CN113359207A

CN113359207A - Terahertz radar-based sound-induced water surface micro-motion feature extraction method and device

Info

Publication number: CN113359207A
Application number: CN202110619739.0A
Authority: CN
Inventors: 邓彬; 汤斌; 杨琪; 王宏强; 郭超
Original assignee: National University of Defense Technology
Current assignee: National University of Defense Technology
Priority date: 2021-06-03
Filing date: 2021-06-03
Publication date: 2021-09-07
Anticipated expiration: 2041-06-03
Also published as: CN113359207B

Abstract

The application relates to a terahertz radar-based sound-induced water surface micro-motion feature extraction method and device. The method comprises the following steps: the method comprises the steps of obtaining an echo signal obtained after a terahertz radar detects a target water surface, extracting a corresponding one-dimensional range profile based on the echo signal, obtaining an original vibration signal of the target water surface according to the one-dimensional range profile in combination with a phase ranging method, sequentially carrying out wavelet threshold filtering and Kalman filtering on the original vibration signal to obtain a real vibration signal of the target water surface, and finally carrying out time-frequency analysis according to the real vibration signal to obtain a sound-induced water surface micro-motion characteristic. By adopting the method, the effective characteristics of the sound-induced water surface micro motion can be accurately extracted under the actual condition that the water surface has large fluctuation (the lake surface and the sea surface).

Description

Terahertz radar-based sound-induced water surface micro-motion feature extraction method and device

Technical Field

The application relates to the technical field of radar detection of water surfaces, in particular to a terahertz radar-based sound-induced water surface micro-motion feature extraction method and device.

Background

The underwater information network is one of the important components of the marine environment guarantee system in China, is constructed to acquire underwater information in time, can effectively implement underwater monitoring, and can be used for oceanographic data acquisition, underwater target detection, abnormal earthquake and volcanic activity monitoring on the seabed and the like. However, the ocean is used as a huge barrier, electromagnetic waves in most frequency bands hardly exceed the barrier, and generally can only propagate for several meters to dozens of meters, so that the underwater extension of an information data chain is seriously hindered.

In recent years, the development of underwater acoustic basic theory and technology provides an effective way for realizing water-air cross-medium information transmission. The acoustic wave is the only known information carrier which can be remotely transmitted in seawater, the attenuation of acoustic wave signals in the ocean along with the distance is more than 1000 times smaller than that of electromagnetic waves, and underwater medium-and-long-distance communication networking mainly depends on an underwater acoustic communication mode. However, how to obtain underwater communication information from the air by using electromagnetic waves requires research on a mechanism of forming a water surface by exciting a sound source and a method of extracting a weak micro-motion feature of the water surface under a strong clutter noise condition.

At present, certain progress is made in the aspect of laser detection sound-induced water surface communication at home and abroad, but laser spots are generally small, and stable target tracking and detection are difficult to realize. In the aspect of microwave sound-induced water surface detection, as early as 1972, the institute of electronics of university of california, usa utilizes a microwave radar to develop a series of preliminary experiments of sound-induced water surface micro-motion detection, and discusses the feasibility of microwave radar water-air span medium communication; in 2018, a related research group of the national academy of science and technology of the Massachusetts is based on millimeter wave radar micro-motion detection, the principle verification of cross-medium underwater Acoustic-RF communication (TARF) is developed, a series of research results are obtained by utilizing the millimeter wave radar, but the experimental environment is ideal, and the research on clutter interference suppression under the condition of large water surface fluctuation is not deep enough.

Disclosure of Invention

In view of the above, it is necessary to provide a terahertz radar-based method and apparatus for extracting a micro-motion feature of a water surface by sound, which can solve at least one of the technical problems.

The application provides a terahertz radar-based sound-induced water surface micro-motion feature extraction method, which comprises the following steps:

acquiring an echo signal, wherein the echo signal is obtained after a terahertz radar is adopted to detect a target water surface;

extracting a corresponding one-dimensional range profile based on the echo signal, and obtaining an original vibration signal of the target water surface according to the one-dimensional range profile and a phase ranging method;

performing wavelet threshold filtering on the original vibration signal to remove noise of water surface clutter to obtain an original vibration signal which is subjected to primary denoising, and performing Kalman filtering on the original vibration signal which is subjected to primary denoising to remove phase noise brought by a terahertz radar to obtain a real vibration signal of a target water surface;

and performing time-frequency analysis according to the real vibration signal to obtain the sound-induced water surface micro-motion characteristics.

In one embodiment, the extracting the corresponding one-dimensional range profile based on the echo signal comprises:

acquiring a reference target position, and obtaining a reference echo signal according to the distance between the reference target position and the terahertz radar;

and calculating difference frequency signals of the echo signals and the reference echo signals, and performing fast Fourier transform on the difference frequency signals to obtain the one-dimensional range profile.

In one embodiment, the obtaining of the original vibration signal of the target water surface according to the one-dimensional range profile in combination with a phase ranging method includes:

extracting delay phases of distance units where targets of all points in the target horizontal plane are located according to the one-dimensional distance image;

and carrying out phase unwrapping processing on the delay phase corresponding to each point target to obtain an original vibration signal of the target water surface.

In one embodiment, wavelet thresholding the original vibration signal comprises:

performing wavelet decomposition on the original vibration signal to obtain a wavelet coefficient;

performing threshold quantization processing on the wavelet coefficients according to a selection rule and a quantization rule of a preset threshold;

and performing inverse wavelet transform on the wavelet coefficient subjected to threshold quantization processing to obtain an original vibration signal subjected to primary denoising.

In one embodiment, performing kalman filtering on the primarily denoised original vibration signal includes:

carrying out windowing pretreatment on the original vibration signal subjected to initial denoising, and then constructing an autoregressive model;

constructing a predicted value according to the autoregressive model, and obtaining a water surface vibration signal autoregressive model containing terahertz radar phase noise based on the predicted value and the autoregressive model;

and obtaining the real vibration signal according to the water surface vibration signal autoregressive model and a Kalman filtering formula.

In one embodiment, the predicted value is obtained by performing parameter estimation on the autoregressive model by using a linear prediction analysis estimation method.

In one embodiment, the acoustically induced surface micromotion feature comprises frequency information emitted by an underwater sound source.

A terahertz radar-based acoustically-induced water surface micro-motion feature extraction device, comprising:

the echo signal acquisition module is used for acquiring an echo signal, wherein the echo signal is a radar echo signal obtained after a terahertz radar is adopted to detect a target water surface;

the original vibration signal obtaining module is used for extracting a corresponding one-dimensional range profile based on the echo signal and obtaining an original vibration signal of the target water surface according to the one-dimensional range profile and a phase ranging method;

the real vibration signal obtaining module is used for performing wavelet threshold filtering on the original vibration signal to remove noise of water surface clutter to obtain an original vibration signal which is subjected to primary denoising, and then performing Kalman filtering on the original vibration signal which is subjected to primary denoising to remove phase noise brought by a terahertz radar to obtain a real vibration signal of a target water surface;

and the sound-induced water surface micro-motion characteristic obtaining module is used for carrying out time-frequency analysis according to the real vibration signal to obtain sound-induced water surface micro-motion characteristics.

A computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program:

A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of:

According to the sound-induced water surface micro-motion feature extraction method, device, computer equipment and storage medium based on the terahertz radar, the terahertz radar and the phase ranging method are adopted to carry out high-precision extraction on the phase features of sound-induced water surface micro-motion, the phase features are converted into original vibration signals of a target water surface, wavelet threshold filtering is adopted to remove water surface interference signals with large amplitude in the original vibration signals, Kalman filtering is adopted to remove phase noise caused by the terahertz radar in the original vibration signals, and finally the sound-induced water surface micro-motion feature extraction under the condition of strong clutter is achieved.

Drawings

FIG. 1 is a schematic flow chart of a method for extracting sound-induced water surface micro-motion features in one embodiment;

FIG. 2 is a schematic diagram of phase unwrapping in one embodiment;

FIG. 3 is a schematic flow chart of a radar phase ranging algorithm in one embodiment;

FIG. 4 is a diagram illustrating wavelet threshold quantization rules in one embodiment;

FIG. 5 is a schematic diagram of Kalman filtering in one embodiment;

FIG. 6 is a schematic flow chart of a wavelet-Kalman signal filtering algorithm in one embodiment;

FIG. 7 is a schematic flow chart of a method for extracting sound-induced water surface micro-motion features in another embodiment;

FIG. 8 is a schematic diagram of an original signal waveform and corresponding time domain in a simulation verification;

FIG. 9 is a schematic diagram of a wavelet threshold filtered signal waveform and corresponding time domain in a simulation verification;

FIG. 10 is a schematic diagram of a Kalman filtered signal waveform and corresponding time domain in simulation verification;

FIG. 11 is a schematic diagram of a single frequency test source signal in experimental data;

FIG. 12 is a graph of a chirp test source signal in experimental data;

FIG. 13 is a schematic diagram of a processing result of an underwater 0.2m interference-free single-frequency signal in experimental data;

FIG. 14 is a schematic diagram of a processing result of an underwater 0.2m interference-free linear frequency modulation signal in experimental data;

FIG. 15 is a schematic diagram of a processing result of an underwater 0.2m band interference single-frequency signal in experimental data;

FIG. 16 is a schematic diagram of a result of processing 0.2m underwater interfering chirp signals in experimental data;

FIG. 17 is a block diagram showing the structure of an apparatus for extracting a characteristic of a sound-induced surface micro motion in one embodiment;

FIG. 18 is a diagram illustrating an internal structure of a computer device according to an embodiment.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.

As shown in fig. 1, the application provides a terahertz radar-based method for extracting a sound-induced water surface micro-motion feature, which comprises the following steps:

step S100, obtaining an echo signal, wherein the echo signal is a radar echo signal obtained after a terahertz radar is adopted to detect a target water surface;

step S110, extracting a corresponding one-dimensional range profile based on the echo signal, and obtaining an original vibration signal of the target water surface according to the one-dimensional range profile and a phase ranging method;

step S120, performing wavelet threshold filtering on the original vibration signal to remove noise of the water surface clutter to obtain an original vibration signal which is subjected to primary denoising, and performing Kalman filtering on the original vibration signal which is subjected to primary denoising to remove phase noise brought by a terahertz radar to obtain a real vibration signal of the target water surface;

and S130, performing time-frequency analysis according to the real vibration signal to obtain the sound-induced water surface micro-motion characteristics.

In this application, can bring the micro-vibration of water-air interface to the propagation by sound source signal under water and promptly cause the surface of water, and this micro-vibration is hidden the information of sound source signal, can survey and extract the fine motion information of sound to causing the surface of water through the radar, surveys the information below the surface of water in proper order.

In step S100, the terahertz radar is a radar that detects using an electromagnetic wave in the terahertz frequency band. The Terahertz (THz) frequency band generally refers to electromagnetic waves with the frequency between 0.1THz and 10THz (corresponding to the wavelength of 30um-3mm), is short in wavelength, high in phase sensitivity and Doppler sensitive, and has unique advantages in detection and acquisition of micro-vibration on the water surface. The characteristic of short wavelength of terahertz radar is utilized to obtain high-precision sound-induced water surface vibration information.

In step S110, the extracting of the corresponding one-dimensional range profile based on the echo signal includes:

and calculating difference frequency signals of the echo signals and the reference echo signals, and performing fast Fourier transform on the difference frequency signals to obtain a one-dimensional range profile.

Specifically, based on a theoretical model of the sound-induced water surface, it can be known that the surface acoustic wave caused by the underwater sound source is a sine wave, and the form of the terahertz radar transmitting LFM (linear frequency modulation) signal is as follows:

in the formula (1.1), the first and second groups,

for radar fast time, t_mSlow time, full time

The pulse repetition period being T, f_cIs the center frequency, T_pIs the pulse duration, gamma is the frequency modulation rate,

and recording the distance from the position of the reference target to the radar as R_refThe distance from a certain point target to the radar is R_iThen reference echo signal

And point target to echo signal

Comprises the following steps:

the point target refers to a strong scattering point in the water surface of the target, and each point has effective information of the water surface micro-motion characteristic.

Reference echo signal

And point target to echo signal

The difference frequency signal of the two is

Namely:

fast fourier transform of the fast time is performed on the difference frequency signal formula (1.3):

of the 3 phase terms in equation (1.4), the first term is the doppler term, the second term is the residual video phase, and the third term is the echo envelope tilt term, where R_ΔIs the distance difference between the reference object and the point object.

The formula (1.4) is a one-dimensional range profile extracted from the echo signal. When echo signals are processed, the distance from a terahertz radar (hereinafter referred to as a radar) to a target water surface is unimportant, and what is important is that the micro-motion distance of the target water surface changes, so that when a one-dimensional distance image is extracted, a reference target position close to the target water surface is also introduced to analyze the water surface micro-motion distance.

In step S110, obtaining the original vibration signal of the target water surface according to the one-dimensional range profile and the phase ranging method includes:

Specifically, the delay phase of the range cell in which the point target is located is extracted, i.e., in equation (1.4), the delay phase is extracted

The time phase. When R is_ΔWhen the second term and the third term are negligible, the extracted phase is

The change of the distance from the target to the reference can be inverted by the change of the phase.

From a macroscopic analysis, the understanding is more intuitive. In the process of propagating the electromagnetic wave, the phase changes to 2 pi for every distance of one wavelength lambda. For radar echo, the target position changes by a distance of one wavelength λ, the electromagnetic wave needs to propagate by a distance of 2 λ, the phase change is 4 π, and according to this relationship:

according to wavelength λ ═ c/f_cThe following can be obtained:

it can be seen from the formula (0.6) that the same distance variation causes a larger phase variation for a higher radar frequency band. Namely, the terahertz radar has natural advantages in the aspect of measuring the target micromotion.

The phase information extracted from the one-dimensional range profile is blurred, and unwrapping is required to obtain the real phase information.

The phase proposed by the target range bin is generally limited to-pi to pi when R is_ΔWhen the induced phase change exceeds the range, the real phase change is added or subtracted by integral multiple of 2 pi, so that the phase change falls in the range, namely, the phase winding phenomenon occurs. In this case, the extracted phase cannot directly reflect a change in distance, and phase unwrapping is required. The method for performing phase unwrapping by comparing phases of adjacent sampling points is adopted, and the specific process is as follows:

where k is an integer greater than 1, e (k) is the difference between the true phase value and the measured phase value of the kth slow time sample point, e (1) is 0, and φ (k) is the measured phase value of the kth slow time sample point. So the phase after unwrapping is:

φ_Δ(k)＝φ(k)+e(k) (0.8)

in the formula (1.8), phi_ΔIs the phase after unwrapping.

Theoretically, the unwrapped phase should be equal to the true phase value. However, since the phases of the sampling points are not continuous, the true phase variation between adjacent sampling points is unknown, and when the true phase variation between adjacent sampling points exceeds a certain range, the true phase variation cannot be obtained by the phase unwrapping algorithm. It is analyzed in detail below.

When the true phase variation between adjacent sampling points is greater than 2 pi, as shown in fig. 2(a), the true phase difference between point 2 and point 3 is greater than 2 pi, point 3' is the extracted phase measurement, and after processing by the above-mentioned unwrapping algorithm, unwrapped point 3 ″ is obtained, obviously, the unwrapped phase is not the true phase of point 3. When the true phase change between adjacent sampling points is between pi and 2 pi, as shown in fig. 2(b), the phase change between point 5 and point 6 is between pi and 2 pi, point 6' is a measured value of the phase of the point, and after the above-mentioned unwrapping algorithm, unwrapped point 6 ″ is obtained, but the point cannot reflect the true phase of the point. So to avoid this, the true phase difference between adjacent sampling points must be less than pi, which can be achieved by increasing the number of sampling points by increasing the radar Pulse Repetition Frequency (PRF).

Assuming that the point target is in simple harmonic vibration, the form is Acos (ω t), a is amplitude, and ω is angular frequency, the maximum speed of the target water surface vibration is a ω. Noting that the radar pulse repetition frequency is f_RPFThen, according to the above description, it can be known that the phase variation of the neighboring points is maximum:

making the change less than π, one can deduce:

the amplitude of the sound-induced water surface is very small, and is in the micron order, and the traditional radar ranging method cannot achieve high precision. The phase ranging method reflects the movement of a target by means of the change of the phase of an echo, and is high in precision. After the radar is improved to the terahertz frequency band, the ranging precision can be further improved, and therefore the sound-induced water surface vibration signal is extracted.

The flow of the acoustic water surface micro-motion extraction algorithm based on phase ranging is shown in fig. 3, and after echo signals are processed by Decirp, FFT (fast Fourier transform) is carried out to obtain a one-dimensional distance image of a target water surface. And extracting the phase of the distance unit where the target water surface is located, and obtaining a displacement sequence of water surface vibration, namely an original vibration signal of the target water surface after phase unwrapping.

In step S120, the original vibration signal of the target water surface obtained by the phase distance measurement method contains interference of water surface clutter and phase noise, the amplitudes of the interference signal and the noise signal are relatively large, and clutter needs to be suppressed for extracting micron-sized sound-induced water surface micro-motion information, so that a sound-induced water surface radar detection signal extraction algorithm based on wavelet-kalman filtering is provided.

Firstly, processing an original vibration signal through wavelet threshold filtering to obtain a water surface interference signal, wherein the water surface interference signal has the characteristics of larger energy and lower frequency, then removing the water surface interference signal by using the original water surface vibration signal, and the residual signal contains water surface vibration caused by an underwater sound source.

Wavelet threshold filtering of the original vibration signal comprises:

In the above steps, the selection rule and quantization rule of the threshold value have a large influence on the denoising effect.

The application of engineering practice shows that the minimax and rigrsure threshold rules are conservative, and the method is suitable for the situation that the low-frequency part of a useful signal has large energy and the high-frequency part is not or rarely superposed with noise. Compared with sqtwolog and heursure threshold rules, the two denoising effects are more obvious, but the high-frequency part of the signal is easily filtered, so that signal distortion is caused. In the actual processing process, the selection rule can be determined according to the de-noised effect.

After determining the threshold, there are generally two quantization rules for quantizing the coefficients, including a hard threshold and a soft threshold, as shown in fig. 4.

Wherein, the hard threshold value is reserved when the absolute value of the decomposed wavelet coefficient is larger than the threshold value; when the absolute value of the coefficient is less than the threshold value, directly setting zero:

wherein, the soft threshold value is reserved when the absolute value of the decomposed wavelet coefficient is larger than the threshold value; when the absolute value of the coefficient is less than the threshold, it is subtracted by the threshold:

hard threshold quantization can achieve a higher signal-to-noise ratio but is prone to local jitter. Soft thresholding achieves a low signal-to-noise ratio and a hard threshold, but the processed signal is more gradual. In the actual processing process, a proper threshold rule and a proper quantification mode can be selected according to the characteristics of the signals to obtain an expected result.

After the original vibration signal after the initial denoising is obtained through wavelet threshold filtering, the signal is filtered again through Kalman filtering. Performing Kalman filtering on the primary denoised vibration signal comprises the following steps:

carrying out windowing pretreatment on the original vibration signal subjected to initial denoising, and then constructing an autoregressive model on the original vibration signal;

and obtaining a real vibration signal according to the water surface vibration signal autoregressive model and a Kalman filtering formula.

Specifically, the kalman filter can be divided into two steps as a whole: firstly predicting and then correcting.

After wavelet threshold filtering, the vibration signal of the sound-induced water surface can be regarded as a signal model containing additive noise:

y(n)＝x(n)+v(n) (0.13)

in equation (1.13), x (n), i.e. the signal we want to find, represents the vibration signal caused by the underwater sound source, v (n) represents the phase noise of the radar, and v (n) is uncorrelated with the vibration signal x (n). Because the vibration of the water surface is a continuous process and must be accumulated for a certain time to achieve a certain amplitude of vibration, the water surface vibration signal has the characteristic of being stable for a short time. By windowing the signal, the signal within a window can be approximated as a stationary random signal, and thus the vibration signal x (n) can be described as an autoregressive process where a white noise excitation signal is output through an all-pole linear system. Let x (n) be expressed as an Auto-Regressive (AR) model of order p:

in equation (1.14), p is the order of the AR model, a_j＝1,…,pIs a linear prediction coefficient, w (n) is a mean of 0, and a variance of

White gaussian noise.

The Kalman filtering algorithm is a filtering algorithm based on a state space, and the accuracy of a signal model has a great influence on the filtering effect. In one embodiment, a Linear Prediction Coding (LPC) estimation method is used to perform parameter estimation on the AR model of the signal. The LPC algorithm is established on an AR model of a signal, and the predicted value of the signal is recorded as

The difference between the signal and the predicted value, i.e. the linear prediction error, ise, (n), then:

the prediction error is expressed as:

through the transfer function H (z) of the system, the linear prediction coefficient a is solved_j＝1,…,pMinimizing the mean square error of the prediction, i.e. E²(n)]And minimum. Find E [ E ]²(n)]Partial derivatives of the coefficients and making the result 0, i.e.:

will be provided with

Substituting to obtain:

-2E[e(n)x(n-k)]＝0 (0.18)

the autocorrelation sequence, where R (n) is x (n), is:

R(k)＝E[x(n)x(n-k)] (0.20)

substituting into equation (0.19) yields:

equation (1.21) is expressed in matrix form:

wherein,

prediction coefficient a obtained by the above equation_j＝1,…,pCan order E [ E ]²(n)]Minimum, record E_p＝E[e²(n)]_minNamely:

it is represented in matrix form:

combining equations (0.22) and (0.24), we can:

the above equation is the Yule-Walker equation of the AR model, which is combined with the linear prediction coefficient a in the model_j＝1,…,pIs a linear system of equations and this relationship is unique. Because the autocorrelation matrix of the formula is a Toeplitz structure, a Levinson-Durbin algorithm can be used for solving, so that various linear prediction coefficients are obtained, and a water surface vibration signal AR model containing radar phase noise is obtained.

And establishing a Kalman state equation and an observation equation according to the AR model with the noise signal. The equations (0.14) and (0.13) are rewritten as matrix equations:

wherein x (n) ═ x (n-p +1), x (n-p +2), …, x (n)]^T，H＝G^T＝[0,…0,1]_1×p，

The first equation is a state equation and the second equation is an observation equation. X (n) is the state variable at time n, the true value of the signal, and y (n) is the observed value of the signal at time n. A is a state transition matrix composed of linear prediction coefficients, and H is an observation matrix. w (n) is process noise and v (n) is observation noise.

The kalman filter model assumes that w (n) and v (n) are uncorrelated white noise and are also uncorrelated with the state variables x (n) of the system. Let w (n), v (n) be mean 0 and variance be

Let Q and R be covariance matrices of w (n) and v (n):

then:

from the formula of kalman filtering, one can obtain:

wherein k (n) represents a kalman gain;

a covariance matrix representing the prediction error, i.e. the covariance matrix of the prior error;

a covariance matrix representing the estimation error, i.e. a covariance matrix of the a posteriori errors; y (n) represents an observed value at time n;

the predicted value of the state at the time n according to the signal state value at the time n-1 is shown;

represents the observed value y (n) and the predicted value based on the signal at the time n

And (4) estimating the true value of the signal at the time n, namely the value of the signal after Kalman filtering. As can be seen from the above equation, given the initial values of the signal X (0) and

namely, the state estimation value of the real value of the signal at the time n can be recurred through the observed value at the time n.

The kalman filtering can be divided into two steps as a whole: prediction is performed first and then correction is performed as shown in fig. 5. The Kalman filtering calculates the prior error covariance at the current moment through the posterior error covariance at the previous moment, and then predicts the signal at the current moment by using the signal estimation value at the previous moment to obtain a predicted value; and then calculating the Kalman gain at the current moment, obtaining an estimated value of the signal at the current moment through the observed value of the signal at the current moment and the predicted value of the signal, and calculating the covariance of the posterior error. By continuously iterating, state estimates at any time can be obtained.

The flow of the sound-induced water surface micro-motion feature extraction algorithm based on the combination of the wavelet threshold method and the Kalman filtering is shown in FIG. 6.

The original vibration signals of the target water surface extracted by the radar phase ranging algorithm contain water surface vibration caused by an underwater sound source, interference signals caused by natural fluctuation of the water surface and phase noise caused by radar. The interference signal has larger energy and lower frequency, and the phase noise has higher frequency and smaller energy. The original vibration signal is firstly subjected to wavelet decomposition, a proper threshold quantization wavelet decomposition coefficient is selected, and a low-frequency signal is reconstructed according to the quantized wavelet coefficient. The signal component extracted by the wavelet threshold method is a low-frequency interference signal of natural fluctuation of the water surface or external interference, and the original vibration signal subtracts the extracted interference signal to obtain the water surface vibration caused by an underwater sound source and the phase noise caused by a radar. The phase noise of the radar is additive noise and is irrelevant to the water surface vibration caused by an underwater sound source. The water surface vibration signal has a short-time stable characteristic, and after windowing pretreatment is carried out on the signal, data in each window can be approximated to a stable signal, so that the requirement of a Kalman filtering algorithm is met. According to the steps of the Kalman filtering algorithm, an AR model of the signal is established, linear prediction coefficients are obtained through an LPC algorithm and are substituted into an equation of the Kalman filtering algorithm, and finally the filtered real vibration signal is obtained, wherein the whole flow of the steps is shown in FIG. 7.

In step S130, after wavelet threshold filtering and kalman filtering, the water surface fluctuation interference and radar phase noise are effectively filtered, the water surface vibration signal caused by the underwater sound source is retained, and the sound-induced water surface characteristics are obtained by performing time-frequency analysis on the real vibration signal.

In one embodiment, the acoustically induced surface micromotion feature includes frequency information emitted by an underwater sound source.

Based on the method for extracting the sound-induced water surface micro-motion characteristics, the application provides related simulation verification data as follows:

the interference signal brought by the natural fluctuation of the water surface is assumed to be a sinusoidal signal combination of 5Hz and 10Hz with the amplitude of 1mm, the signal exists continuously within the simulation time of 2s, and the sampling rate of the signal is set to be 5 KHz. The sound source causes a 100Hz sinusoidal signal with a water surface vibration of 4um, starting from 0.5s and lasting for 1 s. The radar phase noise is gaussian noise with a signal-to-noise ratio of 5dB, as shown in fig. 8, where fig. 8(a) is the original signal waveform and fig. 8(b) is the time-frequency diagram.

It can be seen from the time-frequency diagram that the water surface vibration information caused by the sound source cannot be directly obtained from the time-frequency diagram because the interference signal energy is large. The original signal is subjected to wavelet thresholding processing, the wavelet basis is db6, the threshold rule is rigrsure, the number of decomposition layers is 5, and the processing result is shown in fig. 9, where fig. 9(a) is a time domain waveform diagram after wavelet filtering, and fig. 9(b) is a time-frequency diagram after wavelet filtering.

After filtering by a wavelet threshold method, water surface vibration signals caused by an underwater sound source can be seen from a time domain oscillogram and a time-frequency graph, the amplitude of vibration is about 4um, the frequency is 100Hz, and the starting time and the duration time of the signals are the same as the set time. In general, the wavelet threshold method can filter low-frequency interference signals with large amplitude, but has no influence on high-frequency noise signals, and a great amount of phase noise exists in the signals as can be seen from time domain waveforms and time-frequency graphs.

The signal is further processed, phase noise is filtered by using a kalman filtering algorithm, a hanning window with a window length of 25ms is used as a parameter of the kalman filtering algorithm, the AR order is set to 20, and the processing result is shown in fig. 10, where fig. 10(a) is a time domain waveform diagram after kalman filtering, and fig. 10(b) is a time-frequency diagram after kalman filtering.

After Kalman filtering, radar phase noise is effectively filtered, a water surface vibration signal caused by an underwater sound source is reserved, feasibility of the algorithm is verified, and a good effect can be obtained through wavelet threshold filtering and Kalman filtering.

In addition, another relevant experimental validation data is provided in the present application for further demonstrating the feasibility of the method, as follows:

the terahertz radar with the frequency band of 0.12THz is selected for the experiment, the radar vertically irradiates the water surface, the distance to the water surface is 0.6m, the overwater platform can not vertically move, and the distance can not be adjusted. The radar parameter settings are shown in table 1, where the sampling frequency is a fast-time sampling frequency. The length of a water pool used in the experiment is 20m, the width of the water pool is 8m, and the depth of the water pool is 7m, and six surfaces of the water pool are covered with conical rubber wedge plates which are used for absorbing incident sound waves, reducing interface reflection and forming a free sound field environment. Two movable platforms are arranged on the pool and can be used for carrying out radar detection water surface experiments. The underwater sound source adopts an underwater loudspeaker UW30, the output power of UW30 is 30W, and the impedance is 8 omega. The UW30 has good low-frequency response, the lowest transmitting signal frequency is 100Hz, and the highest transmitting signal frequency can reach 10 KHz.

TABLE 1 Experimental Radar parameters

The sound source transmits signals in two forms:

(1) the single-frequency signal is continuously and intermittently transmitted, starting from 50Hz, at intervals of 50Hz, continuing to 500Hz, and then at intervals of 100Hz, continuing to 2 KHz. Each frequency point signal lasts for 2s and is separated by 2 s. The signal form is shown in fig. 11, in which fig. 11(a) is a signal time-domain waveform diagram, and fig. 11(b) is a signal time-frequency diagram.

(2) The total of 4 sets of chirp signals are transmitted, the signal duration is 2s, the interval time is 2s, the starting frequency is 100Hz, the bandwidths are respectively 200Hz, 300Hz, 400Hz and 500Hz, and the signal form is shown in fig. 12, wherein fig. 12(a) is a signal time domain waveform diagram, and fig. 12(b) is a signal time-frequency diagram.

A sound source is placed 0.2m below the water surface, and according to the method for extracting the sound-induced water surface micro-motion characteristics, the sound-induced water surface micro-motion characteristics under the conditions of a still water surface and an interference water surface are extracted as follows.

(1) The sound source is located at 0.2m under water and is on the surface of the flat water

When the sound source is located at 0.2m underwater, and the sound source emission signal is a single-frequency signal in a flat water surface state, the algorithm processing process and the result are shown in fig. 10, where fig. 13(a) is a one-dimensional distance image, fig. 13(b) is phase and phase unwrapping, fig. 13(c) is an original vibration signal, fig. 13(d) is an original vibration signal time-frequency diagram, fig. 13(e) is a vibration signal after wavelet threshold filtering, fig. 13(f) is a vibration signal time-frequency diagram after wavelet threshold filtering, fig. 13(g) is a vibration signal after kalman filtering, and fig. 13(h) is a vibration signal time-frequency diagram after kalman filtering.

The process of the chirp signal is similar to that of the single frequency signal, and only the result finally processed by the algorithm is shown here, as shown in fig. 14, where 14(a) is the vibration signal after kalman filtering, and fig. 14(b) is the time-frequency diagram of the vibration signal after kalman filtering.

When the water surface is in a calm state, the final processing result of the algorithm is still relatively ideal, and interference signals caused by water surface fluctuation and phase noise caused by radar are obviously removed.

For the processing of single-frequency signals, the frequency points of the signals from 50Hz to 600Hz can be obviously seen from a time-frequency diagram, the duration time and the interruption time of the signals are consistent with those of the transmitted signals, and the signals of the frequency points above 600Hz cannot be seen, because the displacement of the water surface is too small due to the fact that the frequency points above 600Hz, and radar cannot detect the signals. The signal amplitude of the frequency point after 150Hz is reduced along with the increase of the frequency, and the variation trend is consistent with the theoretical model.

From the original water surface vibration signal, although no man-made interference is added, the state of the still water surface has micron-level natural disturbance, and the natural disturbance can be well filtered by a wavelet threshold method. From the water surface vibration signal after small wave filtering, the radar phase noise amplitude is about 1 μm, and can be well filtered after Kalman filtering, and the radar phase noise is also verified to accord with the setting of a Kalman filtering algorithm, namely the radar phase noise is Gaussian white noise and is irrelevant to the vibration signal.

The linear frequency modulation signals are processed, and four groups of frequency modulation signals with different modulation frequencies can be seen from a time-frequency result graph, wherein the change condition, the duration and the interval time of the signals are all consistent with those of the transmitted signals. After wavelet-Kalman filtering, a better time-frequency analysis result can be obtained.

The parameters are kept unchanged, artificial water surface interference is added, and according to the method provided by the invention, the experimental treatment results are as follows.

(2) The sound source is located at 0.2m under water and has interference

When the sound source is located at 0.2m underwater and artificial interference is added to the water surface, the processing flow of the single-frequency signal is shown in fig. 15, where fig. 15(a) is a one-dimensional distance image, fig. 15(b) is phase and phase unwrapping, fig. 15(c) is an original vibration signal, fig. 15(d) is an original vibration signal time-frequency diagram, fig. 15(e) is a vibration signal after wavelet threshold filtering, fig. 15(f) is a vibration signal time-frequency diagram after wavelet threshold filtering, fig. 15(g) is a vibration signal after kalman filtering, and fig. 15(h) is a vibration signal time-frequency diagram after kalman filtering.

The process of the chirp signal processing is similar to that of a single-frequency signal, and the result after algorithm processing is shown in fig. 16, where 16(a) is a vibration signal after kalman filtering, and fig. 16(b) is a time-frequency diagram of the vibration signal after kalman filtering.

From the processing results, after the artificial interference is added, the extracted original water surface vibration signal has larger interference, the time-frequency analysis result is poorer, the sound source frequency is basically submerged by the noise, and the water surface natural fluctuation and the radar phase noise are restrained to a certain degree from the result graphs of wavelet filtering and Kalman filtering, so that the effectiveness and the practicability of the method provided by the invention are verified.

The method for extracting the sound-induced water surface micro-motion characteristics based on the terahertz radar obtains ideal results by respectively carrying out simulation verification and experimental verification, and can prove that the method is really feasible and can effectively filter noise, so that the extracted sound-induced water surface micro-motion characteristics can truly reflect the condition of an underwater sound source.

In the method for extracting the sound-induced water surface micro-motion characteristics based on the terahertz radar, compared with the current sound-induced water surface detection by the laser and millimeter wave radar, the method has certain advantages, the problem that a laser detection light spot is small and stable target tracking and detection are difficult to realize is solved, in addition, the terahertz radar has high Doppler sensitivity, and micro-motion information with higher precision can be obtained by the characteristic of short wavelength, and particularly, the terahertz radar has strong measurement capability for micron-sized weak signals of the sound-induced water surface. By adopting the sound-induced water surface micro-motion feature extraction method based on wavelet-Kalman filtering, the method aims at the characteristic of high phase ranging precision of the terahertz radar, but is very easily influenced by water surface clutter and radar phase noise in the micro-motion feature extraction process, so that the method combining wavelet threshold value method filtering and Kalman filtering is pertinently provided, and water surface micro-motion information caused by a sound source can be effectively extracted. Moreover, the method has the characteristics of simplicity in implementation, good stability and universality and the like, and simulation and actual measurement experiments show that the method has good practicability.

It should be understood that, although the steps in the flowcharts of fig. 1, 3, 6 and 7 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 1, 3, 6, and 7 may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of performing the sub-steps or stages is not necessarily sequential, but may be performed alternately or alternatingly with other steps or at least some of the sub-steps or stages of other steps.

In one embodiment, as shown in fig. 17, there is provided a terahertz radar-based acoustic surface micro-motion feature extraction device, including: an echo signal obtaining module 200, an original vibration signal obtaining module 210, a real vibration signal obtaining module 220, and a sound-induced water surface micro-motion characteristic obtaining module 230, wherein:

the echo signal acquiring module 200 is configured to acquire an echo signal, where the echo signal is obtained after a terahertz radar is used to detect a target water surface;

an original vibration signal obtaining module 210, configured to extract a corresponding one-dimensional range profile based on the echo signal, and obtain an original vibration signal of the target water surface according to the one-dimensional range profile in combination with a phase ranging method;

a real vibration signal obtaining module 220, configured to perform wavelet threshold filtering on the original vibration signal to remove noise of the water surface clutter to obtain an original vibration signal that is primarily denoised, and then perform kalman filtering on the original vibration signal that is primarily denoised to remove phase noise caused by the terahertz radar to obtain a real vibration signal of the target water surface;

and the sound-induced water surface micro-motion characteristic obtaining module 230 is configured to perform time-frequency analysis according to the real vibration signal to obtain a sound-induced water surface micro-motion characteristic.

For specific limitations of the sound-induced water surface micro-motion feature extraction device based on the terahertz radar, reference may be made to the above limitations of the sound-induced water surface micro-motion feature extraction method based on the terahertz radar, and details are not repeated here. All modules in the sound-induced water surface micro-motion feature extraction device based on the terahertz radar can be completely or partially realized through software, hardware and a combination of the software and the hardware. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.

In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as shown in fig. 18. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to realize a terahertz radar-based sound-induced water surface micro-motion feature extraction method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.

Those skilled in the art will appreciate that the architecture shown in fig. 18 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.

In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program:

acquiring an echo signal, wherein the echo signal is a radar echo signal obtained after a terahertz radar is adopted to detect a target water surface;

performing wavelet threshold filtering on the original vibration signal to remove noise of the water surface clutter to obtain an original vibration signal which is subjected to primary denoising, wherein the wavelet threshold filtering cannot filter a high-frequency noise signal, and performing Kalman filtering on the original vibration signal which is subjected to primary denoising to remove phase noise brought by a terahertz radar to obtain a real vibration signal of a target water surface;

In one embodiment, the processor, when executing the computer program, further performs the steps of: the extracting of the respective one-dimensional range profile based on the echo signals comprises:

In one embodiment, the processor, when executing the computer program, further performs the steps of: the step of obtaining the original vibration signal of the target water surface according to the one-dimensional range profile and the phase ranging method comprises the following steps:

In one embodiment, the processor, when executing the computer program, further performs the steps of:

performing wavelet threshold filtering on the original vibration signal comprises:

performing Kalman filtering on the primary denoised vibration signal comprises:

In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:

In one embodiment, the computer program when executed by the processor further performs the steps of: the extracting of the respective one-dimensional range profile based on the echo signals comprises:

In one embodiment, the computer program when executed by the processor further performs the steps of: the step of obtaining the original vibration signal of the target water surface according to the one-dimensional range profile and the phase ranging method comprises the following steps:

In one embodiment, the computer program when executed by the processor further performs the steps of: performing wavelet threshold filtering on the original vibration signal comprises:

In one embodiment, the computer program when executed by the processor further performs the steps of: performing Kalman filtering on the primary denoised vibration signal comprises:

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.

The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims

1. The method for extracting the sound-induced water surface micro-motion features based on the terahertz radar is characterized by comprising the following steps of:

2. The method of claim 1, wherein the extracting a corresponding one-dimensional range profile based on the echo signal comprises:

3. The method for extracting the characteristics of the sound-induced water surface micro motion as claimed in claim 1, wherein the step of obtaining the original vibration signal of the target water surface according to the one-dimensional distance image and the phase ranging method comprises the following steps:

4. The method of extracting features of acoustically induced water surface micro-motion as claimed in claim 1, wherein wavelet threshold filtering the original vibration signal comprises:

5. The method of extracting the features of the sound-induced water surface micro motion as claimed in claim 4, wherein the Kalman filtering of the original vibration signal after the initial denoising comprises:

6. The method for extracting the characteristics of the sound-induced water surface micro motion, according to claim 5, wherein the predicted value is obtained by performing parameter estimation on the autoregressive model by using a linear prediction analysis estimation method.

7. The method of claim 1, wherein the acoustically induced water surface micro motion features comprise frequency information emitted by an underwater sound source.

8. A terahertz radar-based sound-induced water surface micro-motion feature extraction device is characterized by comprising:

the system comprises an echo signal acquisition module, a data acquisition module and a data processing module, wherein the echo signal acquisition module is used for acquiring an echo signal, and the echo signal is obtained after a terahertz radar is adopted to detect a target water surface;

9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.