Chapter 5 Experiments planning
In the last part of this work, we carried out experiments regarding the feasibility of a passive radar with a low cost equipment by using a software defined architecture (i.e USRP and Gnuradio environment ). Experiments have been conducted for different scenarios as urban, coastal and aerial in order to demonstrate the feasibility and also the flexibility of the proposed solution.
The scenario, and the targets of interest was defined before each experiment. Then, to understand the detection probability and the radar coverage for a fixed geometry, the expected Doppler frequencies and SNR values were calculated.
The experiment is presented together with a few considerations about SNR, direct signal and Doppler frequency.
The majority of the following experiments are showed in [31][32][34]-[38].
5.1 Expected SNR
The bistatic radar equation represents the starting point for analysis of the performance of a passive radar system.
2
2 2
0
1 1
4 4 4
t t
r r
i b
n t r
PG
P G
SNR L
P r r kT BF
σ λ
π π π
= = (5.1)
where
• Pr is the received power (W)
• Pn is the receiver noise power (W)
• Pt is the transmit power (W)
• Gt is the transmit antenna gain
• Gr is the receiver antenna gain
• rt is the transmitter-to-target range (m)
• rr is the receiver-to-target range (m)
• σb is the bistatic radar cross-section
• λ is the signal wavelength
• k is the Boltzman constant 1.38e-23
• T0 is the noise reference temperature, 290 K
• B is the receiver effective bandwidth (Hz)
• Fis the noise figure
• Lare the system losses (≤1)
Equation (5.1) represents the Signal to Noise Ratio before the matched filter, SNRi. The direct signal is used as a reference against which the echo signal is correlated to provide processing gain for sensitivity and bandwidth for resolution. The effective bandwidth of the reference signal is subject to the processing gain owing to the coherent integration.
The coherent integration provides a processing gain and the signal to noise values become:
( )
out i p i int
SNR =SNR G = SNR T B (5.2)
Where SNRout is the signal to noise ratio after the matched filter and Tint is the
As we know, the performances of a passive radar system are strictly dependent on the geometry ( i.e. transmitter and receiver positions and chosen surveillance area).
In order to have a clear and intuitive view of the surveillance area, a georeferencing software tool to predict the SNR levels has been developed. The software tool exploiting the Google Earth Toolbox, available in
and Googlearth. Matlab allows to calculate eq.
is possible to view the results in Googlearth.
The user must insert the following parameters: the integration time in seconds, the geographical coordinates ( i.e. : Latitude and Longitude in degrees and altitude respect to the sea level) of transmitter and receiver, the Effective Radiate Power ERP of the transmitter, the losses L on the receiver chain, the receiver bandwidth and the receiver noise figure. Moreover the beam
considering Figure 4.38 and Figure
considered irradiation patterns available from
trisector antenna pattern composed by 3 planar arrays has been simulated for UMTS antenna (Figure 5.2).
Figure
As we know, the performances of a passive radar system are strictly dependent on the geometry ( i.e. transmitter and receiver positions and chosen surveillance area).
In order to have a clear and intuitive view of the surveillance area, a georeferencing software tool to predict the SNR levels has been developed. The software tool exploiting the Google Earth Toolbox, available in [33],integrates two programs Matlab and Googlearth. Matlab allows to calculate eq.(5.2) in the surveillance area and then it is possible to view the results in Googlearth.
The user must insert the following parameters: the integration time in seconds, the geographical coordinates ( i.e. : Latitude and Longitude in degrees and altitude respect ) of transmitter and receiver, the Effective Radiate Power ERP of the transmitter, the losses L on the receiver chain, the receiver bandwidth and the receiver noise figure. Moreover the beam-patterns of the receiving antennae are inserted Figure 4.39. Regarding the antenna of the transmitters, we nsidered irradiation patterns available from [34] for TV antenna (Figure
trisector antenna pattern composed by 3 planar arrays has been simulated for UMTS
igure 5.1 Azimuth pattern for TV antenna
As we know, the performances of a passive radar system are strictly dependent on the geometry ( i.e. transmitter and receiver positions and chosen surveillance area).
In order to have a clear and intuitive view of the surveillance area, a georeferencing software tool to predict the SNR levels has been developed. The software tool ,integrates two programs Matlab e area and then it
The user must insert the following parameters: the integration time in seconds, the geographical coordinates ( i.e. : Latitude and Longitude in degrees and altitude respect ) of transmitter and receiver, the Effective Radiate Power ERP of the transmitter, the losses L on the receiver chain, the receiver bandwidth and the receiver ing antennae are inserted . Regarding the antenna of the transmitters, we igure 5.1) and a trisector antenna pattern composed by 3 planar arrays has been simulated for UMTS
Figure 5.2 UMTS 3-D pattern antenna
An example of SNR map for DVB-T transmitter is shown in Figure 5.3.
Where we considered an integration time equal to 200 ms, a bistatic radar cross section BRCS of 5 m2, a noise figure equal to 5 dB ( DBSRX datasheets indicate a noise figure in the range from 2.5 to 5 dB ), a value of losses around 5 dB. The transmitter is located on Monte Serra and the receiver is locate in Pisa about 14 km away from the transmitter. The Effective Radiated Power considered is 10 kW for DVB-T transmitter.
Figure 5.3 Example of view using the georeferencing software tool
Scenario in Figure 5.3 presents values of 12-13 SNR at 50 km from receiver.
Figure 5.4 SNR map for UMTS transmitter
Figure 5.4 shows a UMTS scenario for transmitter and receiver located in Pisa, the distance between transmitter and receiver is around 200 m and the ERP is 100 W. In this configuration there are values of 15 dB at 8.5 km from receiver.
The SNR maps do not consider the acquisition system and the direct signal, the next paragraph shows how the direct path interference limits the range coverage of the radar.
5.2 Direct Path Interference
As we know, the scattered radiation from illuminator of opportunity is used to survey a scene or zone of interest. Directly received radiation provides a reference signal for coherent operation in a separate receive channel. However, the directly received signal will also enter the surveillance channel and can represent a fundamental source of undesirable interference that will provide a basic limit on radar performance.
The portion of direct signal that enters the surveillance channel limits the capability to capture the targets. In fact, the dynamic range of Analog to Digital Converter risks to be saturated by the direct signal level.
We can formulate a simple expression for the amount of direct signal suppression required by calculating the ratio of the direct signal power to the target signal power and requiring this to be at least the same value as that used to compute the signal to quantization noise for analog to digital converters. Therefore the obtained equation should guarantee to have at least 1 bit that codifies the target signal.
The target signal power is:
2 3 2 2
(4 )
t t r b
r
t r
P PG G
r r λ σ
= π (5.3)
where
• Pr is the received power (W)
• Pt is the transmit power (W)
• G is the transmit antenna gain
• rt is the transmitter-to-target range (m)
• rr is the receiver-to-target range (m)
• σb is the bistatic radar cross-section
• λ is the signal wavelength
Furthermore, the direct signal power received on surveillance channel is:
2 1
4 2 4
t t r
r d
P PG G d
λ
π π
= (5.4)
where:
• Prd is the direct signal power received on surveillance channel (W)
• Pt is the transmit power (W)
• Gt is the transmit antenna gain
• Gr1 is the receiver antenna gain on the direction of the transmitter
• d is the transmitter-to-receiver distance (m)
The direct signal suppression factor is defined as:
rd r
DPI P
= P (5.5)
The 4 ADCs of USRP have a sampling rate of 64 MSps and a bit resolution, that can be managed by software, of 12 bits or 8 bits for sample. Because of the presence of USRP interface after the sampling and digital down conversion, a decimation of 8 is performed and the signal-to-quantization noise ratio is given by:
6.02 1.76 10 log s ( )
ADC
D
SNR N dB f dB
f
= + +
(5.6)
where:
• N is the number of bit ( our case is 12 or 8 bit )
• fs is the sampling frequency of the ADC converter 64 MHz
• fD is the sampling frequency after decimation ( in our case 8 MHz)
If eq.(5.5) is greater than eq.(5.6) this means that the ADC is sampling only direct signal and does not have bit for target signal. The equation that should be satisfied in order to have at least 1 bit for the target signal is:
( ) ( )
r d r ADC
P dB −P dB ≤SNR (5.7)
The effect of the above equation is to limit the radar coverage especially when we acquire with 8 bit per sample. As we can see in Figure 5.5 and Figure 5.6, the white line and the blue line represent the border where eq.(5.7) is satisfied.
Figure 5.5 DVB-T Scenario receiver located on Pisa, white line delimits the detection range with 12 bit, the blue line limits the detection range with 8 bit
In Figure 5.5 the maximum range with 8 bit is around 8 km, instead for UMTS scenario ( Figure 5.6 ) the maximum range becomes less than 5 km with 8 bit per sample.
Figure 5.6 UMTS Scenario receiver located on Pisa, white line delimits the detection range with 12 bit, the blue line limits the detection range with 8 bit
The eq.(5.7) localizes the limit where the target signal on the surveillance channel can be acquired. After that step a digital direct signal suppression using pre-processing techniques as adaptive filters (see par. 3.4.1) will be performed.
Moreover a key point in the acquisition phase is to avoid the saturation of the ADC. It is possible to carry out this operation looking for the gains on DBSRX that allows to keep the input signal within the input voltage range of the ADC. In fact, the saturation effect is to distort the signal and to compromise the detection of the target signal. Thus, before to perform the detection, the system needs a calibration phase where the gains of DBSRX are chosen in order to exploit all the dynamic range of the ADC.
5.3 Expected Doppler Frequencies
When an experiment was carried out we performed Doppler frequency calculations for the geometry considered. Also in this case a software tool has been developed considering the geographical area of interest.
The implemented formula is:
1 2
1 2
1
d
r v r v f =
r r
λ
⋅ + ⋅
(5.8)
where
• fd is the bistatic Doppler frequency
• rt
is the transmitter-target vector
• rr
is the receiver-target vector
• v
is the velocity vector
5.4 EXPERIMENTS
In this paragraph the experiments results are shown. Experiments in different scenarios have been carried out . Firstly we show results for Urban environment, then for coastal environment. Afterwards detections in aerial scenario with a long data acquired is been carried out. The cross-ambiguity functions CAFs have been calculated with one of the methods shown in Chapter 2.
5.4.1DVB-T passive radar detections in urban environment [32]
The experiment scenario geometry is shown in Figure 5.7. The Illuminator of Opportunity used for this experimental research is a TV transmitter located on Monte Serra (close to Pisa). The DVB-T channel exploited as an illuminator of opportunity operates at carrier frequency of 818 MHz. The receiver is placed at the Department of Information Engineering of the University of Pisa. The baseline length (transmitter- receiver distance) is about 14 km. Cars travelling on Aurelia roadway near the department (about 300 m from the receiver) have been considered as targets of interest.
Figure
Figure
The antenna configuration is presented in Figure 5.9 shows the S
paragraphs. The blue line represents the detection limit with 8 bit.
TX direction
Figure 5.7 DVB-T passive radar environment
Figure 5.8 Antenna configuration for the experiment
The antenna configuration is presented in Figure 5.8.
shows the SNR levels at receiver under the assumptions done in previous e line represents the detection limit with 8 bit.
TX direction
Aurelia direction
NR levels at receiver under the assumptions done in previous
Figure 5.9 Expected SNR levels
In Figure 5.10 the expected Doppler frequencies for cars travelling on Aurelia roadway are shown and the green rectangular border represents the expected Doppler frequencies in the range from 5 Hz for cars travelling at 10 km/h, to 35 Hz for cars travelling at 50 km/h (if the cars respect the velocity limit of 50 km/h ).
Figure 5.10 Expected Doppler frequencies for cars travelling on Aurelia roadway
In Figure 5.11b the Range Doppler Cross
Some dominant structures are highlighted with circles, the black one is a big building and the white one is a row of trees. Their presence in the CFA is evident at zero Doppler.
Figure 5.11 a) Experiment scenario with highlighted structures,
b the Range Doppler Cross-Ambiguity Function (CAF) is shown.
me dominant structures are highlighted with circles, the black one is a big building and the white one is a row of trees. Their presence in the CFA is evident at zero
a)
b)
Experiment scenario with highlighted structures, b) Cross ambiguity function
) is shown.
me dominant structures are highlighted with circles, the black one is a big building and the white one is a row of trees. Their presence in the CFA is evident at zero-
Cross ambiguity function
A photo taken at the same time of the data acquisition is shown in Figure 5.12a. The Cross-Ambiguity Function (CAF) presented in Figure 5.12b has been obtained by using pre-processing technique in order to reduce clutter echo. Some details are shown in Figure 5.12b for the detected targets. The truck echo is easily recognizable as the strongest echo at a distance equal to 351 m with a positive Doppler frequency equal to 30.7 Hz (i.e.: about 50 km/h). Moreover, some car echoes present themselves with negative Doppler frequencies as the cars were travelling along the opposite direction.
The strong echo placed at 420 m from the receiver is not due to stationary clutter since its Doppler frequency is 5 Hz, as shown in Figure 5.12b. It is caused by a number of cars, with a very low speed, approaching or departing from a traffic light ruled crossroads.
a)
Figure 5.12– Experimental scenario: a)
A photo taken during another Ambiguity Function (CAF) is
been used in order to reduce clutter echo.
In the CAF of Figure 5
visible at a range of about 315 m with a positive Doppler frequency equal to 35 Hz (i.e.:
about 60 km/h). Echoes of cars travelling in the opposite direction to the bus are
around the same distances with negative Doppler frequencies. A superposition of target echoes can be seen at a distance of about 400 m from the receiver
by cars approaching the crossroad.
b)
Experimental scenario: a) Scenario’s snapshot during the acquisition, b) CAF after pre processing on reference channel
A photo taken during another acquisition ( Figure 5.13 ) and the
Ambiguity Function (CAF) is presented in Figure 5.14. A pre-processing technique has been used in order to reduce clutter echo.
5.14 the echo relative to the bus is the strongest peak clearly visible at a range of about 315 m with a positive Doppler frequency equal to 35 Hz (i.e.:
Echoes of cars travelling in the opposite direction to the bus are
around the same distances with negative Doppler frequencies. A superposition of target echoes can be seen at a distance of about 400 m from the receiver,
pproaching the crossroad.
Scenario’s snapshot during the acquisition, b) CAF after pre-
and the relative Cross- processing technique has
the echo relative to the bus is the strongest peak clearly visible at a range of about 315 m with a positive Doppler frequency equal to 35 Hz (i.e.:
Echoes of cars travelling in the opposite direction to the bus are visible around the same distances with negative Doppler frequencies. A superposition of target probably produced
Figure 5.13
Figure 5.14 CAF after preprocessing relative to above figure
5.4.2UMTS passive radar detections in urban environment
The experiment scenario geometry is shown in
experimental research is a UMTS base station. The receiver is placed at the Department of Information Engineering of the University of Pisa. The baseline length (transmi receiver distance) is about 114 m. The UMTS downlink channel operates at a carrier frequency of 2.1367 GHz. Cars
department (about 250 m from the receiver) have been considered as targets of interest.
In Figure 5.16 the measurement site is presented where the reference and the target antenna steering directions are shown
position. It should be noted that antennas are not steered in a favourable direction (i.e.:
13 Snapshot takebn during the acquisition
CAF after preprocessing relative to above figure
radar detections in urban environment
scenario geometry is shown in Figure 5.15. The IO used for this experimental research is a UMTS base station. The receiver is placed at the Department of Information Engineering of the University of Pisa. The baseline length (transmi receiver distance) is about 114 m. The UMTS downlink channel operates at a carrier frequency of 2.1367 GHz. Cars and trucks travelling on Aurelia roadway near the department (about 250 m from the receiver) have been considered as targets of interest.
the measurement site is presented where the reference and the target antenna steering directions are shown along with the UMTS transmitter and the road position. It should be noted that antennas are not steered in a favourable direction (i.e.:
. The IO used for this experimental research is a UMTS base station. The receiver is placed at the Department of Information Engineering of the University of Pisa. The baseline length (transmitter- receiver distance) is about 114 m. The UMTS downlink channel operates at a carrier
ks travelling on Aurelia roadway near the department (about 250 m from the receiver) have been considered as targets of interest.
the measurement site is presented where the reference and the target with the UMTS transmitter and the road position. It should be noted that antennas are not steered in a favourable direction (i.e.:
“transmitter looking over the receiver’s shoulder”) thus resulting in a strong direct path signal on the target channel.
Figure 5.15 Experiment scenario geometry
Figure 5.16 Measurement set-up
The expected Doppler frequencies for cars travelling on Aurelia roadway are shown in Figure 5.17. Within the distances of interest (230-260 m) and in the assumption of car
speed ranging between 10 and 70 km/h, the expected Doppler frequency absolute value is 210 Hz at most.
Figure 5.17 Expected Doppler frequencies
A photo taken at the same time of the data acquisition is shown in Figure 5.18, where only one moving target is present (highlighted with a red circle).
Figure 5.18 – Surveillance area snapshot during the acquisition
The Cross-Ambiguity Function (CAF) presented in Figure 5.19 is obtained directly from the raw data without any pre-processing technique. Clutter echo and direct path interference can be noted at zero Doppler and at zero delay respectively. Nevertheless the target echo is easily recognizable in the range bin corresponding to 240 m (receiver- target distance) with a negative Doppler frequency equal to 168 Hz (i.e.: about 65 km/h). The CAF presented in has been obtained by using NLMS (Normalised Least Mean Squares) filtering Figure 5.20 on the reference channel in order to reduce clutter echo. Both of the CAFs exhibit a sidelobe spread along the range and periodic peaks along the Doppler. Specifically the repetition period is equal to 10 ms (time frame length) as detailed in Chapter 4. Nevertheless it should be noted that their strength is clearly reduced in the pre-processed CAF.
Figure 5.19 CAF evaluated on raw data (no pre-processing)
Figure 5.20 CAF after pre-processing on reference channel
In Figure 5.21, the UMTS CAF clearly shows the presence of a strong echo relative to a target located at a distance of about 240 m with a Doppler frequency of 175 Hz that corresponds to a target speed of about 70 km/h. In the snapshot the presence of a single target (i.e.: a van) in the area under surveillance is confirmed.
Figure 5.21 Snapshot during the experiment
Figure 5.22 UMTS CAF
5.4.3Coastal ship detection
The experiment scenario geometry is shown in Figure 5.23.
Figure 5.23 Experiment scenario geometry
Specifically the receiver was located at the “CSSN-ITE G. Vallauri” institute in Livorno, the used illuminator of opportunity was a DVB-T transmitter located on
“Monte Serra” in Pisa (around 32 km far from the receiver) and the surveillance antenna was directed towards an area of sea in front of the receiver site.
The used DVB-T channel carrier frequency is 818 MHz and the targets of interest were ships arriving and departing from the nearby harbour.
In Figure 5.24 the expected coverage capability of the system is presented. The colormap is relative to the SNR levels at the receiver, the blue and white contours are the maximum range capability as a result of the number of bits used for the sampling, respectively 8 and 12 bits.
Figure 5.24 Expected coverage capability (SNR values)
In Figure 5.25 the expected Doppler frequencies for ships departing from the nearby harbour are shown.
Figure 5.25 Expected Doppler frequencies for ships departing from the nearby harbour (receding from receiver)
Within the distance of interest (1.5-3.3 nm) and in the assumption of ship speed ranging between 5 and 10 kts, the expected Doppler frequency absolute value will be comprised between 20 and 60 Hz.
The surveillance area during the acquisition is shown Figure 5.26; the target of interest is clearly shown in the zoomed view.
Figure 5.26 Surveillance area during the acquisition
Figure 5.27 Zoomed view of the target
A pre-processing technique based on NLMS (Normalised Least Mean Squares) filtering has been used in order to reduce clutter echo and direct path interference. The Cross- Ambiguity Function (CAF) obtained after pre-processing is presented in Figure 5.28.
The echo relative to the ship is clearly visible at a range of about 2.1 nm with a negative Doppler frequency equal to -32 Hz (i.e.: about 6.5 kts). It should be noted that the target echo is actually formed by two main peaks with the same Doppler frequency, respectively a strongest one further away from the radar and a closer one that is weaker.
Looking at the close up view of the target reported in Figure 5.27, it can be noted that the target presents two main scattering structures, a huge one at the bow and another big one at the stern. The two main peaks visible in the CAF can be directly associated with the two main scattering structures. Another confirmation comes from the target length (around 170 m) that is in accordance with the distance of the two main target peaks in the CAF (around 160 m).
The target of interest was voluntarily a big ship in order to verify the effectiveness of the proposed passive radar system, nevertheless more experiments are planned in order to deal with smaller ships located at further distances.
Figure 5.28 DVB-T CAF of the surveillance area
5.4.4Aerial Scenario
The experiment scenario geometry is shown in Figure 5.23.
Figure 5.29 Geometry the experiment
The receiver was located at the CNIT institute in Pisa, the used illuminator of opportunity was a DVB-T transmitter located on “Monte Serra” in Pisa (around 13 km far from the receiver) and the surveillance antenna was directed towards the Pisa airport nearby.
Figure 5.30 Expected Doppler frequencies
The expected Doppler frequencies for a target approaching the receiver are shown in Figure 5.30. The data has been acquired each 2 seconds. A pre-processing technique has been applied in order to reduce the strong direct path interference. The target is clearly visible, and was moving from 1753 m to 910 m with a average speed of 290 kilometres per hour (Figure 5.31).
a)
b)
c)
d)
