# Experiences Gained during the Development of a Passive  BSAR with GNSS Transmitters of Opportunity

Authors: M. Cherniakov; Rajesh Saini; Michael Antoniou; Rui Zuo; Eleftherios Plakidis
Journal: International Journal of Navigation and Observation
 (2008)

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## Abstract

This paper presents an overview of the research conducted at University of Birmingham. It highlights and briefly discusses various systems parameters (e.g., resolution, power budget), problems (e.g., interference, heterodyne channel Doppler compensation), and signal processing algorithms (imaging, synchronization) required for successfully obtaining an image. The GLONASS satellite is used for experiential confirmation of the main results. All these results are presented and briefly discussed.


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## Body

## 1. Introduction

Synthetic aperture radar (SAR) works
by collecting the echo returns from many pulses along the flight path and
processing them into a single high-resolution radar image. In
a monostatic SAR,
the transmitter and receiver are on the same platform, whereas in a bistatic
SAR (BSAR) the transmitter and receiver are separated by a distance that is
comparable to the expected target distance. Examples of BSAR include airborne
systems, where the transmitter and receiver are located on different aircrafts
[1, 2]. In a space-borne system, the transmitter and receiver are based on
two or more satellites [3, 4]. There is another subclass of bisatic SAR known
as space-surface bistatic synthetic aperture radar (SS-BSAR). The SS-BSAR
consists of a spaceborne transmitter and a receiver mounted on or near the
earth’s surface, see Figure 1. The receiver could be airborne, mounted on a
ground vehicle, onboard a ship, or even in a stationary position on the
surface. For the stationary receiver, a nongeostationary satellite should be
used to provide aperture synthesis.Figure 1
SS-BSAR concept for imaging.The core of SS-BSAR systems is their
asymmetric topology [5]. This is in contrast to a more usual bistatic SAR
configuration, where the transmitter and the receiver are moving along
collinear trajectories.
The basic operation of SS-BSAR systems is much the same as the operation
of other BSAR systems, the differences being introduced mainly as a consequence
of the geometry employed.University of Birmingham  (UoB) has been carrying out
active research in the area of SS-BSAR since 2003. The main aim of this
research is to experimentally demonstrate the feasibility and performance of
airborne SS-BSAR, utilising Global Navigation Satellite System (GNSS) as the
transmitter of opportunity.The goal
of this paper is to give the reader an overview of the research conducted at
the UoB. It highlights and briefly discusses various systems parameters (for resolution,
power budget), problems (e.g., interference, motion compensation), and signal
processing algorithms (imaging, synchronisation) required for successfully
obtaining an image. The GLONASS satellite is used for experiential confirmation
of the main results. All these results are presented and briefly discussed.It should be noted that although we are using a
particular satellite system, the structure and main parameters of the radar
system are generic and could be used with different GNSS transmitters.


## 2. Resolution

The GLONASS
satellite was selected as the transmitter of opportunity for experimentation,
despite its rather poor potential slant range resolution (30 m). This was due to
the fact that it was  the only one available at the time of experimentation.
Another GNSS system (GPS) has a navigation signal with twice the spectral
width, and hence 15 m potential range resolution, but this is a coded signal and
can be used only with special authorisation.
The most promising GNSS for the considered purposes are the EU Galileo system
and the new proposed GPS signal structure [6].
Figure 2(a)  shows the signal
spectrum of Galileo E5 signal. Two signal components (E5a and E5b) in the E5
band are modulated as a single wideband signal generated following the AltBOC
(15, 10) modulation. This wideband signal is centred at the frequency of 1191.795 MHz and has a bandwidth of
at least 50 MHz. Since the E5a and E5b signals transmit independent information from
each other, it is possible in theory to develop a signal processing technique
that combines the received E5a and E5b signals in such a way to give a
resolution of 3–8 m. Similarly,
the different signals (P-code and M-code) of the modernised GPS L1/L2 channel can be
combined to achieve 20–30 MHz bandwidth
or 5–8 m range
resolution. This makes GNSS (Galileo, GPS) based SS-BSAR a prospective
candidate for many practical applications.(a) Galileo E5 channel Spectrum, (b) GPS L1 or L2 channel Spectrum.
(a)(b)Table1 summarises some GNSS signal
parameters and their potential slant range resolution. The stated resolution is
for a quasimonostatic configuration. However, for other configurations the
resolution is dependent upon the geometry of the system, that is,
satellite-receiver-target positions not relative to each other. The effect of the system’s geometry on the
resolution is comprehensively  discussed in [7].Table 1
The main parameters of
different GNSS signals.
GNSSGLONASSGPSGalileoChannels  (code)L1(P)L2 (P)L1(P/M)L2 (P/M)L5E5a/bE5 (E5a+E5b)Central frequency (MHz)1602–16151246–12571575.41227.611761191.791191.795Minimum power (dBW)−161−167−158 (global beam)−154.9−157−154−138 (spot beam)Chip rate (Mcps)5.115.1110.23/5.1110.23/5.1110.2310.2310.23Aggregated bandwidth (MHz)——20–3020–30——20–50Range resolution (m)30305–85–815153–8It
should be noted that one of the key advantages of using a GNSS satellite
compared to other satellites (e.g., Geostationary TV satellite) is that the
user can choose the desired bistatic topology (low bistatic angle, i.e., negligible
resolution loss). This is due to the fact that 4 to 10 GNSS satellites are
simultaneously visible at any point on the earth. As a result, a particular
satellite in the best (or at least suitable) position can be selected and there
is no need for a very specific aircraft trajectory to allow the observation of
an area. On the other hand, geostationary satellites are fundamentally
positioned above the equator and this requires a specific aircraft trajectory
for mapping a particular area and, in many or even most situations, a vital
loss in ground resolution may take place.


## 3. Power Budget

The
receiving part of the SS-BSAR consists of two channels: the heterodyne channel
(pointed directly towards the satellite) is used for synchronisation and the
radar channel is used for receiving reflected signal from the target area. The
power budget of the radar channel is a determining parameter for target
detection, which is calculated for the time of aperture synthesis and
considering only targets that have RCS independent of frequency and angle. For
bistatic SAR, the expression for SNR after range and azimuth compression can be
written as [8](1)SN=ρAeσλη4πRRKTSVaΔaz,where  ρ is power flux density
of the transmitting signal in target area,  σ is the radar cross-section of the
target, η  is a general loss
factor, RR  is the receiver-target
range,  Va is the receiver
velocity,  Δaz is the potential
azimuth resolution.Table1 shows the
minimum received signal power by 0 dB omnidirectional antenna on the ground for
different GNSS signals. It can be noted that the Galileo E5 signal and new GPS
L5 signal provide at least 6 dB more power compared to GLONASS L1 channel. In
addition GPS after modernisation will broadcast the signal from a high-gain
directional antenna, termed as spot beam, which generates 20 dB more power than
its global beam and covers the ground area of 1200 km ×  1200 km that is enough
for our application.Using the parameters
provided in Tables1, 2,
and (1), Figure 3 shows the potential range of SS-BSAR
for different GNSS signals. If we consider 10–13 dB SNR as the radar detection
threshold, targets with 50 m2 RCS can be detected at the range of
approximately 5 km using Galileo E5 or GPS L5 signal. By using GPS spot beam (available
in 2013), the detection range can be extended to more than 50 km for target
with 50 m2 RCS.Table 2
Potential target detection range for different GNSS signals.
Transmitterλ(cm)Va(m/s)ηAe(m2)Δaz(m)Ts(K)Galileo
E525.22000.50.51410GPS
L525.5GPS
spot beam19GLONASS
L118.8Figure 3
Detection range for 50 m2 RCS target.It also should be noted that all the above range
calculations were done using the minimum power received for each signal. The
minimum power level is received when the satellite is near the horizon and the maximum when the satellite is at an
elevation of approximately 45°. On average, the difference between the minimum and maximum
power level is around 6 dB. Hence, one can expect four times more improvement
in the  maximum range
when a satellite at an elevation of about 45°.


## 4. Interference

Using GNSS as an illuminator in
SS-BSAR presents a specific problem in signal detection. In addition to the
desired target echo, a number of interference signals are present in the
system. GNSS signals are modulated continuous waves and the desired reflected
signal has to be detected against a continuous interference background. The
first type of such interference is direct path interference (DPI), which is the
signal received directly by the radar antenna from the illuminating satellite.
The second type is adjacent channel interference (API) coming from another GNSS
satellite operating in the same frequency band. These interference sources discussed
in [8, 9] showed
that the signal-to-interference ratio (SIR) is high enough to detect the
desired signal. It was shown that the interference and the desired signal are
essentially the same process, and at the output of the matched filter the
interference level is specified by the cross-correlation side-lobes. In
addition, the SAR algorithm itself acts as a spatial filter to these
interference sources. The fact that the interference signals are coherent to
the desired signal introduces some low-level false targets, but these false
targets will be
outside the observation area. Figure 4 shows the SIR evaluated
for a practical situation [9].Figure 4
Signal-to-interference ratio [9] (zoomed).


## 5. Glonass Signal Structure and Synchronisation

Figure5
shows a simplified block diagram of the structure of GLONASS signals transmitted
in the L1 frequency band [10]. The C/A and P-code signals are in phase quadrature.
The C/A code rate is 511 KHz and the code period is 1 millisecond. The C/A code
sequence is added (mod 2) to a 100 Hz navigation message. The P-code has a chip
rate of 5.11 MHz and is a truncated M-sequence repeated every 1 second. The
navigation message on the P-code has a clock rate of 50 Hz.Figure 5
Simplified block diagram of the L1 GLONASS signal structure.Mathematically
the transmitted signal can be written as(2)Y=ApP(t)Dp(t)cos(ωct+ϕ)+ACC(t)Dc(t)cos(ωct+ϕ),where  Y is the signal at L1
frequency, Ap(t)  is the amplitude of
the P-code, P(t)  represents the
polarity (±1) of the P-code, Dp(t)  is the binary (±1)
P-code navigation message,  ωc is the L1 frequency, ϕ is the initial phase,  Ac(t) is the amplitude of
the C/A code, C(t)  represents the polarity (±1) of
the C/A code,  Dc(t) is the binary (±1) C/A
navigation message.The
P-code will be used for the purpose of imaging, as it provides a reasonable
range resolution of about 30 m (quasimonostatic case) and is no longer
encrypted. Usually, in a radar signal processor, the range compression consists
of a correlation of the radar channel signal with the heterodyne channel signal
delayed for each range resolution cell in the multichannel correlator. In our
previous publication [7], it was demonstrated that for GLONASS signal it is not
possible to directly correlate the heterodyne channel with the radar channel.
For a quick reminder, we briefly discuss the reason below.The
signal received from the GLONASS satellite is a superposition of the C/A code
and P-code signals, the spectra of which overlap; the P-code (5.11 MHz
bandwidth) is used for the purpose of imaging, as mentioned earlier. If the
heterodyne channel is directly correlated with the radar channel,  the P-code
will be masked by the C/A code at the output of the correlator. The bandwidth
of the C/A code is only one-tenth that of the P-code but even if the C/A code
of the desired satellite signal is filtered out in the heterodyne channel, the
signal correlation properties are degraded by the C/A
codes of interfering satellites (It should be noted that this is a peculiarity of only GLONASS signal. In
GPS, C/A codes are transmitted on the same carrier frequencies by different
satellites. Therefore, the C/A code can be filtered out for all the satellites
in the heterodyne channel). It
was also demonstrated that, if the radar channel signal is correlated with a
locally generated signal containing only the P-code, the effect of the C/A code
could be suppressed. However, this technique needs navigation message decoding
which, in turn, requires P-code synchronisation. Figure6 shows the principles of the
proposed range compression  algorithm.Figure 6
Range compression algorithm.The synchronisation compromises of tracking the
satellite in delay, Doppler, and phase in order to fully decode the navigation
message. The “block adjustment of synchronising signal
(BASS)” algorithm for Doppler extraction was used. This algorithm is used in
software GPS receivers and is comprehensively discussed in [11]. For delay
tracking and algorithm based on conventional delayed-locked loop was used [11].
It should be noted that a detailed discussion of the synchronisation algorithms is outside the
scope of this paper. Here, we only present the experiment  results.Figure7
shows the experimental setup for collecting data from the satellite to verify
the range compression and synchronisation
algorithms. The signal from the satellite was directly received on two channels
(radar and heterodyne channels). The radar channel antenna was a stationary
omnidirectional antenna (6 dBi gain) and was separated by some 3 m from the
heterodyne channel antenna. This topology corresponds to a target at “zero
range.” For the heterodyne channel, a directional spiral antenna with a gain of
16 dBi was pointed towards the satellite. A satellite transmitting on channel
10 (1607.625 MHz) was used.Figure 7
Experimental setup for
confirming synchronisation.Figures8(a) and 8(b) shows the estimated frequency and
delay variation of the satellite. From Figure 8(b),
it is seen that the rate of
frequency variation is around 0.4 Hz/sec. Figure 8(c) shows the decoded message
signal from the P-code after phase tracking. These results confirm the proper
functioning of our algorithm. Figure 8(d) represents the correlation obtained
from the proposed range compression algorithm shown in Figure 6. It is seen
that the algorithm gives a good correlation and confirms our computer modelling
result. As expected, the width (first null) of the main lobe  is 0.2 microsecond
and the RMS sidelobe level of −60 dB for 45  seconds integration  time.(a) Estimated frequency, (b) estimated delay variation (c), decoded
navigation message, and (d) output
of range compression algorithm.
(a)(b)(c)(d)


## 6. Heterodyne Channel Doppler Compensation

Figure9
shows the experimental setups used to verify the imaging algorithm (discussed
in the next section). The first setup consisted of stationary heterodyne
channel and moving radar channel (for antenna synthesis). The second experiment
represents a more practical scenario, where both the heterodyne and radar
antennas are moving. It is obvious that the only difference between the two
setups is the presence of extra Doppler shift in the moving heterodyne channel.
This Doppler shift can be large enough to introduce a complete mismatch between
the heterodyne channel and the radar channel. Hence,
one needs to compensate the relative motion between transmitter and receiver.Figure 9
Experimental setups.First
of all, we consider an instantaneous bistatic triangle formed by the stationary
heterodyne channel, shown in Figure10(a). The three arms of the bistatic
triangle are transmitter-to-receiver path (represented by B), transmitter-to-target path (RT), and target-to-receiver (RR). Also, the bistatic angle is denoted by β, the transmitter elevation angle by φ, and the angle between  B and   RT by θTr. For our experimentation with flight imitator we considered
targets at short ranges (maximum target range 600 m). The typical
transmitter-to-receiver range for GLONASS is ~ 20000 km. Therefore, the angular
separation, θTr, is negligible. Hence, for a stationary heterodyne channel,
the Doppler shifts due to satellite motion (FS) in the two paths are similar. The residual Doppler
variation after range compression is present only due to receiver motion
relative to target at RR. This residual Doppler variation in the radar channel forms the
azimuth signature of a target. In the imaging algorithm, an appropriate azimuth
compression filter is designed for each range bin accordingly.Bistatic triangle for (a) stationary
heterodyne and (b) moving heterodyne.
(a)(b)In a
practical scenario, the heterodyne channel will be in motion as it is mounted
on a real aircraft. Figure10(b) shows the bistatic triangle for moving
heterodyne channel. The only difference compared to the stationary case is the
additional presence of Doppler variation (FRS) due to receiver motion relative to the satellite in the
heterodyne channel. It is clear from the figure that the residual Doppler
variation is FRS+FR. The question whether   FRS can be ignored when
designing the azimuth filter is discussed below.For
20 seconds integration time, the maximum tolerable frequency difference between
the designed azimuth signature and the actual received signal is ~ 0.003 Hz (Consideringπ/8 phase difference over the integration time.).  The value of FRS can range
from 10–100 Hz for an
aircraft moving with a velocity of 30 m/sec. Hence,   FRS should be
estimated and incorporated in the azimuth filter. It should be noted that this
problem is not faced in a monostatic SAR as the transmitter and receiver are
colocated. The method we used to estimate   FRS is explained in the
following section with the aid of an experimental setup.


### 6.1. Experimental
Setup to EstimateFRS and to Verify Moving Heterodyne
Synchronisation

Figure11
shows the experimental setup for collecting data from the GLONASS satellite to
estimate FRS  and to verify the
moving heterodyne synchronisation (We used GLONASS satellite
as GALILEO was not available at the time of experimentation. However, this is a
generic problem and the analysis can be extended to any transmitter used in
SS-BSAR.). The signal from the satellite
was directly received on two channels (moving and stationary). The moving
channel antenna was an omnidirectional antenna and was separated by some 3 m
from the stationary channel antenna.  The
moving channel corresponds to a moving heterodyne channel and we assume the
stationary antenna to be a target at “zero range.” In this experiment, we are
ignoring the radar channel as the sole aim of the experimentation is to verify
whether we can correctly estimate the FRS.Figure 11
Experimental setup to calculateFRS.The
two channels were simultaneously fed to a microwave receiver that down-converts
the received signal through appropriate filtering and amplification. The base-band
signal was then stored on a PC for off-line processing. The output of the
moving channel is also fed to a second “GNSS receiver.” This receiver stores
the navigation message of the GPS/GLONASS satellite and receiver observation
data. An algorithm was developed that extracts the satellite and the moving antenna
positions from the stored data. From this information we calculated theFRS, that is, Doppler variation due to receiver motion relative
to the satellite.To test that
whether we have correctly estimated theFRS, first of all we correlated the moving and the stationary
channel without compensating  FRS from the moving channel. Due to presence
of Doppler difference (FRS) between the two channels, one can expect degradation of
correlation function and rise in sidelobe level. Figure 12(a) shows the
degradation of correlation function and Figure 12(c)  shows a rise in the
sidelobe level (dotted line). Figure 12(b)  shows the correlations of the two
channels after we have removed the FRS from the moving channel. It
is seen that after removing the residual Doppler we obtain a good correlation function.
As
expected, the width (first null) of the main lobe is 0.2 microsecond and the
RMS sidelobe level of −64 dB for 35 seconds integration time
(Figure 12(c)). Hence, our estimate of FRS  is correct.(a) Correlation without removingFRS,  (b) correlation after removing FRS, and (c) RMS sidelobe level versus
integration time (log scale).
(a)(b)(c)


## 6.1. Experimental
Setup to EstimateFRS and to Verify Moving Heterodyne
Synchronisation

Figure11
shows the experimental setup for collecting data from the GLONASS satellite to
estimate FRS  and to verify the
moving heterodyne synchronisation (We used GLONASS satellite
as GALILEO was not available at the time of experimentation. However, this is a
generic problem and the analysis can be extended to any transmitter used in
SS-BSAR.). The signal from the satellite
was directly received on two channels (moving and stationary). The moving
channel antenna was an omnidirectional antenna and was separated by some 3 m
from the stationary channel antenna.  The
moving channel corresponds to a moving heterodyne channel and we assume the
stationary antenna to be a target at “zero range.” In this experiment, we are
ignoring the radar channel as the sole aim of the experimentation is to verify
whether we can correctly estimate the FRS.Figure 11
Experimental setup to calculateFRS.The
two channels were simultaneously fed to a microwave receiver that down-converts
the received signal through appropriate filtering and amplification. The base-band
signal was then stored on a PC for off-line processing. The output of the
moving channel is also fed to a second “GNSS receiver.” This receiver stores
the navigation message of the GPS/GLONASS satellite and receiver observation
data. An algorithm was developed that extracts the satellite and the moving antenna
positions from the stored data. From this information we calculated theFRS, that is, Doppler variation due to receiver motion relative
to the satellite.To test that
whether we have correctly estimated theFRS, first of all we correlated the moving and the stationary
channel without compensating  FRS from the moving channel. Due to presence
of Doppler difference (FRS) between the two channels, one can expect degradation of
correlation function and rise in sidelobe level. Figure 12(a) shows the
degradation of correlation function and Figure 12(c)  shows a rise in the
sidelobe level (dotted line). Figure 12(b)  shows the correlations of the two
channels after we have removed the FRS from the moving channel. It
is seen that after removing the residual Doppler we obtain a good correlation function.
As
expected, the width (first null) of the main lobe is 0.2 microsecond and the
RMS sidelobe level of −64 dB for 35 seconds integration time
(Figure 12(c)). Hence, our estimate of FRS  is correct.(a) Correlation without removingFRS,  (b) correlation after removing FRS, and (c) RMS sidelobe level versus
integration time (log scale).
(a)(b)(c)


## 7. SS-BSAR Imaging

In this section, a conceptual description of the
algorithm used for SS-BSAR image formation will be provided, along with some
experimental results. A specific configuration is assumed, where the
transmitter is a GNSS satellite and the receiver is airborne. A full
description of the algorithm can be found in [12, 13], along with detailed
descriptions of algorithms designed for other SS-BSAR configurations. Results
of our research in this area can also be found in
[14–17].


### 7.1. Algorithm Description

Before
proceeding into an explanation of the algorithm, it is perhaps useful to state
problems associated with SS-BSAR image formation first. Traditional monostatic SAR
algorithms exploit similarities in echo returns from different targets within
the illuminated scene in order to reduce the signal processing load. These
similarities are usually present in the range or azimuth target signatures and
allow efficient processing in the frequency domain. In BSAR, and particularly
SS-BSAR, these similarities do not exist. Moreover, the exact relationship between
the azimuth-time signature (a chirp signal) of a target and its Doppler
frequency spectrum is yet to be calculated analytically (at least without some
approximation). These features are impediments to the derivation of efficient
and accurate image formation algorithms operating in the frequency domain.A block diagram of the generalised SS-BSAR image
formation algorithm is shown in Figure13. It may be noticed that it is a
modification of the standard Range-Doppler algorithm, widely used in monostatic
SAR image formation, and thoroughly described in [18].Figure 13
Block diagram of SS-BSAR image formation algorithm.The
first step in the algorithm is to equalize the Range and Doppler histories of
targets residing at the same range from the receiver. This is achieved by
applying a single correction function to the SS-BSAR data, which removes the
time delay and associated phase due to the transmitter-to-receiver range at
each azimuth-time instant. The correction function is integrated with the range
compression step, hence the term “modified range compression.” The modified
range compression is performed in the range-frequency, azimuth-time domain. At
the output of this step, the Range and Doppler histories of targets at the same
range, but different cross-range becomes very similar for a wide range of
possible geometries. Since the transmitter-to-target and receiver-to-target ranges
can normally be approximated using second-order Taylor
series expansions in SS-BSAR, it is
also possible to derive signal expressions in the frequency domain. Therefore,
use of a modification of the Range-Doppler algorithm is a convenient method to
form the image of an observation area. A secondary range compression (SRC) is
performed in the two-dimensional frequency domain to compensate for the
cross-coupling between the range and azimuth frequencies. Before this operation
is executed, the Doppler ambiguity is resolved (i.e., because the target
azimuth signature could contain a large Doppler centroid outside the range of sampled
azimuth frequencies). Range cell migration (RCM) is corrected in the
range-time, azimuth-frequency (or Range, Doppler) domain, after RCM components
due to the receiver motion and residual RCM after the modified range
compression are calculated. For this operation, it is proposed to estimate the
receiver-to-target range from the range sum (the difference between the total
range history and the transmitter-to-receiver range history) in order to
identify the individual RCM components mentioned above. Finally, azimuth
compression is performed in the range, Doppler domain.


### 7.2. Experimental Results

In this section, we present the SS-BSAR images
obtained by applying the proposed algorithm on experimental data collected from
the experimental setups shown in Figure7. A detailed description of our
experimental hardware (including the flight imitator) can be found in [19]. The
experimental parameters are shown in Table 3. The geometry of the first
experiment is shown in Figure 14.Table 3
Experimental parameters.
ParameterStationary heterodyneMoving heterodyneGLONASS satelliteCOSMOS 2394COSMOS 2418Carrier frequency (MHz)1603.1251603.125Signal bandwidth (MHz)5.115.11Satellite’s azimuth angle (degree)178.49182Satellite’s elevation angle (degree)11.3638Bistatic angle (degree)5.6338Receiver velocity (m/sec)0.60.6Receiver’s height above the ground (m)~25~25Receiver’s aperture length (m)2727Integration time (s)4545Figure 14
SS-BSAR experiment geometry.Figure15(a)
shows the SS-BSAR image obtained for a stationary heterodyne channel (5.63° bistatic angle). The image obtained from the observation area is superimposed
on a satellite photograph of the area. The color-scale is in dB, where 0 dB
represents the highest processed echo intensity. It is important to note that
the heterodyne channel was stationary, so there was no need for the motion
compensation described in Section 6. However, the signal synchronisation
described in Section 5  was required. Estimates of the satellite’s instantaneous
Doppler frequency and time delay with respect to the heterodyne antenna
(provided by the synchronisation algorithm) were used for the modified range
compression. We can see from Figure 15(a) that the intensity variation in the SS-BSAR image obtained corresponds to
the variation in the strength of echoes from the observed area. More analysis
and verification on the obtained image can be found in [20].SS-BSAR image for (a) stationary
heterodyne and (b) moving heterodyne.
(a)(b)Figure15(b) shows the SS-BSAR image obtained for
moving heterodyne channel (38° bistatic angle). Therefore, all the algorithms described in previous
sections (synchronisation, motion compensation, and imaging) were applied to
the acquired data. The experimental parameters are also included
in Table 3.Comparing Figures15(a) with 15(b)
shows high level of similarity. Strong reflections at the near field are
observed in both images. Also, targets at the range of 240 m, 350 m, 500 m are visible
in both images. Figures 16(b)
and 16(c) show the strong reflections from the building
at 240 m in both images. The dynamic range is almost same in
both images, although the signal strength in second image is lower than the
first one. This is due to possible bistatic RCS reduction.(a) Aerial photo of building at 240 m, (b) enlargement of image
area around the building at 240 m for
10° bistatic angle, and (c) enlargement of image area
around the building at 240 m for 40° bistatic angle.
(a)(b)(c)Figure17 shows cross-sections of the building shown
in Figure 16(a), taken along the azimuth direction for each of the SS-BSAR
images. It is observed that even though the physical characteristics of the
building have not changed, image reflectivity has changed. This can be because
the imaging geometry between the two acquisitions is different.Azimuth cross-sections
taken along the building at 240 m for the (a) moving heterodyne and (b) stationary
heterodyne images.
(a)(b)


### 8. Conclusions

This
paper gave an overview of the research carried out by University of Birmingham
in the area of SS-BSAR, utilising microwave emissions from GNSS transmitters as
the ranging signal. GLONASS satellite was used for experimentation.In the paper, different
GNSS transmitters (GPS, GLONASS, and GALILEO) were compared on the basis of
resolution and power budget. It was highlighted that GALILEO and the new GPS
satellite are the most prospective candidate for SS-BSAR. GALILEO E5 signal has
potential range resolution of 3–8 m and can detect targets with 50 m2 RCS at a range of
approximately 5 km. On the other hand, the new GPS signal can also provide the
same range resolution, but has 10 times more detection range compared to
GALILEO.Some of the practical problems (such as interference,
signal structure, synchronisation) were also briefly discussed. One of the important
problems was moving heterodyne channel. It was discussed that the motion of
the heterodyne channel introduces a Doppler shift. This Doppler shift needs to
be estimated and incorporated in the azimuth compression filter design. A
method to estimate this Doppler shift was described and experimentally
verified.An imaging algorithm for SS-BSAR was briefly
discussed and experimentally tested. Using the flight imitator, bistatic images were
successfully obtained for moving and stationary heterodyne channel. These
images were also briefly analysed. Comparison of moving heterodyne image with
that of stationary heterodyne channel showed high level of similarity,
suggesting our new (moving heterodyne) configuration is functioning properly. Finally,
we can conclude that we can obtain an SS-BSAR image for a moving heterodyne
channel. The next stage of our work will concentrate on obtaining image using a
real aircraft.


## 7.1. Algorithm Description

Before
proceeding into an explanation of the algorithm, it is perhaps useful to state
problems associated with SS-BSAR image formation first. Traditional monostatic SAR
algorithms exploit similarities in echo returns from different targets within
the illuminated scene in order to reduce the signal processing load. These
similarities are usually present in the range or azimuth target signatures and
allow efficient processing in the frequency domain. In BSAR, and particularly
SS-BSAR, these similarities do not exist. Moreover, the exact relationship between
the azimuth-time signature (a chirp signal) of a target and its Doppler
frequency spectrum is yet to be calculated analytically (at least without some
approximation). These features are impediments to the derivation of efficient
and accurate image formation algorithms operating in the frequency domain.A block diagram of the generalised SS-BSAR image
formation algorithm is shown in Figure13. It may be noticed that it is a
modification of the standard Range-Doppler algorithm, widely used in monostatic
SAR image formation, and thoroughly described in [18].Figure 13
Block diagram of SS-BSAR image formation algorithm.The
first step in the algorithm is to equalize the Range and Doppler histories of
targets residing at the same range from the receiver. This is achieved by
applying a single correction function to the SS-BSAR data, which removes the
time delay and associated phase due to the transmitter-to-receiver range at
each azimuth-time instant. The correction function is integrated with the range
compression step, hence the term “modified range compression.” The modified
range compression is performed in the range-frequency, azimuth-time domain. At
the output of this step, the Range and Doppler histories of targets at the same
range, but different cross-range becomes very similar for a wide range of
possible geometries. Since the transmitter-to-target and receiver-to-target ranges
can normally be approximated using second-order Taylor
series expansions in SS-BSAR, it is
also possible to derive signal expressions in the frequency domain. Therefore,
use of a modification of the Range-Doppler algorithm is a convenient method to
form the image of an observation area. A secondary range compression (SRC) is
performed in the two-dimensional frequency domain to compensate for the
cross-coupling between the range and azimuth frequencies. Before this operation
is executed, the Doppler ambiguity is resolved (i.e., because the target
azimuth signature could contain a large Doppler centroid outside the range of sampled
azimuth frequencies). Range cell migration (RCM) is corrected in the
range-time, azimuth-frequency (or Range, Doppler) domain, after RCM components
due to the receiver motion and residual RCM after the modified range
compression are calculated. For this operation, it is proposed to estimate the
receiver-to-target range from the range sum (the difference between the total
range history and the transmitter-to-receiver range history) in order to
identify the individual RCM components mentioned above. Finally, azimuth
compression is performed in the range, Doppler domain.


## 7.2. Experimental Results

In this section, we present the SS-BSAR images
obtained by applying the proposed algorithm on experimental data collected from
the experimental setups shown in Figure7. A detailed description of our
experimental hardware (including the flight imitator) can be found in [19]. The
experimental parameters are shown in Table 3. The geometry of the first
experiment is shown in Figure 14.Table 3
Experimental parameters.
ParameterStationary heterodyneMoving heterodyneGLONASS satelliteCOSMOS 2394COSMOS 2418Carrier frequency (MHz)1603.1251603.125Signal bandwidth (MHz)5.115.11Satellite’s azimuth angle (degree)178.49182Satellite’s elevation angle (degree)11.3638Bistatic angle (degree)5.6338Receiver velocity (m/sec)0.60.6Receiver’s height above the ground (m)~25~25Receiver’s aperture length (m)2727Integration time (s)4545Figure 14
SS-BSAR experiment geometry.Figure15(a)
shows the SS-BSAR image obtained for a stationary heterodyne channel (5.63° bistatic angle). The image obtained from the observation area is superimposed
on a satellite photograph of the area. The color-scale is in dB, where 0 dB
represents the highest processed echo intensity. It is important to note that
the heterodyne channel was stationary, so there was no need for the motion
compensation described in Section 6. However, the signal synchronisation
described in Section 5  was required. Estimates of the satellite’s instantaneous
Doppler frequency and time delay with respect to the heterodyne antenna
(provided by the synchronisation algorithm) were used for the modified range
compression. We can see from Figure 15(a) that the intensity variation in the SS-BSAR image obtained corresponds to
the variation in the strength of echoes from the observed area. More analysis
and verification on the obtained image can be found in [20].SS-BSAR image for (a) stationary
heterodyne and (b) moving heterodyne.
(a)(b)Figure15(b) shows the SS-BSAR image obtained for
moving heterodyne channel (38° bistatic angle). Therefore, all the algorithms described in previous
sections (synchronisation, motion compensation, and imaging) were applied to
the acquired data. The experimental parameters are also included
in Table 3.Comparing Figures15(a) with 15(b)
shows high level of similarity. Strong reflections at the near field are
observed in both images. Also, targets at the range of 240 m, 350 m, 500 m are visible
in both images. Figures 16(b)
and 16(c) show the strong reflections from the building
at 240 m in both images. The dynamic range is almost same in
both images, although the signal strength in second image is lower than the
first one. This is due to possible bistatic RCS reduction.(a) Aerial photo of building at 240 m, (b) enlargement of image
area around the building at 240 m for
10° bistatic angle, and (c) enlargement of image area
around the building at 240 m for 40° bistatic angle.
(a)(b)(c)Figure17 shows cross-sections of the building shown
in Figure 16(a), taken along the azimuth direction for each of the SS-BSAR
images. It is observed that even though the physical characteristics of the
building have not changed, image reflectivity has changed. This can be because
the imaging geometry between the two acquisitions is different.Azimuth cross-sections
taken along the building at 240 m for the (a) moving heterodyne and (b) stationary
heterodyne images.
(a)(b)


## 8. Conclusions

This
paper gave an overview of the research carried out by University of Birmingham
in the area of SS-BSAR, utilising microwave emissions from GNSS transmitters as
the ranging signal. GLONASS satellite was used for experimentation.In the paper, different
GNSS transmitters (GPS, GLONASS, and GALILEO) were compared on the basis of
resolution and power budget. It was highlighted that GALILEO and the new GPS
satellite are the most prospective candidate for SS-BSAR. GALILEO E5 signal has
potential range resolution of 3–8 m and can detect targets with 50 m2 RCS at a range of
approximately 5 km. On the other hand, the new GPS signal can also provide the
same range resolution, but has 10 times more detection range compared to
GALILEO.Some of the practical problems (such as interference,
signal structure, synchronisation) were also briefly discussed. One of the important
problems was moving heterodyne channel. It was discussed that the motion of
the heterodyne channel introduces a Doppler shift. This Doppler shift needs to
be estimated and incorporated in the azimuth compression filter design. A
method to estimate this Doppler shift was described and experimentally
verified.An imaging algorithm for SS-BSAR was briefly
discussed and experimentally tested. Using the flight imitator, bistatic images were
successfully obtained for moving and stationary heterodyne channel. These
images were also briefly analysed. Comparison of moving heterodyne image with
that of stationary heterodyne channel showed high level of similarity,
suggesting our new (moving heterodyne) configuration is functioning properly. Finally,
we can conclude that we can obtain an SS-BSAR image for a moving heterodyne
channel. The next stage of our work will concentrate on obtaining image using a
real aircraft.



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*Source: 807958-2008-07-08.xml*