Hardware-In-the-Loop Radar Test Simulator

Halit Ergezer, M. Furkan Keskin and Osman Gunay

IKES, Microwave Electronic Systems Inc., Ankara, Turkey

Keywords:

Radar Simulator, hardware-in-the-loop, Target, Jammer, Clutter

Abstract:

In this work, a real-time hardware-in-the-loop (HIL) radar target and environment simulator (RTSim) is pre-

sented. RTSim is developed to test the radar systems starting from the initial algorithm development until

the ﬁnal ﬁeld testing stages. In this way, it is possible to avoid the costly ﬁeld tests in constantly changing

conditions and test the radar systems in a controlled but highly complex environments. In the real-time oper-

ation scenario, Radar Signal Processing Unit (RSPU) sends the parameters of the radar signal to the RTSim.

For each receive channel, RTSim generates baseband IQ (16-bit I, 16-bit Q) signals using these parameters

and user programmed environment including targets, jammers, atmospheric effects, clutter, and radar related

system noise. The generated baseband signals are sent to RSPU over ﬁberoptic lines.

1 INTRODUCTION

The need for real-time simulation tools to test radar

systems is increasing in parallel with developments

in radar technology. The algorithms used in RSPUs

should be justiﬁed under real environment conditions

prior to the integration with radar hardware. The cost

of real environment tests is very high and it is al-

most impossible to repeat the experiments under the

same conditions. Therefore this necessitates the de-

velopment of a system that simulates the environment

in which radar signals travel, and the targets that the

radar is trying to detect.

Many of the radar target and environment simu-

lators are designed as commercial products and their

implementation details are not disclosed as academic

publications (Utteridge, 1987; Saab Sensis, 2013; In-

tersoft Electronics, 2013; EW Simulation Technol-

ogy, 2013; Technology Service Corporation, 2013).

All of these products are analog simulators that work

in the RF (Radio Frequency) or IF (Intermediate Fre-

quency) band and they are not closed loop systems

which means that they do not receive the radar param-

eters in real-time. Another radar target generator that

uses FPGAs (Field Programmable Gate Arrays) for

baseband signal generation is proposed in (Andraka

and Phelps, 1998). Compared to these products, RT-

Sim is a more ﬂexible digital simulator that generates

phase-coded baseband IQ signals. All parameters of a

radar signal (RF frequency, waveform type, sampling

rate, etc.) can beadjusted by radar for each pulse burst

waveform.

The components of a typical radar, a test environ-

ment and the role of RTSim is shown in Figure 1.

The simulator generates signals whose properties

are determined by the radar signal processing units,

in the form of baseband IQ. The generated signal will

contain all the effects described in the user deﬁned

scenario including; targets, jammers, chaff, decoys,

environment (clutter and propagation effects), anten-

nas, and radar hardware (ampliﬁers, mixers, etc.).

The generated IQ signal is sent to the RSPUs over

ﬁberoptic lines. In that sense RTSim provides a

hardware-in-the-loop test environment that can ac-

count for all the effects that a radar signal encounters

until it is received by the RSPUs.

2 COMPONENTS OF RTSIM

RTSim consists of a control PC, embedded processors

and FPGA (Field Programmable Gate Array) hard-

ware as shown in Figure 2. Control PC has the sim-

ulation engine and user interfaces. Simulation engine

calculates the navigation and orientation parameters

of radar and target platforms in real-time. It uses Dig-

ital Terrain Elevation Data (DTED) maps for terrain

visualization. All radar and target parameters can be

adjusted from the user interfaces.

Target parameters include:

• Position, velocity, route,

666

Ergezer H., Keskin M. and Gunay O..

Hardware-In-the-Loop Radar Test Simulator.

DOI: 10.5220/0005034506660673

In Proceedings of the 4th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH-2014),

pages 666-673

ISBN: 978-989-758-038-3

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

Figure 1: RTSim and radar environment.

• Scatter position and RCS (Radar cross section)

parameters,

• RCS type (constant, orientation/frequency depen-

dent),

• Multipath parameters,

• Scintillation parameters (swerling type and corre-

lation time),

• Jammer parameters (noise jammer, deceptive

jammer, activation time, etc.),

• Jet engine modulation, helicopter blade modula-

tion.

Radar parameters include:

• Transmitted power, maximum range,

• Antenna scan pattern (circular or RSPU con-

trolled),

• Antenna pattern tables,

• Waveform tables (for phase-coded signals),

• Pulse-repetition-interval (PRI) tables.

Environment parameters that can be programmed

from the user interface are: statistical clutter model

(distribution type, parameters), atmospheric attenua-

tion and rain attenuation.

Simulation parameters that are adjusted in the user

interface are sent to the embedded processors and FP-

GAs before the simulation starts. Radar and target

platforms’ navigation information is sent to the em-

bedded processors in real-time. Embedded processors

also receive radar beam parameters from RSPUs. The

beam message deﬁnes the parameters of the radar sig-

nal that RTSim generates:

• RF frequency and sampling rate,

• Antenna pattern table ID,

Figure 2: RTSim and radar environment.

• Waveform table ID,

Hardware-In-the-LoopRadarTestSimulator

667

• Pulse Width (PW) and Pulse-repetition-interval

(PRI),

• PRI table ID,

• Antenna orientation.

When the beam message is received embedded

processors calculate the parameters that FPGAs use

for signal generation. Parameters deﬁned in time units

(PW, PRI, delay, etc.) are converted to FPGA clock

units. Doppler calculations are performed for each

platform. Channel attenuations for each platform,

jammers and clutter are calculated and sent to the FP-

GAs. Servo and encoder model calculations are also

performed by embedded processors to determine the

orientation of the radar antenna. Encoder model con-

trols the ﬁxed speed circular motion of the antenna,

whereas servo model is for the RSPU controlled mo-

tion of the antenna.

FPGAs generate the radar signals using the pa-

rameters sent by the embedded processors. Radar

signal generator, receiver channels, clutter distribu-

tion generators, system noise generator, jammer noise

generators are all implemented in the FPGAs.

3 RTSIM MODELS

3.1 Target Modeling

Radar target generation approaches in the literature

has focused on statistical modeling of target RCS.

Jet Engine Modulation (JEM) and Helicopter Blade

Modulation (HBM) effects are also studied for mov-

ing target identiﬁcation (Sandhu and Saylor, 1985;

Carriere and Moses, 1988; Bell and Grubbs, 1993;

Phu et al., 1995). In RTSim, targets are modeled

as independent multiple scatters with different dy-

namic RCS tables. When the orientation of the tar-

get changes, the relative position of each scatter with

respect to the radar changes.

Dynamic RCS of targets is modeled by using the

RCS value that corresponds to the orientation (yaw,

pitch, roll) of the target. JEM and HBM are mod-

eled using correlated complex Gaussian distributed

signals. Target scintillation is modeled by four Swer-

ling models (Richards, 2005). Doppler effect is mod-

eled by adding an additional doppler phase value to

the phase codes of the radar signals at each clock cy-

cle of the FPGA.

Doppler speed, which is the velocity component

of the target in the direction of the radar, can be cal-

culated as follows:

= V

cos(φ)cos(θ)

+ V

sin(φ)cos(θ) +V

sin(θ) (1)

Figure 3: Multiple scatter modeling.

where φ is the azimuth and θ is the elevation angle

between radar and the target. Doppler frequency is

calculated using RF frequency (f) and speed of light

2f|V

(2)

The doppler phase value should be incremented

at speciﬁed intervals that depend on the doppler fre-

quency, to give the doppler effect. The doppler phase

is incremented by a speciﬁc amount at speciﬁc inter-

vals. The interval and increment values and the sign

of the velocity are sent to the FPGA as doppler pa-

rameters for each beam message. The interval is cal-

culated as the number of FPGA clock cycles:



× k

− 1)

+ 0.5



(3)

where f

is the sampling frequency, k is the in-

crement value, nb is the number of bits of the phase

codes. The value of k that gives the best frequency

resolution is determined by the following optimiza-

tion:

k = argmin



× x

− 1)

−



× x

− 1)





(4)

where x = 1,. .., 100.

Multipath effects are also modeled in RTSim.

Three multipaths can be used for each target platform.

Multipaths are modeled in the same way as the targets

but they have different complex attenuation constants

that depend on reﬂection angle, radar frequency and

the reﬂection surface (Skolnik, 2008).

SIMULTECH2014-4thInternationalConferenceonSimulationandModelingMethodologies,Technologiesand

Applications

668

Figure 4: Multipaths.

For each receiverchannel the received power from

a target platform is calculated as follows:

(4π)

(5)

where P

is the transmitted power, K

, K

are transmit

and receive antenna gains, λ is the wavelength, σ is

the RCS, R

is the distance between radar and target,

and L

denotes the losses (polarization, atmospheric,

rain). Using the received power the channel attenua-

tion is calculated as follows:

= e

− j4πR

/λ

× e

j(∠K

+∠K

)

× D

(6)

where D

is the default received power that corre-

sponds to the quantized signal amplitude D

. These

two parameters are used to adjust the dynamic range

of the 16-bit baseband IQ signal.

3.2 Jammer Modeling

The purpose of electronic warfare is to control the

electromagnetic spectrum. RTSim employs differ-

ent electronic attack ( electronic counter measures

) techniques to test the radar’s performance under

difﬁcult scenarios. RTSim models spot, barrage,

swept spot noise jammers, and Range/Velocity Gate

Pull off/in (RVGPO/I) deception techniques (Schle-

her, 1999; Kalata and Chmielewski, 1997; Greco

et al., 2006; Neng-Jing and Yi-Ting, 1995; Jing et al.,

2011; Townsend, 2008). Antenna gain, transmitted

power, bandwidth and center frequency parameters

can be adjusted for noise jammers. For swept spot

noise different frequency patterns can be deﬁned.

RGPO is implemented by adjusting the delays

and PRIs of pulse burst radar waveforms. A sample

RGPO scenario is given in Figure 5. When VGPO is

applied, velocity difference proﬁle is deﬁned instead

of range difference. For each burst, range or veloc-

ity pull off/in amounts are calculated at 16 different

Figure 5: RGPO scenario.

points in the burst. It is observed that this resolution

is satisfactory for 200 MHz sampling rate.

RGPO range difference from the beginning of a

segment is calculated as follows:

= R

+ S



× t +

× t



(7)

where t is the time since the beginning of the segment,

is the initial range difference, S

is the sign of the

range pull (off=1,in=-1),V

(m/s) is the pull velocity,

(m/s

) is the pull acceleration, J

(m/s

) is the pull

jerk, and R

is the ﬁnal range difference. For each

pulse burst the RGPO segment is determined and R

values are calculated. Using these values PRI differ-

ence values that will be added to the radar PRI are

calculated as follows:

∆P(m) =



2(R

(m) −R

(1))

× F



m = 1,.., M (8)

where c is the speed of light, F

is the sampling rate.

The delay for the generated RGPO/I signal is calcu-

lated as follows:

D =



+ 2R

(1)

× F



(9)

where R

is the distance between the radar and the

jammer. These calculations are performed on the em-

bedded processors and the results are sent to the FP-

GAs.

VGPO velocity difference from the beginning of a

segment is calculated as follows:

= V

+ S



× t +

× t



(10)

where V

is the initial velocity difference, S

is the

sign of the velocity pull (off=1,in=-1), V

(m/s

) is

the pull velocity, a

(m/s

) is the pull acceleration,

and V

is the ﬁnal velocity difference. Using these

differences “phase counter (Φ

)” and “phase incre-

ment (Φ

)” values are calculated:

(m) =

× |V

(m)|

(m) =



× Φ

(m)(2

− 1)



(11)

Hardware-In-the-LoopRadarTestSimulator

669

where f

is the doppler frequency, f

is the radar

RF frequency. VGPO is modeled by changing the

phase of the baseband signal. After Φ

(m) clock cy-

cles phase difference is increased by Φ

Coordinated RVGPO/I signal generator is shown

in Figure 6. For coordinated implementation parame-

ters should be set as V

= V

, V

= a

, and a

= J

The same signal generator can be used to generate tar-

get signals as well.

Figure 6: RVGPO/I Signal Generator.

3.3 Clutter Modeling

RTSim implements statistical clutter models.

Rayleigh, Weibull and K-distributions are supported.

In Figure 7 a typical clutter scenario is described.

For each clutter patch a random number is generated

corresponding to its range bin. The attenuation for

clutter signal is determined by the patch area, grazing

angle and distance from radar. The parameters of the

random distributions depend on the grazing angle,

polarization, radar frequency, surface’s dielectric

constant and conductivity.

Figure 7: Clutter Scenario.

Random clutter samples are generated using

inverse CDF (Cumulative Distribution Function)

method. In this method CDF of the distribution is

generated as a table, then a uniform number is gener-

ated in the interval [0 1], the index of the CDF table

that this number falls into is selected as the desired

random number. The FPGA implementation of clut-

ter distribution generator is shown in Figure 8. CDF

table contains 255 elements of 32-bit numbers which

are obtained by quantizing the CDF function of the

distribution. Uniform numbers are also 32-bits and

the generated random numbers are 8-bits.

Figure 8: Clutter Distribution Generator.

4 EXPERIMENTAL RESULTS

In the experiments the IQ signals generated by the

FPGAs are compared to the theoretical calculations.

In the ﬁrst example there are four targets at distances

1343, 1460, 1656, and 1814 meters and moving with

speeds 29.83, -309.10, -347.19, -833.52 m/s. The

radar parameters are, PRI = 560 samples, PW = 100

samples, number of pulses in each burst is 64. In Fig-

ure 9 the I and Q signals generated by the simula-

tor are displayed. These signals are analyzed using

range doppler matrices. The ranges and velocities of

detected targets are shown in Figure 10. The results

agree with the input parameters.

In Figure 11 sample RGPO and target signals are

given. For this example range gate pull of is applied

with V

= 500 m/s. Radar signal parameters are; PRI

= 100 µs, pulse width = 20 µs, chip width = 200 ns,

number of pulses = 500 and sampling rate = 10 MHz.

As seen in the ﬁgure in approximately 1.5 seconds 50

samples pull off is applied, and this corresponds to

750 meters range difference at 5 µs.

SIMULTECH2014-4thInternationalConferenceonSimulationandModelingMethodologies,Technologiesand

Applications

670

(a) I

(b) Q

Figure 9: I and Q samples for four targets.

In Figure 12 spectrum estimations for target and

VGPO signals are displayed. For this example a ve-

locity gate pull off proﬁle with V

= 150 m/s

is ap-

plied. Radar signal parameters are; PRI= 30 µs, radar

frequency = 10 GHz, number of pulses = 128, and

sampling rate = 200 MHz. Using these parameters

the maximum unambiguous velocity is 250 m/s, and

velocity resolution is 7.8 m/s. As seen from the ﬁg-

ure in 0.5 seconds the pull off rate is 71 m/s, and in

1 second it is 143 m/s, which are consistent with the

VGPO proﬁle.

In Figure 13 the RGPO signal generated by RT-

Sim is analyzed and the results are compared with the

real constant acceleration proﬁle. RGPO constant ac-

celeration parameter is set to a

= 1000 m/s

. The

generated signal has 10000 bursts each of which has

a pulse of 100 µs. The sampling rate of the signal is 10

MHz. As can be seen from the ﬁgure, the generated

signal’s proﬁle is consistent with the actual proﬁle ex-

cept for some quantization errors.

In Figure 14 the VGPO signal generated by RT-

Sim is analyzed and the results are compared with the

Figure 10: Processed pulses for four targets.

Figure 11: Sample RGPO application. Scenario initializa-

tion, after 0.5 ve 1.5 seconds.

real constant acceleration proﬁle. VGPO constant ac-

celeration parameter is set to a

= 1000 m/s

. The

generated signal has 130 bursts each of which has 128

pulses of 30 µs. As can be seen from the ﬁgure, the

generated signal’s proﬁle is consistent with the actual

proﬁle except for some quantization errors.

The ﬁnal experiment is clutter generation. K-

distributed clutter with parameters shape = 4 and scale

= 2.06 is generated. Radar signal has four pulses each

with 10k samples at 40 MHz sampling frequency.

In Figure 15(a) amplitude of the generated clutter

samples is displayed. The histogram of the sam-

ples is analyzed and compared with the histograms

Hardware-In-the-LoopRadarTestSimulator

671

(a) Target Return (b) VGPO Return t = 0.0 sec

Figure 12: Sample VGPO Application.

Figure 13: RGPO constant acceleration proﬁle and analysis

result.

Figure 14: VGPO constant acceleration proﬁle and analysis

result.

of Rayleigh, Weibull and K-distributions and then the

closest histogram is selected as the true histogram.

The estimated parameters are shape = 4 and scale =

2.11 which are in close agreement with the true pa-

rameters. Histogram plots are shown in Figure 15(b).

(a) Amplitude samples

(b) Histogram

Figure 15: Amplitude and histogram samples for clutter.

5 CONCLUSION

In this work, a real-time hardware-in-the-loop radar

target and environment simulator, RTSim, is de-

scribed. The simulator can be used to test radar signal

processing units even during the early stages of de-

velopment. RTSim models moving and stationary tar-

gets, radars with multiple receiverchannels, jammers,

statistical clutter returns. RTSim is currently being

used in some radar development projects in Turkish

Defence Industry. Future work includes implementa-

tion of terrain-dependentmore realistic clutter models

and atmospheric propagation models.

REFERENCES

Andraka, R. and Phelps, R. (1998). An FPGA based pro-

cessor yields a real time high ﬁdelity radar environ-

SIMULTECH2014-4thInternationalConferenceonSimulationandModelingMethodologies,Technologiesand

Applications

672

ment simulator. In Military and Aerospace Appli-

cations of Programmable Devices and Technologies

Conference, pages 220–224.

Bell, M. and Grubbs, R. (1993). Jem modeling and

measurement for radar target identiﬁcation. IEEE

Transactions on Aerospace and Electronic Systems,

29(1):73–87.

Carriere, R. and Moses, R. (1988). Autoregressive moving

average modeling of radar target signatures. In IEEE

National Radar Conference, pages 225–229.

EW Simulation Technology (2013). Radar

Target & Environment Generator.

http://ewst.com.au/index.php?option=com

content&view=article&id=14&Itemid=28.

Greco, M., Gini, F., Farina, A., and Ravenni, V. (2006). Ef-

fect of phase and range gate pull-off delay quantisa-

tion on jammer signal. Radar, Sonar and Navigation,

IEEE Proceedings -, 153(5):454 –459.

Intersoft Electronics (2013). Radar Environ-

ment Simulator. http://www.intersoft-

electronics.com/HTML/RES.html.

Jing, Y., Mei-guo, G., and Yun-jie, L. (2011). Digital real-

ization of pull-off jamming in R-V dimensions. In 4th

International Congress on Image and Signal Process-

ing (CISP), volume 4, pages 2177 –2181.

Kalata, P. and Chmielewski, T. (1997). Range gate pull

off (RGPO): detection, observability and α- β target

tracking. In Proceedings of the Twenty-Ninth South-

eastern Symposium on System Theory, pages 505 –

508.

Neng-Jing, L. and Yi-Ting, Z. (1995). A survey of radar

ecm and eccm. Aerospace and Electronic Systems,

IEEE Transactions on, 31(3):1110 –1120.

Phu, P., Adler, E., Innocenti, R., and Paolella, A. (1995).

A test target generator for wideband pulsed doppler

radars. In IEEE MTT-S International Microwave Sym-

posium Digest, pages 973–975 vol.2.

Richards, M. (2005). Fundamentals of Radar Signal Pro-

cessing. McGraw-Hill.

Saab Sensis (2013). Radar Environmental Simulator

(RES). http://www.saabsensis.com/products/radar-

environmental-simulator-res/.

Sandhu, G. and Saylor, A. V. (1985). A real-time statistical

radar target model. IEEE Transactions on Aerospace

and Electronic Systems, AES-21(4):490–507.

Schleher, D. (1999). Electronic Warfare in the Information

Age. Artech House Radar Library. Artech House.

Skolnik, M. (2008). Radar Handbook, Third Edition. Elec-

tronics electrical engineering. McGraw-Hill Educa-

tion.

Technology Service Corporation (2013). Radar En-

vironment Simulators. http://www.tsc.com/Fact

sheets/Radar Simulators Fact Sheet.pdf.

Townsend, J. (2008). Improvement of ECM Techniques

Through Implementation of a Genetic Algorithm.

Master’s thesis, Air Force Institute of Technology.

Utteridge, E. J. (1987). Radar environment simulator. In

Radar - 87; Proceedings of the International Confer-

ence, pages 520–524.

APPENDIX

Hardware components of RTSim (white rack) and in-

terface simulator for RSPUs (gray rack) is shown in

Figure 16. RSPU interface simulator is developed to

test RTSim before integration with actual radar sys-

tems.

Figure 16: RTSim and RSPU-IS hardware components.

Hardware-In-the-LoopRadarTestSimulator

673