Nghiên cứu giải pháp nâng cao chất lượng mảng cảm biến thu tín hiệu thủy âm trong vùng biển nông tt tiếng anh

MINISTRY OF EDUCATION AND TRAINING

MINISTRY OF DEFENSE

ACADEMY OF MILITARY SCIENCE AND TECHNOLOGY

***************

PHAN HONG MINH

RESEARCHING METHODS TO IMPROVE THE
QUALITY OF AN UNDERWATER SENSOR ARRAY
RECEIVING SIGNALS IN SHALLOW WATER AREAS

Specialization : Electronic Engineering
Code No : 9 52 02 03

SUMMARY OF TECHNICAL DOCTORAL THESIS

Ha Noi – 2020


The thesis is completed at:
Academy of military Science and Technology

Scientific Supervisor:
1. Dr Phan Trong Hanh
2. Dr Vu Van Binh

Reviewer 1: Prof. Dr Vu Van Yem
Hanoi University of Science and Technology

Reviewer 2: Assoc. Prof. Dr Do Quoc Trinh
Military Technical Academy
Reviewer 3: Dr. Vu Le Ha
Academy of military Science and Technology
The thesis will be defended before approval committee at :
Time
, date
month year 2020

This thesis may be found at:
- Library of Academy of Military Science and Technology
- The Vietnam National Library.


LIST OFPUBLICATIONS
1) Phan Hong Minh, Phan Trong Hanh, Luong Thi Ngoc Tu,
“Configuration of hydrophone array based on ICA pre-processing to
enhance accuracy position of multi-targets”, Journal of Military
Research and Technology, No. 48, 04/2017.
2) Phan Hong Minh, Phan Trong Hanh, Vu Van Binh, Nguyen
Cong Dai, “The Solution of configuration 2D hydrophone array based on
beamforming option”, Journal of Military Research and Technology, No.
54, 04/2018.
3) Le Ky Bien, Phan Hong Minh, Tran Hieu Thao, Phan Trong
Hanh, “The solution of signal processing for sonobouy systems detection
and identification based on passive sonar”, The National conference
"High-tech applications in practice" 2018, Journal of Military Research
and Technology, No. Special issue, 08/2018.
4) Phan Hong Minh, Phan Trong Hanh, Vu Van Binh, “Multichannels blind deconvolution of shallow underwater signals based on
Feed-Foword neural networks”, Journal of Military Research and

Technology, No. 62, 08/2019.


1
INTRODUCTION
1. Necessity of the thesis
The sea is especially important for national defense and security, for
socio-economic development and integration with the world. All
countries having seas must have their own plans and solutions to protect
the safety of their waters, islands and territorial waters. Safely protecting
coastal, military bases and archipelagos, detecting target identification to
prevent underwater targets from the sea is necessary.
2. Obiectives of the study
Research and develop solutions to improve the signal quality of the
underwater sensor array for sonar systems and passive positioning
devices to enhance the ability to detect and locate targets as sources
underwater sound in the shallow sea.
3. The main results, scientific significance and practical meaning of
the thesis
3.1. The main results
1) Proposed structural model of the retangular acoustic sensor
array, combined with a customized adaptive beamforming solution,
increased the gain of the sensor array.
2) Proposed a model and solution for complex underwater signal
processing on the basis of combining independent component analysis
(ICA) and multi-channel blind analysis (MBD) to improve SNR ratio in
shallow water areas.
3.2. Scientific significance and practical meaning of the thesis.
The research to improve the quality of the underwater sensor array
for sonar systems, passive positioning devices for underwater acoustic

emission targets is carried out on the basis of structural solutions and
signal processing for shallow waters is to solves scientific and practical
requirements.
Research results of the thesis with the proposal of a solution of array
structure and adaptive beamforming and a solution of complex acoustic
signal processing based on the combination of two signal processing
techniques ICA and MBD will contribute more theoretically to the field
of hydrodynamic positioning. At the same time, these research results
are related to the conditions and characteristics of Vietnam's territorial
waters, so it will be a good basis and orientation when designing sonar
systems or underwater positioning devices in Vietnamese.


2
CHAPTER1: UNDERWATER SENSOR ARRAY AND PROBLEM
TO IMPROVE QUALITY ARRAY IN THE SHALLOW WATER
1.1 Overview of underwater sensor array
1.1.1 Model of sensor array
The sources of interest in sonar and ultrasound are the
narrowband and wideband applications that satisfy the wave
transmission equation in [31], [37], and their spatial properties can be
independently separated. Therefore, the measurement of the 𝑧 𝑟, 𝑡 is
stimulated by negative sources that can determine the time-space
response 𝑥 𝑟, 𝑡 . The vector 𝑟 is the relative position of the sensor and
the sound source, t is the time.

Figure 1.1 Space-Time Model receiving signal of sensor array
Response output 𝑥 𝑟, 𝑡 is convolution of 𝑧 𝑟, 𝑡 and response of
sensor array ℎ 𝑟, 𝑡 .
(1.1)

𝑥 𝑟, 𝑡 = 𝑧 𝑟, 𝑡 ⊗ ℎ 𝑟, 𝑡
There 𝑧 𝑟, 𝑡 is defined are input of receiver, and is convolution of
acoutic souce parameter 𝑦 𝑟, 𝑡 with underwater environment Ψ 𝑟, 𝑡 .
(1.2)
𝑧 𝑟, 𝑡 = 𝑦 𝑟, 𝑡 ⊗ Ψ 𝑟, 𝑡
1.1.2 Sensor array and underwater passive sonar system
Model of structure system
The sonar system is a system of devices that determine the
position of the sound source in the space under the sea surface.
Depending on the application and different characteristics, the system


3
has the form: mobile or fixed. The basic structure model of a passive
sonar system with M sensors can be described according to the progress
of the identification detection information as follows (Figure 1.2):

Figure 1.2: Model of underwater passive sonar system
The accuracy positioning of sound source
𝜎𝑝 𝐷 =

2
2
2
𝜎đ𝑡
𝐷 + 𝜎𝑚𝑡
𝐷 + 𝜎𝑡𝑛
𝐷

𝜎𝑖2 𝐷

(1.7)

𝑖

1.2 Shallow water and characteristic
1.2.1 The concept of shallow sea
1.2.2 Multi-path effect in the shallow sea

Figure 1.3: Multipath trajectories in an isvelocity shallow water
configuration. (A) direct path; (B) Reflection on the surface; (C)
Reflection on the bottom and surface


4
For shallow waters the transmission environment is limited by
the sea surface and the seabed, the signal propagation is reflected many
times before go to the receiver. According to the experimental results of
Lurton [37] in Figure 1.3a, the path of the negative rays in shallow water
is reflected many times, Figure 1.3b shows the multi-path effect of
measuring signals in real time domain.

Hình 1.4: Simulate multi-path with 5 acouctic path

Figure 1.5: Receiving pulse in the shallow water
Figure 1.4 illustrates the sound channel in shallow water affected
by the multi-path with 5 rays: sound speed is 1520 m/s, depth of channel
is 100m, source with coordinates [0,0, -60], receiver 1 has coordinates
[500,0, -40], receiver 2 has coordinates [500, 1000, -70], isotropic
sources and direct and reflected sound at the bottom have a loss of

0.5dB.


5
The isotropic source generates a pulse of 13.2ms width into the
audio channel with 5 rays received at the receiver. In Figure 1.5, the
signal receives multiple echoes generated by reflected sound rays, which
interfere with each other. Thus, in the shallow sea, the effect of multipath effect on signal quality is enormous.
To solve this problem, there are several solutions such as: The
first is design the geometric structure of the array to increase the gain of
the receiving array. The second is beamforming of the sensor array so
that the main beam is directed towards the direct beam while the signal
coming from the other directions is noise, in order to increase the SNR.
Thirdly, solution DSP to recontruct signal. These solutions are discussed
in detail in the following sections.
1.2.3 Parametric effect of shallow sea on the quality of passive sonar
system
1.3 Solutions to improve the quality of sensor array.
1.3.1 Optimize the geometric structure of the array
1.3.2 Beamforming sensor array
1.3.3 Signal processing array sensors

Figure 1.7: Block diagram of sensor signal processing array system
Signal processing underwater sensor array is an extended
concept including processing sonar sensor, underwater communication
network ... including functional blocks such as ADC conversion, FIR
filtering, Adaptive LMS filter, Kalman, adaptive noise suppression,
linear adaptive enhancement, DEMON / LOFAR analysis, FFT / MUSIC
spectrum analysis, target detection (torpedoes, submarines, strange ships,
clones, fish stocks, etc.), target identification, of SNR of the array,

recording, tracking, etc. (Figure 1.7).
1.4 The problem of improving the quality of the underwater
sensor array and the research direction of the thesis
1.4.1 Related studies have been published
Researches in our country,
International pulication
1.4.2 Requirements and research directions of the thesis


6
From scientific requirements and practical requirements on the
quality of sensor arrays, based on the theory of electronic and
engineering, the thesis aims at the following tasks:
- Develop solutions to improve the quality of the underwater
sensor array by the method of customized beamforming;
- Develop a solution to improve the receiving signal quality of
the underwater sensor array using a customized complex signal
processing method (Figure 1.9).

Figure 1.9: Proccesing signal model to impove quality of sensor array
1.4.3 Researching of the thesis
The research problem was raised to propose a solution to improve
the quality when working in shallow sea environment, characterized by the
multi-path effect and high noise. In order to solve the above, it is necessary
to fix the following issues: The first is to study a customized
beamforming, combining conventional and adaptive control mail lobe in
order to improve the SNR ratio of the sensor array. The second is to
research a solution processing suitable to the structure of the underwater
sensor array based on the combination of ICA technology and the solution
of multi-channel blind deconvolution by neural network into processing

sensor signal array to restore original signal.
2
CHAPTER 2: SOLUTION TO IMPROVING SIGNAL
QUALITY BASED ON CUSTOMIZE BEAMFORMING ARRAY
2.1 Beamforming sensor array
2.1.1 Linear beamforming
Considering the array of sensors (Figure 2.2), there are N sensors
placed along the z axis with equal spacing and d (ULA - Uniform Linear
Arrays). Put array in the center of the coordinate system; sensor positions
𝑁−1
(2.8)
𝐩𝑧𝑛 = 𝑛 −
𝑑
𝑛 = 0, 1, 2, … , 𝑁 − 1
2


7
𝐩𝑥 𝑛 = 𝐩𝑦𝑛 = 0

(2.9)

Figure 2.2: Linear array along z-axis
Where:

𝑁−1
𝐻

𝐰𝑛∗ 𝑒

ϒ 𝜔, 𝑘𝑧 = 𝐰 𝐯𝐤 𝑘𝑧 =

𝑁−1
−𝑗 𝑛−
𝑘𝑧 𝑑
2

𝑛=0
𝑁−1

𝐵𝜓 𝜓 = 𝐰 𝐻 𝐯𝜓 𝜓 =

𝑁−1
−𝑗
𝜓
2
𝑒

𝑤𝑛∗ 𝑒 𝑗𝑛𝜓 , −
𝑛=0

(2.13)

2𝜋𝑑
2𝜋𝑑
≤𝜓≤
𝜆
𝜆

(2.26)

We now restrict our attention to the uniform weighting case,
1
(2.29)
𝑤𝑛 = ,
𝑛 = 0, 1, … , 𝑁 − 1
𝑁
We can also write (2.29) as
1
(2.30)
𝐰= 𝟏
𝑁
where 1 is the Nx1 unity vector. Thus, the frequency-wavenumber
function can be written in ψ –space.
ϒ.𝜓 𝜓 =
as

1
𝑁

𝑁 −1

1 −j 𝑁 −1 𝜓
2
e
𝑁
𝑁 −1
𝑗𝑁𝜓
1 −j
𝜓 1−𝑒

2
e
𝑁
1−𝑒 𝑗𝜓

𝑁−1 𝑗 𝑛− 2
𝑛=0 𝑒

𝜓

=

𝑁−1 𝑗𝑛𝜓
𝑛=0 𝑒

=

(2.31)


8
𝑁−1

𝑥𝑛 =
𝑛=0

1 − 𝑥𝑛
1−𝑥

or

𝜓
1 𝑠𝑖𝑛 𝑁 2
ϒ.𝜓 𝜓 =
(2.32)
,
−∞ ≤ 𝜓 ≤ +∞
𝑁 𝑠𝑖𝑛 𝜓
2
We observe that ϒ.𝜓 𝜓 is periodic with period 2π for N odd. If N is
even, the lobes at ±2π, ±6π are negative and period is 4π. The period of
|ϒ.𝜓 𝜓 | is 2π for any value of N.
𝑑

ϒ 𝑤: 𝑘𝑧 =

1 𝑠𝑖𝑛 𝑁𝑘 𝑧 2
𝑁 𝑠𝑖𝑛 𝑘 𝑧 𝑑

, −∞ ≤ 𝜓 ≤ +∞

2

(2.34)

ϒ 𝑤: 𝑘𝑧 is periodic with period 2π/d.
Note that the response function depends only upon the wavenumber
component kz and is periodic with respect to kz at intervals of 2π/d.
So that beam lobe in ψ space is:
𝜓

1 𝑠𝑖𝑛 𝑁 2
2𝜋𝑑
2𝜋𝑑
(2.37)
𝐵𝜓 𝜓 =
,
−
≤𝑢≤
𝑁 𝑠𝑖𝑛 𝜓
𝜆
𝜆
2
Simulate uniform linear array beamforming in polar and 3D.

Figure 2.6: ULA beamforming ϒ(ψ) in polar (dB)


9

Figure 2.7: ULA beamforming ϒ(ψ) in 3D space
2.1.2 Beamforming sensor array with different geometry
2.2 Adaptive Beamforming sensor array
2.2.1 Model and method adaptive beamforming
2.2.2 Frost adaptive beamforming
2.3 A solution to solve multi-path based on a customized array of
beams
2.3.1 Rectangular customized beamforming
On the basis of rectangular array of NxM hydrophone, building
calculation models and designing beam for arrays based on manifold
vectors and weighted arrays [17]. The beam of a rectagular array with a

source at position p(r,θ,ϕ) is calculated as follows:
𝐵 𝜓𝑥 , 𝜓𝑦 = 𝑒

−𝑗

𝑁−1
𝑀−1
𝜓𝑥 +
𝜓𝑦
2
2

𝑁−1 𝑀−1

𝒘∗𝑛𝑚 𝑒 𝑗

𝑛𝜓 𝑥 +𝑚 𝜓 𝑦

(2.49)

𝑛=0 𝑚 =0

Where:

2𝜋
2𝜋
𝑑𝑥 𝑠𝑖𝑛𝜃𝑐𝑜𝑠𝜙,
𝜓𝑦 =
𝑑 𝑠𝑖𝑛𝜃𝑠𝑖𝑛𝜙
𝜆

𝜆 𝑦
If array is uniform with dx = dy = λ/2 và N x M = 5 x 7 the beam with is
lobe Figure 2.20. The manifold vectors is mth row along the y-axis of the
retangular array is calculated:
𝑒 𝑗 (𝑚 𝜓 𝑦 )
𝑗 (𝜓 𝑥 +𝑚 𝜓 𝑦 )
𝑒
(2.50)
𝒗𝑚 (𝜓) =
⋯
𝑒 𝑗 ((𝑁−1)𝜓 𝑥 +𝑚 𝜓 𝑦 )
𝜓𝑥 =


10

Figure 2.20: Geometry and5x7 rectangular array beamforming
thus, for the all array, we have a manifold matrix with NxM hydrophone
as follows:
𝜓𝑥
(2.51)
𝑽𝜓 𝜓 = 𝒗0 𝜓 ⋮ ⋯ ⋮ 𝒗𝑀−1 𝜓 T, véc tơ 𝝍 = 𝜓
𝑦
from this, it is possible to define a generalized vector by folding in turn
to have a vector NM x 1 value.
𝒗0 (𝜓)
⋯
(2.52)
𝑣𝑒𝑐 𝑽𝝍 𝜓 =
𝒗𝑀−1 (𝜓)

The same is true for the matrix of weights of a rectangular array we have
𝑾 = 𝒘0 ⋯ 𝒘𝑚 ⋯ 𝒘𝑀−1 , with m^th row 𝒘𝑚 =
𝒘0
𝑤0,𝑚
⋯
𝑤1,𝑚
𝒘𝑚
(2.53)
and
𝑣𝑒𝑐[𝑾]
=
(2.54)
⋯
⋯
𝑤𝑁−1,𝑚
𝒘𝑀−1
Thus:
𝐵 𝜓 = 𝐵 𝜓𝑥 , 𝜓𝑦 = 𝑣𝑒𝑐 H [𝑾]𝑣𝑒𝑐 𝑽𝜓 𝜓
(2.55)
is an overview format to design an NxM hydrophone all planar array.
2.3.2 Calculate and customize arrays to reduce multi-path effect
- Calculating arrays to enhance signals when the target is approaching
Consider the ULA of 30 hydrophones to observe the target from
afar into the array, the array can be customized as follows:
+ A ULA of 30 hydrophone: Array gain GA = 30dBi, The figure shows
that the main-lobe is very narrow and pointed, the side-lobe are
suppressed, when looking at the distant target, it is good (Fig.2.25).


11

Figure 2.25: Linear beamforming 30 hydrophones
+ Three independent linear arrays each with 10 hydrophone arrays: GA =
G1 + G2 + G3 = 30 dBi. The simulation shows that the main-lobe are
larger, the side-lobe also increase, but ensuring the gain (Figure 2.6).

Figure 2.26: Linear beamforming 3 arrays each 10 hydrophone
+ one vertical array in the middle and two customizable segments
independently rotated by 10 degrees (Figure 2.27):

Hình 2.27: Beamforming one vertical and two rotated by 10o
When observing a distant target, the signal field to the array is
parallel, the first two cases are well observed. When the target comes
near, both of the above arrays are much worse. To calculate the
attenuation, consider the magnitude of the main beam at 3dB (the halfpower beamwidth, HPBW).
According to [17] the half-power beamwidth main-lobe:


12
0.886𝜆
𝜆
(2.56)
𝑟𝑎𝑑 ≈ 50
(𝑑𝑒𝑔𝑟𝑒𝑒)
𝑁𝑑
𝑁𝑑
With 3 arrays, each designed 10 hydrophone 50m distance (d =
50m) observed frequency f = 15Hz (λ = 100m), assuming the sound
velocity in water c = 1500 m/s. We have HPBW ≈ 10O. So the distance R
= 550/sin10O = 3167 m.

Thus, when the target near the array to a distance of 3167m, in the case
of 2 the gain will decrease and GA = G1 /2 + G2 + G3 /2 dB, the further
the gain decreases. In the case of 3 custom arrays that have been rotated
to 10O, when the target is close, the gain is still constant.
- Customized array to optimize reception of the desired signal
Consider the rectangular array of 10x10 hydrphone which
assumes that the desired signal comes from the direction of 28O, the
noise signal comes from the direction of 62O and the noise comes from
the direction of 75O. Customizing the planar array into 3 parallel arrays
and determining the gain with the 3 main beams turning in the direction
of 28O. Simulate 3 different linear arrays of configurations to calculate
the 62O and 75O directional gain of the array in the cases to determine
Gmin, GA(θo)= G1(θo)+ G2(θo)+ G3(θo) gain regulation = 10 dBi for each
array.
Table 2.4: Array gain GA at the direction of the customized planar array
NumGeometry
Direction of GA(28o) GA(62 o) GA(75 o)
ber
of 3 ULA
main-lobe
dBi
dBi
dBi
o
o
o
1
2:26:2
28 :28 :28
30

1.2459
2.4640
2
3:24:3
28o:28o:28o
30
2.9497
0.9837
3
4:22:4
28o:28o:28o
30
1.5707
2.7985
4
5:20:5
28o:28o:28o
30
2.8708
1.9806
o
o
o
5
6:18:6
28 :28 :28
30
1.8016
2.3296
6

7:16:7
28o:28o:28o
30
2.6718
2.1814
7
8:14:8
28o:28o:28o
30
1.9362
0.8144
o
o
o
8
9:12:9
28 :28 :28
30
2.3617
3.0255
9
10:10:10
28o:28o:28o
30
1.9791
2.4735
10
11:8:11
28o:28o:28o
30

1.9562
0.7255
11
12:6:12
28o:28o:28o
30
1.9407
2.8942
o
o
o
12
13:4:13
28 :28 :28
30
1.4770
2.1124
13
14:2:14
28o:28o:28o
30
1.8366
2.1415
14
15:0:15
28o:0o:28o
30
0.9500
2.0887
The simulation data from Table 2.4 shows that with the direction

O
of 62 Gmin = 0.95 in the case of Num-14 customized in to 2 linear of 15
𝐻𝑃𝐵𝑊 = 𝛥𝑢1 =


13
hydrophones, with the direction of 75O Gmin = 0.7255, in the case of Num
-10, arrays customized into 3 arrays 11: 8: 11. So to minimize the effects
of inference and noise, the optimal configuration can be completely
determined.
2.4 Effective method of customized beamforming
2.4.1 Cancellation noise and interference
Simulation of conventional beamforming (Delay and Time) and
Frost adaptive beamforming [15] for regular and customized arrays. The
signal used to simulate is the signal emitted from the underwater target
with a length of 10 seconds (Fig.2.30).

Figure 2.30: Some of the underwater signals used for simulation
Figure 2.35 shows that the signal has been significantly
improved in terms of noise and no secondary signal has been seen. Thus,
the Frost adaptive algorithm can significantly improve the quality with
the conventional waveform algorithm. However, with the number of
sensors being constant, the signal quality can be even better when
applied with a 4x3 triangular flat array (Fig. 2.36), the simulation clearly
shows the effect of the waveform shaping solution. Customization has
reduced noise coming from uninteresting directions.


14

Figure 2.35: Frost beamforming with ULA S1 [-30O, 0O]

Figure 2.36: Frost beamforming with customize array S1 [-30O, 0O]

2.4.2

Improve signal gain with customize array.
To see an improvement in the quality of array gain by the
following formula [40]:
𝑆𝑁𝑅0 (𝜔)
1
𝐺𝐴 =
= 𝑁−1
(2.57)
2
𝑆𝑁𝑅𝑖𝑛 (𝜔)
𝑛=0 𝑤𝑛
Or
−1

𝑁−1

𝐺𝐴 =

𝑤𝑛
𝑛=0

2

=

𝑤

2

(2.58)


15
Table 2.5: Gain of ULA with 3 directions arrived of signal
Delay and Time beamforming

N
U
M

Linear (ULA)

1

Linear 12 components (ULA)

Frost beamforming

Direction Direction Direction Direction Direction Direction
S1[-30,0] S2[-10,10] S3[20,0] S1[-30,0] S2[-10,10] S3[20,0]
0.8645

0.2235

0.4764

10.9068

1.6913

3.6562

Table 2.6: Gain of customized array with 3 directions arrived of signal
Rectagular
N
customized
U
array with diff.
M
geometry
1

2

3

4

5

6

Customized
planar 3x4 type

Rectagular
Customized
planar 4x3 type
Rectagular
Customized
planar 2x6 type
Triangular
Customized
planar 6x2 type
Triangular
Customized
planar 3x4 type
Triangular
Customized
planar 4x3 type
Triangular

Delay and Time beamforming

Frost beamforming

Direction Direction Direction Direction Direction Direction
S1[-30,0] S2[-10,10] S3[20,0] S1[-30,0] S2[-10,10] S3[20,0]

2.1456

0.4610

0.5727

11.5240

1.6544

3.6667

1.3982

0.3187

0.6418

11.7307

1.6818

3.6482

4.1100

0.6899

1.01621

11.7816

1.6808

3.6731

1.0032

0.2318

0.7527

11.8109

1.6709

3.6541

2.2093

0.4603

0.6143

11.6094

1.6602

3.6669

1.3950

0.3459

0.6981

11.8155

1.6806

3.6538

The results of Table 2.5 show that the gain when convensional
array beamfoming in different directions, in fact the array can sweep in
any direction, the thesis only simulates some typical direction to find the
solution has the greatest benefit. Table 2.6 is beamforming with a
customized plane array activated different geometry, the simulation
results show that the gain of the customized plane array has improved,
but the disadvantage of that solution is that it takes a lot of the time to
calculate the optimal geometric geometry to give the best structure and
not all directions of the customized flat array have greater profit than the
regular array, this is also consistent with reality.


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3

CHAPTER 3: SOLUTIONS TO PROCCESING SIGNALS OF
SENSOR ARRAY IN THE SHALLOW SEA
3.1 Develop solutions
3.1.1 Model signal proccessing

Hình 3.1: Model signal proccessing of array
Proposing signal processing solutions
The solution used in Figure 3.2 is that after initializing the array
of signals to coventional beamforming and control the main-lobe on the

principle of "Delay and Time" horizontal to detect the target. When the
power level is higher than the detection threshold, the system will alert
the target to appear and based on the energy level, spectral density, array
frequency will customize a number of different geometric structures and
settings Frost beamforming to find the array configuration for the best
signal (Figure 3.3).
3.2 ICA with customized array
3.2.1 Independent Component Analysis - ICA
3.2.2 ICA signal processing enhances target positioning quality
a) Structure and model of target position sensor array
b) Improve the quality of multi-target positioning with ICA
- Develop an ICA pre-proceesing model to track multi-tagets:
For positioning follow to (3.23) (3.24), the number of hydrophone is 4, according to the ICA model above, the number of hydrophones needed is equal to the number of targets to be monitored. Thus,
to monitor 2 targets at the same time, the configuration for 8 hydrophone
works, 3 targets need 12 units .., in addition to setting the structure
(changing the depth of the sensor as well as the geometric layout of
network) easily implemented for monitoring and observation for various
purposes (Figure 3.7).
3.1.2


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Figure 3.2: Flowchart of signal processing algorithms


18

Figure 3.3: Flowchart of the algorithm to beam customized array

19

Figure 3.7: ICA model for multi-target positioning
3.3 Multi-channel blind deconvolution
3.3.1 Model of MBD
3.3.2 MBD condition for the sensor array
3.3.3 Application of Feed-Forward neural network to MBD
Feed Forwardward Neural Networks (FFNWs) is a popular used
multilayer network with back-propagation algorithm (feedback
transmission). This algorithm allows the use of a training signal to train a
neural network that splits a mixture of multi-path signals at the input so
that it is most similar to the desired signal.

Figure 3.11: Structure of Feed-Forward neural network


20
To MBD using FFNWs, consider the advance model Figure
3.10b [11] that have:
𝑚

𝑦(𝑘) =

𝑦𝑖 𝑘 ,

(3.44)

𝑤𝑖𝑝 𝑘 𝑥𝑖 𝑘 − 𝑝 = 𝒘𝑇𝑖 𝒙𝑖 𝑘 ,

(3.45)

𝑖=1

With

𝐿

𝑦𝑖 𝑘 =
𝑝=0

(𝑖 = 1,2, … , 𝑚)
Learning algorithm (3.48) can be rewritten
𝑘
𝑘
(3.49)
∆𝒘𝑖 𝑘 = 𝜂 𝑘 𝚲𝑖 − 𝐑 𝐲𝑖 𝐠 𝒘𝑖 𝑘 , (𝑖 = 1,2, … 𝑚)
In that:
(3.50)
𝚲𝑖𝑘 = 1 − 𝜂0 𝚲𝑖𝑘−1 + 𝜂0 𝑑𝑖𝑎𝑔 𝐲𝑖 𝑘 𝐠 𝑇 (𝐲(𝑘)) ,
𝑘
𝑘−1
𝑇
(3.51)
𝐑 𝐲𝑖 𝐠 = 1 − 𝜂0 𝐑 𝐲𝑖 𝐠 + 𝜂0 𝐲𝑖 𝑘 𝐠 𝐲 𝑘 .
The above algorithm has the same as the natural gradient algorithm.
To MBD with many different algorithms, the thesis uses FFNWs
advanced neural network model with back-propagation algorithm to
analyze MBD. Extract original signal from the mixed signal obtained
through a training signal.

3.3.4 Train FFNNs network to separate the desired signal
3.3.5 Simulate multi-path signal processing with FFNNs
Simulation of sound channel in shallow water affected by multipath effect with 10 sound rays (1 direct and 9 reflections): Assuming the
sound speed in water is constant c = 1520m/s . Depth of sound channel h
= 100m.
- Setting environmental parameters:
The simulated multi-path signal is square pulse with a width of t
= 13.2ms, input impedance of 50Ω, a amplitude of 1V, equivalent to
13dBm, the signal generator is set to a depth of z = -60m (coordinates
[0,0, - 60]), hydrophone H1 set at a depth of -40m with coordinates
[500,0, -40], hydrophone H2 at a depth of -70m with coordinates [500,
900, -70], with isotropic sources with rays straight and reflected sound at
the bottom have attenuation level of 0.5dB.
- Parameters of device transceiver: Hydrophone has sensitivity is 140dBV re 1μPa, scalar receiver in the range below 30kHz, preamplification is 20dB and noise is 10dB.


21

Figure 3.14: Multi-path signal with 10 rays in the underwater channel
- Setting up FFNNs: So when the signal passing through the environment
of the shallow water is reduced to 1.6x10-7 (V), equivalent to -123dBm,
get 300 typical samples for the multi-path signal obtained. (Figure
3.17b) and get 300 training signal samples that are equivalent to the
source signal, with a equivalent to the receiver level. The training
purpose for the network to split the desired signal pulse in the set of
received signals (Figure 3.17a). Setting up neural network with forward
path of 10 input layer cells, 1 output layer, sigmod neuron activation
function, transmission algorithm with wji weight and feedback according
to LMS principle (least square) .

Figure 3.17: Training and signal samples to input the neural network
Applying two signal samples in Figure 3.17 to the neural
network for processing, the mixture of received multi-signal signals has
reconstructed the signal form similar to the training signal (Figure 3.18),
the signal form after processing The theorem has not mixed the reflected
pulses, the effect of the multi-path effect on the receiver signal has


22
decreased. Target detection will become more reliable, negative
hydrographic positioning calculation will be more accurate.

Figure 3.18: After process by neural network; a) multi-path suppression
signals, b) multi-path suppression signals taken at absolute values
3.4 Effective of complex signal processing solutions
3.4.1 Improve SNR and gain after ICA
The multi-component mixed signal receive by hydrophone 1
after spectral analysis showed that many frequency and harmonic
components appeared (Figure 3.19.a), calculating the SNR ratio of this
signal with the addition white noise SNR0 = 6.8282 (Table 3.5). After
ICA process, the submarine's Ping sound is separated from the mixture
with noise and harmonics, which is significantly reduced (Figure
3.19.b), SNR1 = 20.0226. Thus, the gain increased to 13.1944 dB.
Similarly for the floating diesel engine and whale sound (Figure
3.9.5,8,6,9) both increased the SNR to 14 dB.
Calculate the ratio of SNR of the mixed signal collected at 3
hydrophone (Figure 3.9.4,5,6) = SNR0 and SNR of the signal after
separation (Figure 3.9.7,8,9) = SNR1. Assuming noise is white noise
plus constant energy, the simulation for the signals in Figure 3.9 gives:
Table 3.5: Calculating SNR to determine the gain after ICA process

Tỷ số SNR (SNR=
Ptín hiệu / Ptạp)
SNR0 (hỗn hợp trộn)
SNR1 (sau khi tách)
Độ lợi theo công thức
(3.63) = SNR1/SNR0

Tín hiệu 1
(Tiếng Ping của
tàu ngầm)
6.8282
20.0226

Tín hiệu 2 (Tiếng
động cơ Diezen
tàu mặt nước)
5.8788
19.9942

Tín hiệu 3
(Âm thanh của
cá voi)
5.8438
19.9834

13.1944

14.1154

14.1396