design of distributed hardware-in-the-loop simulation...

Indian Journal of Geo Marine Sciences

Vol.46 (11), November 2017, pp. 2314-2322

Design of Distributed Hardware-In-the-Loop Simulation System for

Underwater Acoustic Detection

Zhanlong Yang*, Hang Chen, Xinhong Wang & Hu Yang*

School of Marine Science and Technology, Northwestern Polytechnical University, Shaanxi 710072, China

[E-mail: [email protected], [email protected]]

Received 22 July 2015 ; revised 02 November 2016

Present a novel approach to address System evaluation and testing of underwater acoustic detection system. A

distributed Hardware-In-the-Loop (HIL) simulation environment is developed and a target signal simulator with acoustic

coupling device is proposed to provide acoustic signal for receiving array in detection system. With the HIL simulation by using

acoustic coupling device, the testing and validation of underwater acoustic detection system can be performed under air

conditions, which not only reduce development costs and risks efficiently, but also save the testing and evaluation time.

Experiments with the designed HIL simulation were carried out and simulation results show the feasibility and effectiveness of

the HIL simulation obtained with acoustic coupling device for underwater acoustic detection system.

[Keywords: Hardware-In-the-Loop, acousitc coupling, underwater acoustic detection, target signal simulator]

Introduction

Acoustic detection has received

considerable attention for target detection in

underwater applications1-4

. As the testing and

validation of underwater acoustic detection system

are becoming more and more important in the

design and development process, it has become

clear that we need a simple and effective approach

to reduce development costs and risks efficiently.

It is well known that computer simulation

is an effective approach for designing and testing

the actual system in laboratory. Currently,

extensive research is being carried out to develop

simulation platforms for underwater acoustic

detection system5-7

. Since the modeling and

simulating of acoustic target is vital for acoustic

detection, there have been two simulating paths to

generate the target acoustic signals and supply for

the acoustic detection system. One is supplying the

signal processing subsystem in acoustic detection

system with the generated electrical signals, such

as literatures6,7

. The other path of obtaining the

desired signals is to generate actual acoustic

signals in anechoic water tank5,8

. The main

drawback of the former is that the transducer array

is out of consideration in the testing and simulating

process, thus limiting the simulation accuracy due

to the ignorance of acoustic characteristics. The

methods in water tank suffer from the tremendous

challenges of expensive costs in experiments.

Hardware-in-the-loop simulation is a

technique for performing testing and evaluating

actual system in a comprehensive, cost effective

and repeatable manner. It has been proved to be a

reliable, effective, and nondestructive method in

the design, development, and testing of various

industrial systems. HIL simulation refers to

simulating a system by coupling physical models

of some of its components together with

mathematical models of its remaining

components9-10

. HIL simulation technology has

been widely applied in high investment and high

risk areas such as aviation11-12

, aerospace13-15

,

underwater vehicles16-18

, and large complex control

systems19-22

. On one hand, HIL simulation solves

difficulties of system model abstraction and the

problems that simplified models cannot effectively

simulate the real system in pure digital off-line

simulation. On the other hand, HIL simulation also

avoids long cycle and repetitive work of actual

system development and reduces development

costs and risks efficiently.

In this paper, a distributed HIL simulation

with acoustic coupling is presented for underwater

acoustic detection system, of which a preliminary

INDIAN J. MAR. SCI., VOL. 46, NO. 11, NOVEMBER 2017

version has been described in literature23

. First, the

actual hardware of transducer array is introduced

into the underwater acoustic detection simulation

to resolve the problem of hardness to model

transducer array; and then the detection system are

supplied with the actual acoustic signals by means

of acoustic coupling under air conditions rather

than in water tank. As the key part of the presented

HIL simulation, a type of target signal simulator

with acoustic coupling device is specially designed

for underwater acoustic detection system. The

presented HIL simulation achieves low costs and

can perform the functions of the underwater

acoustic detection under air conditions.

Materials and Methods

HIL Simulation System Design

The HIL simulation with acoustic

coupling device for underwater acoustic detection

system comprises three parts: target signal

simulator, acoustic coupling device, and acoustic

detection system. Fig.1 illustrates the schematic

diagram of the HIL simulation.

(1) Target signal simulator

Target signal simulator is responsible to

generate the simulated target signals for acoustic

detection system. In active detection mode, the

generated target signal comprises target echo,

reverberant, ambient sea noise, and interfering

signals. In passive detection mode, the target

signal comprises target radiated noise, ambient sea

noise, and interfering signals.

Target simulator is an important testing

device playing a critical role in sonar design,

manufacture and manipulation. Its basic function is

not only to generate various simulated signals,

such as target echoes and target radiated noise, for

hydrophone array, but also to provide these signals

to the sonar system for the purpose of functional

and performance testing.

The traditional target simulator is

developed on the DOS operating system, which

function is very poor. With the rapid development

of computer technology, CPU speed has been

greatly improved. The simulator can now be

directly implemented using computer simulation

technology, which previously can only be achieved

on an external circuit.

With the appearance of high performance

DSP, real-time signal processing system has been

used in widespread applications. Especially in

underwater acoustic detection system, real-time

processing is the key, and the multi-DSP system is

urgently needed with high speed A/D converter

and vast data processing. The designed target

simulator is implemented on a multi-DSP

platform. It uses some artificial signal generator to

simulate sonar signals existed in real-world, such

as echo, reverberation, target radiated noise, sea

noise, and other sensor signals, and combine them

to reach the same effect as the real world on the

analog interface. According to the transmitting

data rate and calculating time, TS201 is chosen to

implement the system. The ADSP-TS201 Tiger

Sharc processor is an ultrahigh performance, static

superscalar processor optimized for large signal

processing tasks and communications

infrastructure. It supports floating-point and fixed-

point processing, and also offers powerful features

tailored to multiprocessing DSP systems through

the external port and link ports. This

multiprocessing capability provides the big

bandwidth for inter-processor communication,

including up to 8 DSPs on a common bus, on-chip

arbitration for glueless multiprocessing and link

ports for point-to-point communication24-26

. Fig. 2

shows the details of signal processing board

architecture. On the designed DSP board, each

DSP has an independent external bus, and 256M

bytes exclusive external memory (SDRAM). DSPs

can exchange data through link ports, with the

highest speed of 600 Mbytes/s and it does not

Fig. 1: Schematic diagram of HIL simulation with acoustic coupling.

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YANG et al.: DESIGN OF DISTRIBUTED HIL SIMULATION SYSTEM

consume the time of DSP processors, so that data

transfer between the DSPs will have high

transmission rate. To communicate with the host,

all the DSPs and external memories are connected

to the CPCI (compact PCI) bus through shared

local bus and PCI interface controller. In this way,

the host can directly access the internal and

external memories of any DSPs. The simulated

target signals are converted to analog by D/A

converter and outputted to the acoustic coupling

device.

DSP1

DSP3

DSP5

DSP7

DSP2

DSP4

DSP6

DSP8

FPGA4

FPGA3

FPGA2

FPGA1

SDRAM

SDRAM

SDRAM

SDRAM

SDRAM

SDRAM

SDRAM

SDRAM

PCI Interface

CPCI Bus

DSP Board

Host

PC

PCI Interface

DSP9

D/A

Converter

D/A

Converter

D/A

ConverterD/A Board

Output 1

Output 2

Output N

Link to

DSP1

Fig. 2: Architecture of target signal simulator

A set of application software for the target

signal simulator with bottom-up layer style by

means of module design comprises a main control

software for the main control board, a DSP

software for the DSP board, and a D/A conversion

software for the D/A converter board. As the

upper layer, the main control board software

provides system management, while DSP

software and D/A conversion software perform

system functions as bottom layer of task. The

module design of the set of application software is

described in Fig. 3.

Main control software offers the parallel

processing system management and control to

initial system, load DSP programs, interactive

with DSP board, and provide operator

visualization. The general workflow of the main

control software is depicted in Fig. 4.

Fig. 3: Architecture of set of target signal simulator softwares

with modules.

Fig. 4: Workflow of main control software.

2316


Fig. 5: Workflow chart of target signal generation.

DSP software is the key part of parallel

processing. In order to adapt to the diversity of the

computing tasks, the system can set one DSP as

task manager and the others as the master slaver,

based on the topological structure of calculator. It

was also beneficial for realizing stream processing

between instructions and improving execution

efficiency. According to the module design of the

DSP software and the structure of DSP array, 6

pieces of DSP are used as core processor. Fig. 5

illustrates the workflow of the target signal

generation. There are three phases in the data

flow. In the first phase, each type of target signal

is generated respectively after calculating the

direction angle of target or jamming in certain

location with underwater acoustic detection

system. Then, the target echo (or target radiated

noise) and interfering signal are obtained by

combining the corresponding signals in the second

phase. At last, DSP-#1, as the master processor,

completes the generation of the target signal. In

the above, the tasks in the first phase can be

performed simultaneously to achieve distributed

parallel processing.

(2) Acoustic coupling device

Acoustic coupling device overall serves

the purpose of acoustically coupling the generated

target signals to the receiving transducer array

under air conditions. It consists of an acoustic

array, called acoustic coupling array, and a layer

of acoustic coupling material between the

coupling array and the receiving array. The

electrical signals generated from the target signal

simulator are converted into acoustic signals and

transmitted towards the receiving array through

the acoustic coupling medium, namely acoustic

coupling material. After that, the receiving array

converts the received acoustic signals into

electrical signals for further processing in the

acoustic detection system.

Fig. 6: (A) Acoustic coupling device, (B) Schematic diagram

of the acoustic coupling device.

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Acoustic coupling array is responsible for

converting electrical signals generated from the

target simulator into acoustic signals which is

transmitted towards the receiving array of the

detection system by way of the acoustic coupling

medium.

As an acoustic signal provider, the

coupling array is designed with the same

distributed elements as the receiving array. In this

way, the radiated signals are incident on the

surface of the receiving array in a normal

direction, which can reduce the effects of mutual

radiant impedance between elements. In addition,

for the transmission of sonic energy from a planar

array, the radiating elements take the form of

relatively small sonar transducers fabricated of a

piezoelectric material.

To keep coupling thoroughly, the

coupling surface of the coupling array must

conform to the surface of the receiving array. In

the presented HIL simulation, the surface of the

receiving array and the coupling array are both

plane. The coupling array is attached closely to

the receiving array by the tightening device, as

shown in Fig. 6 (B).

A layer of acoustic coupling material lies

between the surfaces of the coupling array and the

receiving array, which is the propagation medium

for the acoustic signals under air conditions. In

order to obtain the complete transmission of an

acoustic wave, two problems need to be

considered for the coupling material layer. One is

the characteristics of the coupling material and the

other is the thickness of the layer.

As illustrated in Fig. 6, target acoustic

signals generated from the elements pass from the

coupling material into the sound-permeable

membrane of the receiving array. When an

acoustic wave traveling in one medium encounters

the boundary of another medium, reflected and

transmitted waves are generated. Assume the

thickness of the coupling material layer is

described as 2D and its acoustic impedance is

222 cR (1)

where 2 is the density of the coupling material

and 2c is the velocity of propagation in the

coupling material. The thickness of the sound-

permeable membrane layer is 1D and its acoustic

impedance is

111 cR (2)

where 1 is the density of the sound-permeable

membrane and 1c is the velocity of propagation in

the sound-permeable membrane.

Therefore, the intensity transmission

coefficient It27

is given as

2

12

12

2

12

12

)1(

4

)(

4

R

R

RR

RRtI (3)

and the intensity reflection coefficient Ir27

can be

written as 2

12

12

2

12

1

1

11

R

R

RR

RRtr 2II (4)

where 1212 / RRR .

Note that when 21 RR then 1It ,

0Ir , and there is complete transmission. This

means that the acoustic impedances of the

coupling material and the sound-permeable

membrane are the same. As polyurethane rubber

is commonly provided as the material of a sound-

permeable membrane to coincide with seawater,

the same type of rubber is considered for the

choice of coupling material. As a result, the

coupling layer has the same transmission

characteristics as seawater.

Because the coupling material layer and

the sound-permeable membrane have the same

characteristics, they can be seen as one layer.

Thus, the thickness of this layer is 21 DDD .

Now we discuss the reflection and transmission of

a plane wave normally incident on a layer of

uniform thickness. When an incident signal in

material 1 arrives at the boundary between

material 1 and 2, some of the energy is reflected

and some transmitted into the second material.

The portion of the wave transmitted will proceed

through material 2 to interact with the boundary

between material 2 and 3, where again some of

the energy is reflected and some transmitted. If the

final material is the same as the initial material,

the intensity transmission coefficient It27

is given

as

DkRRt

2

22

2112 sin)(4

4

I (5)

where 1212 / RRR , 2121 / RRR , 22 /ck is

the wave numbers of the acoustic signal in

material 2, is the frequency and 2c is the speed

of sound in material 2. From Eq. (5), we see that

the maximum of It occurs approximately when

the item of Dk2sin is a minimum, namely Eq.

(6) is obtained.

0sin 2 Dk (6)

That is

),3,2,1(2 nnDk (7)

2318


Thus,

,2,1,2/2 nnD (8)

If 1n , then

2/2D (9)

Because 12 DD , we can obtain

2/22 D (10)

In order to achieve total transmission, it is

required that the thickness of the coupling

material layer is approximately the half of the

wavelength of acoustic radiant in coupling

medium.

The acoustic detection system is to

determine the presence, location or nature of

targets simulated with the target simulator by

“underwater” sound propagation. It involves a

transducer array and a signal processing

subsystem. The signal processing system

comprises signal conditioner, beam former,

detection/estimation statistics processor, etc28

. In

the presented acoustic detection system, the

transducer array is only used for receiving target

acoustic signals and two types of detection mode

are considered in the target simulator.

Results and Discussion

First, the performance of the acoustic

coupling device is tested under air conditions. An

input signal was applied to the elements of

coupling array, and then the output signal can be

obtained from elements of receiving array. Due to

the large number of array elements, Tab.1 only

shows the partial measured output voltages for a

22 kHz and 10 V peak-to-peak input sinusoid. As

shown in Tab.1, the received voltages are about

30 dB lower than the transmitted voltages because

of the voltage loss during the conversion of

electrical signal into acoustic signal in the

coupling array and acoustic signal into electrical

signal in the receiving array. Due to the tiny

difference of the thickness of the coupling

material layer between array elements, the

received voltages vary in a small range. However,

as will be noted, the measured peak-to-peak

voltages for each array element almost preserved

uniformity.

Simulation experiments are carried out

with the developed HIL simulation for underwater

acoustic system. In the experiments, the simulated

target signals are generated by target signal

simulator and transmitted to the receiving array by

means of acoustic coupling through the coupling

material layer for further processing with the

acoustic detection system.

Table.1 Receiving Response of acoustic coupling array

Receiving Array Vpp (mV)

1 375.0

2 293.8

3 306.3

4 350.0

5 293.8

6 312.5

7 256.3

8 281.3

9 325.0

10 293.8

Fig. 7: Target signals generated from target signal simulator in active detection mode.

Fig. 7 shows the combined target signals

from target signal simulator in active mode. As

huge data volumes, only the first four channels of

the target signal waveforms are displayed in Fig. 7

2319


and also extracted on scale of 100 : 1. As shown

in Fig. 7, the reverberation is arrived first, and the

target echo follows.

Fig. 8 shows the beam signals from signal

processing system in underwater acoustic

detection system. Split beam forming is used in

the signal processing system. As shown in Fig. 8,

the first two channels are signals of left beam and

right beam in horizontal direction and the last two

channels are signals of up beam and bottom beam

in vertical direction.

In Fig. 7, the coordinates of the target and

the UUV (Unmanned Underwater Vehicle) of the

acoustic detection system are as follows: target (-

1588.72, -5, -6349.82) m and UUV (-1404.93, -

7.03, -5488.64) m. The vehicle speed of the target

and the UUV is 18 kn and 50 kn respectively. The

current course of the target and the UUV is 270

deg and 186.85 deg respectively. The real value of

the horizontal angle, vertical angle, and relative

distance of the target calculated in terms of the

coordinates of the target and the UUV are 5.20

deg, 0.13 deg, and 880.58 m, respectively. The

real value of relative speed of the target calculated

according to the speed and course of the target and

the UUV is 48.90 kn.

Fig. 8: Beam signals from signal processing system in active detection mode.

Fig. 9: Target signals generated from target signal simulator in passive detection mode.

2320


Fig. 10: Beam signals from signal processing system in passive detection mode.

In active detection mode, the target

distance can be calculated in the light of the time

delay between the echo signal and the transmitted

signal. The target direction can be recognized with

the normal wave direction, while the target radial

velocity can be inferred by the frequency shift

between the echo signal and the transmitted

signal. As shown in Fig. 8, the estimated values of

the horizontal angle, vertical angle, relative

distance, and relative speed are 2.49 deg, 0.09 deg,

894.72 m, and 40.77 m/s, respectively.

Considering the reasonable error of estimation, the

experimental results show that the developed HIL

simulation with acoustic coupling for underwater

acoustic detection system is reasonable and verify

the feasibility of the presented HIL simulation by

means of acoustic coupling.

Fig. 9 shows the target signals generated

from target signal simulator in passive mode. For

the same reason, only the first four channels of the

target signal waveforms are displayed and also

extracted on scale of 100:1. Fig. 10 shows the

beam signals from signal processing system in

passive detection mode. In passive detection

mode, passive sonar system only listens without

transmitting any signals. It has no reverberation

and its noise mainly comes from self-noise and

environmental noise. It detects targets by

receiving radiation noise from an underwater

target. So as shown in Fig. 10, the target direction

can be recognized but the target distance and the

target radial velocity cannot be obtained.

Conclusions

The paper presented a distributed HIL

simulation with acoustic coupling for underwater

acoustic detection system. A particular approach is

described to provide the receiving transducer array

with acoustic signal under air conditions. The

underlying idea is to introduce a target signal

simulator with acoustic coupling device into the

simulation and the receiving array can be supplied

with the coupled acoustic signal from acoustic

coupling device. The developed HIL simulation

with low cost can minimize the time of research

on actual acoustic detection system and provide

valuable reference in other underwater acoustic

applications.

Acknowledgement

This work was supported in part by the

National Natural Science Foundation of China

(No. 61540047) and the Specialized Research

Fund for the Doctoral Program of Higher

Education of China (No. 20136102120018).

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