Measurement method of acoustic reflection coefficient of underwater acoustic materials with single vector hydrophone

It is bjective to study the changes in the shape and geometrical position of the acoustic focal range of the concave spherical ultrasonic transducer when the sound intensity is high and the medium has a large attenuation. From the perspective of physical acoustics, the effects of nonlinearity and media attenuation caused by high sound intensity on the sound focal range are analyzed, and the linear superposition algorithm of integral is used to perform numerical simulation calculations. Both theoretical analysis and numerical calculation show that with the increase of sound intensity and medium attenuation, the geometric position of the acoustic focal zone has a millimeter-level advance along the acoustic axis in the direction of the transducer; at the same time, the acoustic focal zone The shape gradually changed from a symmetrical long ellipsoid to a short ellipsoid with a "fat head and thin tail".

High sound intensity and medium attenuation have an important influence on the position and shape of the sound focal region of the concave spherical transducer. Full consideration should be given to the precise positioning and dose control of HIFU equipment, the formulation of inspection standards, and even the clinical application.

my country has made remarkable breakthroughs in the development and clinical application of high-intensity focused ultrasound (high-intensity focused ultrasound (HIFU) equipment). However, to truly achieve accurate positioning and treatment dose control on the equipment, so that clinical treatment can achieve the ideal effect of effectively killing the lesion without damaging the surrounding normal tissues, there are still many theoretical and technical issues that need to be studied and resolved in depth. Domestic and foreign experimental studies on the formation of damage of HIFU in biological tissues have shown that with the increase of sound intensity, the position of the focal zone moves forward and gradually changes from a long ellipsoid to a "tadpole shape" or a "cone shape". Although in recent years, foreign literature has made some qualitative explanations for the above phenomenon by numerically solving the nonlinear acoustic wave propagation equation (KZK equation), but the calculation procedure is complicated and the physical relationship in the calculation process is unclear. For this reason, this paper takes the concave spherical focusing transducer as an example, and discusses the problem by studying the influence of the medium attenuation and the nonlinear propagation characteristics under high sound intensity on the sound focal range.

In our previous work, based on the Kirchhoff diffraction integral, we have derived the expression of the sound pressure at any point in the single-frequency sound field under the condition of a linear sound field with a concave spherical focusing transducer with uniform radiation on the surface (also called For Rayleigh points).

From the analysis of nonlinear acoustics theory, when the sound pressure of the single-frequency sine wave radiated from the surface of the transducer into the medium is large enough, it is called a "finite amplitude wave", which propagates a certain distance in the medium (called the discontinuous distance). ), the waveform will be distorted into a sawtooth wave, which can also be regarded as a shock wave. In addition to the fundamental frequency of the original emission, the frequency spectrum of this wave also includes a series of higher harmonics. They are gradually generated by continuously absorbing energy from the fundamental wave during the propagation of sound waves, that is, the tissue harmonics in ultrasound medicine. The amplitude coefficient can be used to describe the propagation of high-order harmonics with the propagation distance and the relationship of energy changes during propagation.

The sawtooth wave forms a distance, so it is a dimensionless quantity reflecting the propagation distance. Based on this, we have calculated the amplitude coefficient curve of the fundamental wave and the first 3 harmonics. When the sound wave propagates in the medium, the sound pressure decays exponentially with the distance, which can be expressed in a form. For general soft tissues, the attenuation coefficient T M is roughly proportional to the frequency. In order to simplify the calculation, this article expresses the attenuation coefficient of each harmonic component as where α is the sound attenuation system of the fundamental frequency sound wave in biological tissues per unit distance.

It should include the sound absorption and scattering of the tissue. After considering the above two factors (non-linearity and attenuation), the expression of the sound pressure in the focused sound field can be extended to the following form: is the wave number of each harmonic. This formula is what we call the linear superposition algorithm of Rayleigh integral.

Result：

1 The influence of medium attenuation on the sound focal range.The parameters of the unit concave spherical transducer used in this paper are: radius of curvature R = 15 cm, radius of aperture a = 42 cm, working frequency f = 1.7 MHz. Assuming that the medium is general soft tissue, its attenuation coefficient α is in the range of 01-30dB stew (cm·Mz). The sound velocity, density and other parameters of the medium are taken according to the relevant literature. In order to study the attenuation coefficient as a single influencing factor, only a single frequency, namely the fundamental frequency, needs to be calculated and analyzed for the change law of the sound focus domain with different α values. For this reason, in the formula , a series of numerical calculations were carried out by taking M=1. The results show that with the increase of attenuation, that is, when α = 0.3, 13 and 23dB stew (cm·Mhz), the shape of the -6dB acoustic focal region gradually changes from a long ellipsoid to a short ellipsoid, and its long axis1 and short axis .

2.They are 111, 104, and 92 respectively. The position of the focal zone (position on the acoustic axis), the latter two are respectively 30mm and 65mm ahead of the former along the acoustic axis of the transducer. At the same time, the head of the focal zone (the end close to the transducer) is more "fat" than its tail (the end far from the transducer).

2 The effect of non-linearity caused by high sound intensity on the sound focus range is the same, the surface radiation sound pressure is considered as a single factor, and its values are respectively 44, 73, 4 MPa, andα = 3dB stew (cm·MHz). Considering that the attenuation of the medium increases rapidly with the increase of the harmonic frequency, the number of harmonics does not need to be too many. The calculation results show that: as the surface radiation sound pressure increases, the position and shape of the focal zone change unlike when the attenuation coefficient changes It's so big, but its changing law is similar. That is, the positions of the latter two focal areas are moved forward by 16mm and 21mm respectively; the ratio of the long and short axis of the 6dB focal area is 119, 116, and 113 respectively, and the head of the focal area also has a tendency to become "fat".

3 The combined effect of attenuation and nonlinearity on the sound focal range.

The above two factors are simultaneously incorporated into formula (3) for calculation. Figure 3(a) and Figure 3(b) respectively show that α=3dB stew (cm·MHz), P′ 0=44MPa and α=2.3dB stew (cm·MHz), P′0=44MPa

When considering attenuation and nonlinear effects at the same time, the contour of the iso-sound pressure line in the focal zone is the calculation result in the figure. Compared with the two, the focal zone position has moved forward by 8.4mm, and the ratio of the focal zone's major and minor axes has changed from 11.9 to 8.5. It shows that the change trend of the focal zone caused by the attenuation coefficient and nonlinearity is the same, so the overall effect is strengthened.

in conclusion

The theoretical analysis and calculation results in this paper show that: high sound intensity and medium attenuation have an important influence on the shape and position of the sound focal zone; the greater the attenuation coefficient of the medium, the higher the sound intensity (that is, the stronger the nonlinearity), and the sound focus The closer the field is to the transducer; the ratio of the long and short axes of the focal field also becomes smaller, that is, its shape gradually changes from a long ellipsoid to a short ellipsoid, and the head of the sound focus area becomes "fat" than the tail. Phenomenon, the shape tends to be "carrot". The above conclusions provide a basis for quantitatively analyzing the change law of the sound focus area of the HIFU sound field, and further study the relationship between the sound focus area and the damage area.

Large sample measurement method of acoustic reflection coefficient of underwater acoustic materialswith single vector hydrophone

In order to realize the free-field broadband measurement of the normal acoustic reflection coefficient of underwater acoustic materials, a single vector hydrophone is used as the core equipment of the measurement system, combined with pulse acoustic emission technology and post-inverse filter signal processing technology, a single vector hydrophone based on single vector hydrophone is proposed. The free-field broadband measurement method of the normal acoustic reflection coefficient of the underwater acoustic material of the underwater acoustic material, through the vector hydrophone electronic rotation technology to realize the effective separation of the direct sound and the reflected sound. The influence of the measurement system error and the signal-to-noise ratio of the received signal on the measurement result is discussed. This method has certain requirements for the signal-to-noise ratio, but it is not sensitive to the measurement system error. The experimental test results show that: Compared with the experimental test results without post-inverse filtering processing, the method described in the article significantly improves the measurement performance, but limited by the low-frequency emission capability of the transmitting transducer, the experimental results are above 2.5 kHz and The theoretical values are in good agreement.

The acoustic reflection coefficient is an important parameter that characterizes the acoustic performance of underwater acoustic materials. At present, the measurement methods of the acoustic reflection coefficient of underwater acoustic materials can be roughly divided into the small sample laboratory acoustic tube method and the large sample free field measurement method. Large sample free field measurement is generally carried out in a large anechoic pool. By laying silencing materials on the boundary of the pool to absorb the reflected sound of the pool boundary, the signal received by the hydrophone is only the direct sound and the reflected sound of the sample. However, due to the limitation of the lower limit of the anechoic pool, the low-frequency multipath effect is obvious; in addition, the free-field measurement method is mostly interfered by the edge diffraction effect of the sample, and this interference is particularly serious in the low-frequency band. In order to solve the above problems, impulse sound testing technology is widely used in the measurement of acoustic parameters of underwater acoustic materials. It is its key technology to transmit pulsed acoustic signals with controllable waveforms and without distortion. However, the transfer function of the transmitting transducer limits the lower frequency of impulse sound testing technology in limited measurement space. For this reason, a variety of compensation methods have been proposed, such as the broadband pulse superposition method proposed by Li Shui et al. This method uses inverse filtering technology to preprocess the excitation signal of the transmitting transducer to compensate the transmission function of the transmitting transducer, so that the signal radiated by the transmitting transducer is an ideal sharp pulse, which effectively reduces the lower limit frequency of the measurement.

Different from the above method, the "post-inverse filtering technology" processes the signal at the receiving end of the hydrophone to achieve the purpose of compensating the frequency response of the transmitting transducer. The "post-inverse filter technology" is adopted in the acoustic tube to achieve broadband measurement of the sound absorption coefficient of underwater acoustic materials. This method first obtains the transfer function of the measurement system, then compensates the observation signal, and finally obtains the acoustic reflection coefficient of the sample by dividing the compensated observation signal amplitude spectrum with the standard sample reflection signal amplitude spectrum, and further calculates the absorption Sound coefficient. In recent years, vector sensors have been successfully applied to the measurement of acoustic parameters of aeroacoustic materials, such as surface impedance method and sound intensity method. The vector hydrophone can pick up the sound field information synchronously and at the same point, which expands the post-signal processing space, and the joint processing of sound pressure and vibration velocity signals can form a certain spatial directivity, which can interfere with the diffraction sound of the sample edge. To a certain degree of suppression, it is unnecessary to use a conventional large sound pressure receiving array, which reduces the complexity of the measurement system. At the same time, the output main maximum direction of the combined processing of sound pressure and vibration velocity of the vector hydrophone can be directed to a predetermined direction through electronic rotation technology, which facilitates the effective peeling of direct sound and reflected sound. In addition, the vector hydrophone also has the advantages of good low-frequency directivity and resistance to isotropic noise. Therefore, compared with the traditional sound pressure hydrophone, using a vector hydrophone to test the sound reflection coefficient of a material has certain advantages. This paper presents a wide-band measurement method for the normal acoustic reflection coefficient of underwater acoustic materials with a large free-field sample. This method uses a single vector hydrophone as the core equipment of the measurement system, combines pulsed acoustic emission technology and post-inverse filtering technology to suppress signal waveform distortion, eliminates sample edge diffraction sound and multi-path interference sound in the time domain, and then passes The electronic rotation technology of the vector hydrophone realizes the effective separation of the direct sound and the reflected sound, and finally the normal sound reflection coefficient of the sample is obtained by dividing the two.

1 Measurement process

In order to explain the measurement principle of this method, while explaining the measurement process, the related formula derivation and simulation results are given.

1.1 The transfer function identification and inverse filter design of the measurement system Before testing the sample, the transfer function of the measurement system should be obtained first. Different from the traditional sound pressure hydrophone, the vector hydrophone includes a sound pressure channel and a vibration velocity channel, so the transfer function of each measurement channel of the vector hydrophone needs to be obtained at the same time. During the measurement, the ideal pulse signal is radiated into the water medium through the transmitting transducer, and then transmitted to the receiving point through the hydroacoustic channel, and finally received by the vector hydrophone and collected by the collector. Therefore, the measurement system can be divided into three parts, namely the signal transmitting system, the underwater acoustic channel and the signal receiving system. Taking the sound pressure channel as an example, the received signal model is shown in Figure 1.

In Figure 1, s(f) is the transmitted signal spectrum, T(f), Hp(f) and R(f) are the transfer functions of the transmitting system, sound pressure hydroacoustic channel and signal receiving system, respectively, and N(f) is the background Noise spectrum, Y(f) is the output signal spectrum of the measurement system. The post-inverse filtering technique is to design an inverse filter to compensate T(f) and R(f) when the transfer function of the measurement system is known. Take the sound pressure channel as an example to illustrate the basic principle of the transfer function identification of the measurement system. Method 1 Consider the signal transmitting system and the signal receiving system as a whole, that is, H(f) = T(f) + R(f). The input signal is x(t), the system output signal is y(t), the background noise is n(t), Y(f) = H(f) X(f) + N(f) (1) where, X(f), Y(f) and N(f) are the Fourier transform of the system input signal x(t), the system output signal y(t) and the background noise n(t), respectively. After calculation, the estimated value of H(f) is ^H(f) =Gxy(f)Gxx(f) (2) where Gxy(f) is the cross-power spectrum of the input signal and output signal of the system, and Gxx( f) is the self-power spectrum of the input signal of the system.

In addition to the aforementioned measurement system identification methods, pseudo-random sequence identification techniques can also be used. Method 2 Suppose the input signal x(t) of the measurement system is a pseudo-random sequence (MLS sequence), and the output signal of the system is y(t). Obviously, y(t) = x(t) * h(t) (3) where , * Means convolution, h(t) is the unit impulse response function of the system. Calculate the correlation function between the input signal and the output signal of the system, rxy = ∫x(τ) y(τ-t) dτ = h(t) * rxx(t) (4) where rxy is the cross-correlation between the input and output of the system Function, rxx is the autocorrelation function of the input signal. Because the MLS sequence has better autocorrelation characteristics, that is, rxx(n) = δ(n)-1L + 1 . where L = 2m-1 is the sequence length, and m is the order of the pseudo-random sequence. It is easy to see that the estimated value of the system unit impulse response function ^h(t) is ^h(t) ≈ rxy (6) Further Fourier transform can obtain the estimated value ^H(f) of the system transfer function of the measurement system. After obtaining ^H (f), design the inverse filter H-1( f) in the frequency domain as Hpost( f) =^H( f)| ^H( f) | 2 + q( 7) where , Q is a normal number, generally 1% of the maximum value of | ^H (f) | 2. Simulation condition 1 The transmitting transducer and the hydrophone are placed in an anechoic pool at equal depth, the distance between the two is 1 m, and the transmitted signal is a 16-order MLS sequence. The method 1 and method 2 are used to identify the system, respectively. The ratios are 10, 20 and 30 dB. Assess the pros and cons of the transfer function identification results of the two methods at different signal-to-noise ratios. In the simulation, the unit impulse response function of the system is simulated by adding Gaussian pulses with center frequencies of 1, 2, 4 and 8 kHz.

Figure 3 shows the identification results of the transfer function of the measurement system under the above conditions. It can be seen from the figure that the two system identification methods described in this article can effectively obtain the transfer function of the measurement system. However, method 1 has certain requirements on the signal-to-noise ratio. When the signal-to-noise ratio is greater than 30 dB, the identification result is accurate.The system identification result of method 2 is better than that of method 1, and high-precision identification results can still be obtained under the condition of low signal-to-noise ratio. This is because the background noise has a small correlation with the excitation signal of the sound source, so this method has a certain anti-noise ability. The following is an analysis of the effectiveness of the measurement method described in this article through simulation and numerical calculation.

1.2 Observation data processing

1) Obtain observation data. The measurement principle diagram of underwater acoustic transducer sensor  is shown in Figure 4. In the figure, ri is the direct sound path, and the distance from the vector hydrophone to the sample is d, the reflected sound path is ri + 2d, re = rs + rr is the diffracted sound path, rq is the reflected sound path at the pool boundary, pi is the direct sound, pr is the reflected sound, pe is the diffracted sound at the edge of the sample, pq It is a multi-way interference sound.

Suppose the excitation signal spectrum of the transmitting transducer is s(f), and the characteristic impedance of the medium is ignored. Without loss of generality, the frequency domain expression of the signal received by the two-dimensional vector hydrophone is P( f) = s( f) · 1 + Rs( f) e－jωτr+ D( f) e－jωτe + Rq( f) e－jωτq Hpt( f)Vx( f) = s( f) · cos( θi) + Rs( f) e－jωτrcos( θr )+ D( f) e－jωτecos( θe) + Rq( f) e－jωτqcos( θq) Hvxt( f )Vy( f) = s( f) ·sin( θi) + Rs( f) e－jωτrsin( θr )+ D( f) e－jωτesin( θe) + Rq( f) e－jωτqsin( θq) Hvyt( f)(8) In the formula, Rs(f) is the sample's acoustic reflection coefficient which depends on the sound wave frequency and incident angle, D(f) is the sample edge diffraction coefficient, Rq(f) is the pool boundary reflection coefficient, τr, τe and τq are the time delays of reflected sound, sample edge diffraction sound, and pool boundary reflection sound and direct sound, respectively. θi, θr, θe and θq are direct sound, reflected sound, sample edge diffraction sound and pool boundary reflection sound, respectively The incident angle of the sound wave, Hpt(f), Hvxt(f) and Hvxt(f) respectively represent the transfer function of each measurement channel of the measurement system.

2) The measurement system transfer function compensation. Multiply the designed inverse filter with the frequency spectrum of the corresponding channel observation data to obtain the compensated signal.The frequency spectrum Ppost(f), Vxpost(f) and Vypost(f) are Ppost(f) ≈ s(f) ·1 + Rs(f) e－jωτr+ D(f) e－jωτe + Rq(f) e－jωτq Vxpost( f) ≈ s( f) · cos( θi) + Rs( f) e－jωτrcos( θr )+ D( f) e－jωτecos( θe) + Rq( f) e－jωτqcos( θq) Vypost( f ) ≈ s( f) ·sin( θi) + Rs( f) e－jωτrsin( θr )+ D( f) e－jωτesin( θe) + Rq( f) e－jωτqsin( θq)

Simulation condition 2 Suppose the depth of the pool is 10 m, the launching transducer, the vector hydrophone and the water depth h of the sample to be tested are 5 m. The distance H from the transmitting transducer to the sample is 15 m, the distance d from the vector hydrophone to the sample is 10 cm, the transmitting signal is a Butterworth pulse acoustic signal, the signal bandwidth is 500-10 kHz, and the sampling frequency fs = 131 072 Hz and a signal-to-noise ratio of 30 dB. Take the sound pressure channel as an example to verify the effectiveness of the post-inverse filter compensation. In the simulation, the sample to be tested is an aluminum plate with a thickness of 0.006 m and a geometric size of 1 m×1 m. The edge diffraction coefficient of the sample is simulated with a low-pass filter.

Figure 5 shows the compensation effect of the sound pressure channel's post-inverse filter. The figure shows that the signal waveform after compensation is more regular and smooth, which effectively suppresses the signal distortion caused by the transfer function of the measurement system and helps eliminate interference such as edge diffraction sound.

3) Eliminate interference sounds. Calculate the time delay of reflected sound, sample diffraction sound, and pool boundary reflection sound according to the measurement system deployment parameters, and perform inverse Fourier transform of equation (9) to obtain the time domain signal, then add a window to intercept the useful signal, and perform Fourier Leaf transformation, we get Pc( f) = s( f) [1 + Rs( f) e－jωτr]

Vx c( f) = s( f) [cos( θi) + Rs( f) e－jωτrcos( θr)]

Vy c( f) = s( f) [sin( θi) + Rs( f) e－jωτrsin( θr)] where Pc(f), Vxc(f) and Vyc(f) are respectively Signal spectrum of each channel. Separate the direct sound and the reflected sound, and obtain the sound reflection coefficient of the sample. Suppose the vector hydrophone's guiding azimuth is ψ, and the calculated composite particle velocity Vc is Vc( f) = Vxc( f) cos( ψ) + Vyc( f) sin( ψ) (11) First, point the guiding azimuth to the transmitter Let ψ = 0, and perform (p + vc) 2 joint processing, omitting the common term s( f), and get the joint processing output Ii as Ii = [Pc( f) + Vc( f)] 2ψ = 0 = 4 (12) Point the guiding azimuth to the sample again, that is, let ψ = π, and perform the joint processing of (p + vc) 2 to obtain the joint processing output Ir = [Pc( f) + Vc( f)] 2ψ = π = 4 ［R2s( f) e－2jωτr］

2 Measurement error analysis

Simulation condition 3 The measurement system parameters remain unchanged, the transmitted signal is a Butterworth pulsed acoustic signal, and the signal bandwidth is 500 ~ 10 kHz. Without considering the diffraction effect of the sample edge and the influence of the reflection sound at the pool boundary, the signal-to-noise ratio is discussed. When it is 20, 30 and 40 dB, the measurement result changes with frequency. The measurement results and measurement relative error curves under different signal-to-noise ratios are shown. It can be seen from the figure that the measurement relative error attenuates with the frequency oscillation, and the low frequency band is greatly affected by the signal-to-noise ratio; in addition, when the signal-to-noise ratio is 20 dB, the change trend of the measurement result is the same as the theoretical value, but the measurement result has a larger error; low The large frequency band measurement error is because the acoustic reflection coefficient is small, and small fluctuations can cause large relative errors. In the actual test, in addition to the signal-to-noise ratio, the measurement system placement error will also have an impact on the measurement results. The following simulation analyzes the impact of the measurement system placement error. Simulation condition 4 The parameters of the measurement system remain unchanged, regardless of interference such as background noise and sample edge diffraction. The distance H from the sound source to the sample is 5, 10, and 15 m, respectively. It is discussed when the distance d from the vector hydrophone to the sample is 10 The measurement result at% error. The measurement results are given when the distance H from the transmitter transducer to the sample is different, and the distance d from the vector hydrophone to the sample has a 10% error. The figure shows that the measurement result is not sensitive to the error of the distance between the vector hydrophone and the sample; H The measurement results are not almost coincident at the same time. It can be seen that in the actual test, it is only necessary to select the appropriate H according to the geometric size of the measuring pool. Simulation condition 5 The measurement system parameters remain unchanged, regardless of interference from background noise and sample edge diffraction. The distance d from the vector hydrophone to the sample is 5, 10, and 15 cm, respectively, and the distance H from the transmitting transducer to the sample is 15 m, discuss the measurement results when there is a 1% error in the distance H from the transmitter transducer to the sample. The measurement results are given when the distance d from the vector hydrophone to the sample is different, and the distance H from the transmitting transducer to the sample has a 1% error. From the figure, it can be seen that the measurement result and the theoretical value have the same trend with frequency, and the higher the frequency, the higher the frequency. The result is more accurate, and this measurement method is not sensitive to the error of the distance between the vector hydrophone and the sample.

3 Experimental research and data processing

The hardware composition block diagram of the measurement system is shown in Figure 11. The system consists of a dry end and a wet end. The dry end is mainly composed of arbitrary signal generator, power amplifier, vector hydrophone conditioning circuit and signal collector, etc., which are used for signal generation, transmission and acquisition. The wet end is mainly composed of a transmitting transducer, a low-frequency two-dimensional vector hydrophone and a sample to measure the sample. The wet end is placed in an anechoic pool with a geometric size of 25 m×15 m×10 m, and the sound center is located 5 m underwater. The pool is muffled on six sides, and the lower limit of sound absorption is 2 kHz. The sample to be tested is an aluminum plate with a geometric size of 1m×1m×0.006 m. The transmitter transducer is suspended on the edge of the vehicle above the pool, and the distance H from the sample is 4.95 m. The sample is fixed on the lifting and rotating device, and the sample can be rotated at an angle during measurement and moved smoothly in three dimensions. The vector hydrophone is placed at the front end of the sample, and the distance d from the surface of the sample is 5.5 cm. The transmitting transducer is a cylindrical sound source, and Figure 12 shows its transmission voltage response curve.

It can be seen from Fig. 12 that the transmitting transducer has a poor radiation ability below 2.5 kHz. The effective working frequency band of the low-frequency two-dimensional vector hydrophone is 1 ~ 12 kHz. During deployment, the vector Vy channel points to the sample to be tested, and Vx points to the wall of the pool. First transmit the 16-order pseudo-random sequence to identify and measure.

Figure 12 Transmitting voltage response curve of the transmitting transducer

System transfer function, and design inverse filter. Figure 13 shows the transfer function identification results of the measurement system. In the figure, hp(f), hvx(f) and hvy(f) are the measured values of the transfer function of the sound pressure channel, vector Vx channel and Vy channel of the measurement system respectively; hpinv( f), hvxinv(f) and hvyinv(f) are the designed inverse filter transfer function respectively.

It can be seen from Figure 13 that the vector Vx channel transfer function identification result is invalid. This is because in the above deployment situation, the "pit" of the vector hydrophone Vx channel is facing the sound source, and the signal received by this channel is only the pool. The wall reflects the acoustic signal, so the system identification result is inaccurate. Keep the spatial position and orientation of the transmitting transducer and the vector hydrophone transducer unchanged, put down the sample, and transmit the Butterworth pulsed acoustic signal with a bandwidth of 500 to 12.5 kHz. Figure 14 shows the original data and modified signal waveforms received by each channel of the vector hydrophone. It can be seen from Figure 14 that the time-domain waveform of the signal after the inverse filter correction becomes regular and the energy is more concentrated. Then calculate the time delay of the direct sound and the reflected sound diffraction sound from the sample edge according to the spatial layout parameters of the measurement system, and add windows to intercept the useful data, and calculate the normal sound reflection coefficient of the sample as shown in Figure 15.

Figure 15 shows the measurement results before and after compensation. It can be seen that the measurement result of the transfer function of the uncompensated measurement system has a large error and is almost invalid. The measurement accuracy is greatly improved after the post-inverse filter processing. When the frequency is greater than 2.5kHz, the measurement error after the post-inverse filter correction is small, and the measurement result below 2.5kHz has a large error. The reason is that the low-frequency transmission capability of the transmitting transducer is limited, and the low-frequency components of the signal are submerged in the background noise, so the measurement result is poor.

4 Conclusion

This paper proposes a method for measuring the normal acoustic reflection coefficient of underwater acoustic materials based on a single vector hydrophone. This method will pulse.The combination of impulse emission technology, vector signal processing technology and post-inverse filter technology, through the post-inverse filter technology to receive the vector hydrophone.

Data is compensated, the signal distortion caused by the transfer function of the measurement system is suppressed, and the edge diffraction sound and multipath of the sample are eliminated in the time domain.Signal interference improves measurement accuracy. The measurement principle is deduced theoretically, the influence of measurement system error is studied through numerical calculation and simulation, and experimental research is carried out. The numerical calculation and simulation results show that the measurement method described in this article has certain requirements for the signal-to-noise ratio; Inaccurate and insensitive system deployment. The experimental results show that the method described in this paper can effectively realize the free-field large-scale measurement of the normal acoustic reflection coefficient of underwater acoustic materials, but due to the limitation of the low-frequency radiation ability of the transmitting transducer, the low-frequency measurement error is relatively large.