PROCEEDINGS of the23rd International Congress on Acoustics
9 to 13 September 2019 in Aachen, Germany

Underwater acoustic recognition system for detection oflow-altitude moving source
Tianqing ZHAO1; Xubin LIANG1; Difeng SUN1; Fang ZHANG1; Deyu SUN1
1

Northwest Institute of Nuclear Technology, China

ABSTRACT
Air targets detection underwater is an important part of marine safety management. For the difference inthe physical property of the water and air, there are many difficulties in the water to air sound detection. In
this paper, we present a recognition system using both time and frequency domain characteristics for targetdetection. In the detection phase, a short-to-long-term energy ratio (SLR) was first obtained using thecoupling acoustic data. This initial SLR indicates the possibility of target appearing. For signals exceeding
SLR threshold, the cepstral coefficients are extracted by a 4-Gammatone filter with cochlea membraneability. The validity of the algorithm is proved by a test adopting BP network with both synthetic and realdata. The results indicating that joint detection algorithm can detect the low-altitude helicopter real-timeand has a good anti-noise performance.
Keywords: Target Detection, Air-to-water Coupling Signals, Dynamic Energy

1. INTRODUCTION
In the air and sea combat, the air platform has an asymmetry advantage to the underwater force .
Usually, air targets in the execution of missions have the characteristics of lo w flight altitude, slow
flight speed and occasional hovering. One area of interest is the detection of air targets using acoustic
signals coupled from air to water. In response to this problem, early research mainly focused on themodeling of underwater acoustic filed, including computational models based on ray acoustics, wave
theory and normal-mode theory (1-3). Some recent studies conducted by domestic scholars include:
acoustic filed propagation of airborne source in shallow sea, technology of helicopter detection
underwater and so on (4-7).
The estimation of target flight distance is usually difficult when the target model is different and noenough stable environmental parameters. These disadvantages limit the experimental and engineering
applications of water-to-air positioning algorithms. In this paper, we propose a detection algorithm
based on signal energy and spectral characteristics that is weakly correlated with the calculation
models, and generates a trigger signal to start the next accurate localization.
Since the acoustic impedance of air and water differs by more than 4000 times, the soundintensity loss is about 20~30dB when sound propagates through the air-water interface. In addition,
total reflection occurs when the incident angle to the water surface exceeds 12.7°, and the acoustic
energy coupled into the water is mainly concentrated within a cone angle of about 25.4° (7, 8). Basedon this feature of sound propagation, our research is mainly set as a shallow sea area . The rest of thispaper is organized as follows: Section 2 presents the system composition of the detect ion system. A
time-frequency joint detection algorithm is given in Section 3. With the extraction features of known
air targets, simulation and measurement results are analyzed in Section 4. Then conclusion is given
in Section 5.

2. DESCRIPTION OF THE PROPOSED SYSTEM
To achieve the underwater detection of flying targets, the main task of underwater acoustic signalprocessing is to identify the target signal under low SNR conditions. The paper considers theapplication of the detector in shallow sea when the altitude of the air target is 100 meters or less, theflight speed is lower than 60km/h and hovering sometimes. The detection system is composed with a1

zhaotianqing@nint.ac.cn2835

signal acquisition unit and a signal processing unit. The whole detector is illustrated in Figure.1. The
acquisition unit mainly includes two hydrophones (GRAS 10CT) and signal conditioning modules.
The hydrophone records the acoustic signal coupled through the air-water interface. After filtering,noise reduction and framing, the signal processing unit runs the detection algorithm and gives adetection result every 0.5s.
SignalSignalAcquisition Unit

Preprocessing Unit

Hydrophones

Conditioning Modules

Time and Frequency Features Extraction

Figure 1 – System composition structure figure
Assuming that the background noise is statistically stable, and the target signal energy is always
larger than the background noise. Firstly, calculating an energy ratio of the short and long frame
signals, which called a short-to-long-term energy ratio (SLR). If the SLR values of consecutive K
frames exceed the set threshold, it is determined that a suspicious target appears. Next, the cepstralcoefficients of the current frame signal are extracted using a 4-order Gammatone filter with eardrum
filtering characteristics. With these learned features input to a BP neural network, the final judgment
result is given as 1 or 0 w means target appears or not. The whole detection process is shown in Figure
2.
Preprocessing

Signal Sampling

Judgment ofSLR Calculation

and Framing

Trigger Detection

Non-helicopterHelicopter

Generating

Generating Cepstral

SLR Judgment ofDetection Results

Coefficients

Continuous K Frames

Figure 2 – System detection flow chart

3. TIME AND FREQUENCY JOINT DETECTION ALGORITHM
3.1 Energy Trigger in Time Domain
In the real-time signal processing, the short-term energy is calculated for each frame of data. Thesevalues form an energy sequence that called STAEn (i)(i is the frame number). In addition, the signal
long-term energy called LTAEn which is the successive K frames before the current frame. Allparameters are calculated according to the following expression.
STAEn (i ) LTAEn ( i 1)* N  N

x ( m) 2(1)

 STA (i)(2)m  ( i 1)* N 1ii  ( K 1)

EnSLREn  STAEn ( K ) LTAEn

y

LTAEn 2
12.2
LTAEn ( STAEn (i ) )K
K -1 i 1
K(3)

n(4)

bgthrd (i)  w * bgthrd (i  1)  (1  w) *  y(5)Where STAEn denotes the short-time energy of the current signal frame, LTAEn is the sum of theprevious K frame energies, bgthrd is the current trigger threshold with an initial valve set to 0.1, and δyis the adjustment value of the detection threshold. The threshold is dynamically updated according to2836

the above formula (4, 5). The algorithm used two sets of parameters as the conditions for trigger
detection, which are STA compared with bgthrd while SLR compared with A 0 . It is determined that thesuspicious target is found when both sets of values exceed the set threshold. The spectrum features arethen analyzed to eliminate interference signals.
3.2 Feature Matching in Frequency Domain
Aerial targets, such as helicopters, can be seen as a strong continuous sound source in flight. The
signal frequency spectrum is a discrete spectrum of harmonic relationship superimposed on thebroadband continuum, as shown in the Figure 3. In general, the fundamental frequency of the main
propeller excitation signal is between 10Hz to 25Hz, and the fundamental frequency of the tailpropeller excitation signal is between 50Hz to 100Hz.

Figure 3 – Typical helicopter signal FFT spectrum
The detection algorithm using the fundamental frequency and harmonic set of helicopter has higher
requirements on the SNR of the real-time signal. Without using the sound field propagation calculation
models, the filtering cepstrum coefficient extraction based on human auditory feature recognition isstudied. Referring to the author’s previous airborne feature extraction algorithm (9), the signal
features are extracted from current frame are called Gammatone Filter Cepstrum Coefficients(GFCC). The extraction features of two targets are shown in Figure 4.

(a)Helicopter GFCC(b) Background Noise GFCC
Figure 4 – Extraction GFCC of Different Targets
Next, the BP neural network is trained by extracting features to achieve the recognition of specifictargets.

4. EXPERIMENTAL RESULTS
4.1 Simulation Experiment in the PoolFirst, an experiment was done in a circular pool (6 meters in diameter) to verify the design
algorithm. A high-powered speaker was hoisted directly above the center of the pool to playhelicopter flight audio files. The above design system was used to collect the coupled water signal(the hydrophone was located at 2m depth), and the signal sampling rate was 8000Hz. The frequency2837

domain features were extracted by acquisition signals from the audio, and the neural network wastrained using a training set to prepare for the real experiment of the signal. The network recognition
result is shown in Figure 5.

Figure 5 – Detection and recognition results of helicopter and noise
It can be seen that the network identification result error rate is 5.95% for the selected 84 test
signals. At the same time, the cepstral coefficient method still has good anti-noise performance in thecharacteristic expression of underwater coupled signals.
4.2 Physical Flight Experiment in the Lake
The experiment was carried out in X-Lake, and the main test shell of the underwater sounddetection system was sunk into different depths (5m, 10m, 15m) in the lake. A French -made small
squirrel helicopter was at different heights on the water surface (30m, 50m, 100m) flight or hover,the main results of the experiment and analysis are as follows. The system operation output is 1 and0, respectively representing to found target and no target.
4.2.1 Detection Performance at Different Flight Altitude
According to the airborne sound propagation characteristics, the system mainly collects the direct
refracted water signal. As the flight altitude changes, the duration time of detection does not changemuch, but the farthest detection distance increases. The results are shown in Table 1.
Table 1 – Detection performance of the detector at 5m depth underwater
Altitude, mCircling flight

Hovering flightDuration, sDistance, mDuration, sDistance, m108.5(7)134(-)

157

67-171

207

127

171

63-22950--9369-148

100

7188

125

70-119From the above results, it can be found that the target in hovering state has a strong energy andcan be detected sustainably.
4.2.2 Detection Performance at Different Depth
As the depth of the detector increases, the signal is successfully dete cted within a water depth of50m. The results are shown in Table 2.
Table 2 – Detection performance of the helicopter at 20m flight altitude
Depth, m2838

Circling flight

Hovering flight

Duration, sDistance, mDuration, sDistance, m

57127

171

63~229104125

179

65~69

15

5.5

168

62

44~162

The experimental results show that under the condition of relaxing the number of experiments
and the quality of data acquisition, the attenuation of the coupled water signal in the shallow waterlayer is not obvious to the target source in the same state.

5. CONCLUSIONS
The acoustic detection system for low-altitude target is designed and realized in the shallow water
environment. It is proved effectiveness that the signal detection algorithm based on energy thresholdjudgement and frequency feature recognition. The results show that the algorithm is highly
transplantable and insensitive to specific targets. Sequential modeling and analysis of detected threat
in the real sea environment is the plan for the future work.

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