt is None")
assert self._dx is not None, logPrint("Error : Simulator badly configured, dx is None")
assert self._xArray is not None, logPrint("Error : Simulator badly configured, xArray is None")
assert self._xSwath is not None, logPrint("Error : Simulator badly configured, xSwath is None")
assert self._tArray is not None, logPrint("Error : Simulator badly configured, tArray is None")
Tp = self._platform.Tp
Vsat = self._platform.Vsat
K = self._platform.K
PRF = self._platform.PRF
H = self._platform.H
Lambda = self._platform.Lambda
# 1) Range compression :
if showProgress:
logPrint(" - Range compression ...")
## # !!! VERY VERY SLOW !!!
## tArray0=np.arange(-Tp*0.5,Tp*0.5,self._dt)
## h = GC.chirpSignal(tArray0, Tp, -K)
## procData = compression(rawData, h, 'range')
fRangeArray = GC.freq(rawData.shape[1], 1.0/self._dt)
HMF = GC.rect(fRangeArray / (np.abs(K)*Tp)) * np.exp(1j*np.pi*fRangeArray**2 / K)
procData = rangeCompression(rawData, HMF)
HMF = None
if self.verbose:
showResponse(procData, None, 'Range compression')
## # TEMP
## yc = self._params[2][1]
## yArray = np.sqrt( (0.5 * GC.C * self._tArray)**2 - H**2 )
## indices = [1050]
## for index in indices:
## f = plt.figure()
## f.suptitle("Range compression")
## plt.subplot(211)
## plt.plot(yArray-yc, np.abs(rawData[index,:]))
## plt.subplot(212)
## plt.plot(yArray-yc, np.abs(procData[index,:]))
## plt.show()
## # TEMP
# 2) Azimuth FFT :
if showProgress:
logPrint(" -- Azimuth FFT ...")
procDataAF = np.zeros(procData.shape, dtype=complex)
for i in range(procDataAF.shape[1]):
# get range fixed column
procDataAF[:,i] = GC.fft(procData[:,i])
procData = procDataAF
procDataAF = None
## fArray = np.fft.fftshift(np.fft.fftfreq( procData.shape[0], self._dx / self._platform.Vsat))
fArray = GC.freq(procData.shape[0], self._platform.Vsat/self._dx)
if self.verbose:
## showResponse(procData, [self._tArray[0], self._tArray[-1], fArray[0], fArray[-1]], 'Azimuth FFT')
showResponse(procData, None, 'Azimuth FFT')
# 3) Range migration :
if showProgress:
logPrint(" --- Range migration ...")
Dfreq = np.sqrt(1.0 - (0.5 * Lambda * fArray / Vsat)**2)
procData = rangeMigration2(procData, self._tArray, Dfreq)
if self.verbose:
## showResponse(procData, [self._tArray[0], self._tArray[-1], fArray[0], fArray[-1]], 'RCMC')
showResponse(procData, None, 'RCMC')
# 4) Azimuth compression :
if showProgress:
logPrint(" ---- Azimuth compression ...")
## xc = self._params[2][0]
## yc = self._params[2][1]
## Rc = np.sqrt(H**2 + yc**2)
## Ka = 2.0 / Lambda * 1.0 / Rc
## h = GC.rect((self._xArray-xc)/self._xSwath) * np.exp(1j * np.pi * Ka * (self._xArray - xc)**2)
## H = GC.fft(h)
xc = self._xArray[len(self._xArray)/2]
for i in range(procData.shape[1]):
# Small squint approximation
## Ka = 4.0 * Vsat**2 / Lambda / GC.C / self._tArray[i]
## hMF = GC.rect((self._xArray-xc)/self._xSwath) * np.exp(1j * np.pi * Ka/Vsat**2 * (self._xArray - xc)**2)
## HMF = GC.fft(hMF)
# Use H(f) = exp( 1j * 4 * pi * R0 * D(f) * f0 / c )
HMF = np.exp( 2*np.pi*1j * GC.C * self._tArray[i] / Lambda * Dfreq)
procData[:,i] = procData[:,i] * HMF
# 5) Azimuth IFFT
if showProgress:
logPrint(" ----- Azimuth IFFT ...")
procDataAF = np.zeros(procData.shape, dtype=complex)
for i in range(procDataAF.shape[1]):
# get range fixed column
procDataAF[:,i] = GC.ifft(procData[:,i])
procData = procDataAF
procDataAF = None
if self.verbose:
showResponse(procData, [self._tArray[0], self._tArray[-1], fArray[0], fArray[-1]], 'Compressed image', False)
return procData, self._xArray, self._tArray
