Method for improving signal-to-noise ratio of underwater weak radiation noise measurement

Abstract

The invention provides a method for improving the signal-to-noise ratio of underwater weak radiation noise measurement. The background of the method is an underwater positioning algorithm and a time domain signal processing technology. In order to solve the problems of low signal-to-noise ratio and poor reproducibility of underwater weak radiation noise measurement in an open water area, the invention provides an underwater weak radiation noise signal-to-noise ratio improving method based on an excess sampling means, and the method comprises the following steps: carrying out time domain splitting on a measurement signal on the premise of keeping the frequency domain characteristic of a target signal by utilizing the characteristic of extremely poor noise time domain correlation so as to obtain a signal-to-noise ratio signal-to-noise ratio signal-to-noise ratio signal-to-noise ratio signal-to-noise ratio signal-to-noise ratio signal-to-noise ratio; and finally adding and averaging to obtain a target signal with a high signal-to-noise ratio. The method can be well applied to data processing of measurement experiments such as mobile sound source level calibration, low signal-to-noise ratio measurement and dynamic target intensity measurement, the measurement efficiency is improved, and the requirement for the noise level of an experimental water area is lowered.

Description

Technical Field

[0001] The invention relates to the field of measurement and testing, in particular to the field of acoustics (hydroacoustics), and mainly relates to a method for improving the signal-to-noise ratio of underwater weak radiation noise measurement. Background Art

[0002] The application of the present invention is based on underwater positioning algorithm and time domain signal processing technology.

[0003] At present, the commonly used methods to improve the signal-to-noise ratio are to take the average value of multiple measurements or to fit the measurement signal using the least squares method. These two signal processing methods have high requirements on the measurement environment and require good reproducibility of the measurement results. If the test is conducted in a wide water area with a lot of interference and changeable environmental conditions, there are many factors that affect the test results and it is difficult to control them manually. This will cause changes in the target signal, and multiple averaging and the least squares method cannot effectively improve the signal-to-noise ratio. Summary of the invention

[0004] The purpose of the present invention is to provide a method for improving the signal-to-noise ratio of underwater weak radiation noise measurement in order to solve the problems of low signal-to-noise ratio and poor reproducibility of underwater weak radiation noise measurement in open waters.

[0005] The objective of the present invention is achieved through the following technical solutions: A method for improving the signal-to-noise ratio of underwater weak radiation noise measurement, comprising the following steps: by performing arithmetic difference splitting on the collected original time domain measurement data, a set of derivative time domain data with a reduced sampling rate but completely identical signal-to-noise ratio and target signal characteristics is produced; utilizing the temporal irrelevance of the noise to process the split derivative time domain data, and obtaining time domain data with improved signal-to-noise ratio.

[0006] As a further improved technical solution, the specific steps are as follows:

[0007] Step 1: The time domain function expression of the sound wave excited by the sound source is S(t). When the sound wave reaches the sensor after t0, the propagation loss is 1-α, and the sensor sampling rate is f s , then the discrete signal collected by the sensor from time t = t0 is R[n], and R[n] is the original time domain data;

[0008] R[n]= α·S[n]+N[n] (1)

[0009] Where N[n] is the random noise signal measured by the sensor, and n is the element number of the data array collected by the acquisition card; if R[m] is the mth element in the data array collected by the sensor from time t=t0, then m=t0*f s ;

[0010] Step 2: When the sampling rate is high enough, that is, when the sampling rate of the acquisition card is fs Much larger than S f [n] When the frequency component has the maximum frequency, S f [n] is obtained by FFT transformation of S[n], and R[n] is split into x arrays according to formula (3). This is equivalent to measuring the same target x times under almost the same test environment, and the sampling rate corresponding to each measurement is f s / x;

[0011] R (x) [n'] = R[(n+1)·x] (3)

[0012] In the formula, R (x) [n'] is the xth array obtained after splitting with R[n] as the data source.

[0013] Step 3: Perform FFT transformation on the split array, add all the split signals and average them, and you will get:

[0014]

[0015] In the formula, S f [n] is the FFT array of S[n], is the FFT array of the xth array obtained after splitting S[n] as the data source; N f [n] is the FFT array of N[n], It is the FFT array of the xth array obtained after splitting with N[n] as the data source. is the result array after signal-to-noise ratio enhancement.

[0016] As a further technical solution, if time domain signals are required, By performing iFFT transformation, new time domain data with a higher signal-to-noise ratio than R[n] can be obtained.

[0017] The beneficial effects of the present invention are as follows: the present invention proposes a method for improving the signal-to-noise ratio of underwater weak radiation noise based on oversampling means. The method utilizes the characteristics of extremely poor time domain correlation of noise, and on the premise of retaining the frequency domain characteristics of the target signal, the measurement signal is split in the time domain, and finally the target signal with a high signal-to-noise ratio is obtained by adding and averaging. The method can be well applied in the data processing of measurement experiments such as mobile sound source level calibration, low signal-to-noise ratio measurement, and dynamic target intensity measurement, thereby improving measurement efficiency and reducing the requirements for the noise level in the experimental waters. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art or ordinary technicians, other drawings can be obtained based on these drawings without paying creative work. Figure 1 This is the simulation result of the method for improving the signal-to-noise ratio described in the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work belong to the protection scope of the present invention.

[0020] Now assume that there is a sound source in a certain water area that excites sound waves. The time domain function expression of the sound source excitation sound waves is S(t). The sound wave reaches the sensor after t0 time, the propagation loss is 1-α, and the sensor sampling rate is f s , then the discrete signal collected by the sensor from time t = t0 is R[n]:

[0021] R[n]= α·S[n]+N[n] (1)

[0022] Where N[n] is the random noise signal measured by the sensor, which is composed of environmental noise and hydrophone self-noise; n is the element number of the data array collected by the acquisition card. If R[m] is the mth element in the data array collected by the sensor since t=t0, then m=t0*f s .

[0023] If we perform FFT transformation on the discrete signal R[n], we get the spectrum array R of R[n] f [n], we can get:

[0024] R f [n] = α·S f [n]+N f [n] (2)

[0025] Where S f [n] is the FFT array of S[n], N f [n] is the FFT array of N[n], both of which are complex arrays. If the signal-to-noise ratio of the target frequency is less than 6dB, that is, α·S f [n] / N f [n]<2, the target signal α·S cannot be obtained f The value of [n].

[0026] Assume that the sampling rate of the acquisition card is f s Much larger than S f [n] is the maximum frequency of the frequency component. If R[n] is split into x arrays according to formula (3), it is equivalent to measuring the same target x times under almost the same test environment. The sampling rate corresponding to each measurement is f s / x.

[0027] R (x) [n'] = R[(n+1)·x] (3)

[0028] Then at this time,

[0029] R (x) [n'] =α·S[(n+1)·x]+N[(n+1)·x] (4)

[0030] For ease of writing, it can be written as,

[0031] R (x) [n'] = α·S (x) [n']+N (x) [n'] (5)

[0032] Performing FFT transformation on formula (5) yields

[0033] R f (x) [n'] = α·S f (x) [n']+N f( (x) [n'] (5)

[0034] If all the split signals are added and averaged, we get:

[0035]

[0036] For each group R f( (x) [n'], and its corresponding S f (x) The frequency domain information contained in [n'] is almost identical; f (x) [n'] is a noise signal, and its frequency domain information distribution should follow a normal distribution centered at 0. Then,

[0037]

[0038] at this time,

[0039]

[0040] It can be seen that when the sampling rate is high enough, this method can eliminate the influence of noise and improve the signal-to-noise ratio. The simulation results of this method are shown in Figure 1 shown.

[0041] Figure 1 The target signal in (a) is a sinusoidal pulse signal with a peak-to-peak value of 2Pa, and the sampling rate is 100 times that of the sinusoidal signal. The sum of the two gives the hydrophone receiving signal (c), with a signal-to-noise ratio of -40dB. After processing using the underwater weak radiation noise signal-to-noise ratio enhancement method based on oversampling, the comparison between the original target signal and the restored signal is shown in Figure (d).

[0042] It can be seen that the method proposed in the present invention can effectively suppress the influence of random noise.

[0043] Example:

[0044] 1. Place the hydrophone underwater according to the radiated noise field test plan and wait for the target to pass by;

[0045] 2. After the target passes, extract the sampling frequency measured by the hydrophone as f s The original time domain data R[n] of the experiment;

[0046] 3. Split R[n] into x arrays according to formula (3). At this time, the sampling rate is f s / x;

[0047] 4. Perform FFT transformation on the split array according to formula (5), and then insert it into formula (6) to obtain Improve the signal-to-noise ratio in the frequency domain;

[0048] 5. If time domain signals are needed, By performing iFFT transformation, new time domain data with a higher signal-to-noise ratio than R[n] can be obtained.

[0049] The signal-to-noise ratio improvement method provided by the present invention does not require multiple repeated measurements, and the signal-to-noise ratio improvement can be achieved using the test data of only one measurement test; the effect of this method on improving the signal-to-noise ratio of measurement data is only related to the sampling rate of the data, and has nothing to do with the repeatability of the test and the reproducibility of the test results.

[0050] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for improving the signal-to-noise ratio of underwater weak radiation noise measurement, characterized in that: The method comprises the following steps: a group of derivative time domain data with reduced sampling rate but completely identical signal-to-noise ratio and target signal characteristics are produced by performing arithmetic difference splitting on the collected original time domain measurement data; and the derivative time domain data after splitting are processed by utilizing the time irrelevance of noise to obtain time domain data with improved signal-to-noise ratio.

2. The method for improving the signal-to-noise ratio of underwater weak radiation noise measurement according to claim 1, characterized in that: The specific steps are as follows: Step 1: The time domain function expression of the sound wave excited by the sound source is S(t). When the sound wave reaches the sensor after t0, the propagation loss is 1-α, and the sensor sampling rate is f s , then the discrete signal collected by the sensor from time t = t0 is R[n], and R[n] is the original time domain data; R[n]= α·S[n]+N[n] (1) Where N[n] is the random noise signal measured by the sensor, and n is the element number of the data array collected by the acquisition card; if R[m] is the mth element in the data array collected by the sensor from time t=t0, then m=t0*f s ; Step 2: When the sampling rate of the acquisition card is f s Much larger than S f [n] When the frequency component has the maximum frequency, S f [n] is the FFT array of S[n]. Splitting R[n] into x arrays according to formula (3) is equivalent to measuring the same target x times under almost the same test environment. The sampling rate corresponding to each measurement is f s / x; R (x) [n'] = R[(n+1)·x] (3) In the formula, R (x) [n'] is the xth array obtained after splitting with R[n] as the data source; Step 3: Perform FFT transformation on the split array, add all the split signals and average them, and you will get: In the formula, S f [n] is the FFT array of S[n], is the FFT array of the xth array obtained after splitting S[n] as the data source; N f [n] is the FFT array of N[n], It is the FFT array of the xth array obtained after splitting with N[n] as the data source. is the result array after signal-to-noise ratio enhancement.

3. The method for improving the signal-to-noise ratio of underwater weak radiation noise measurement according to claim 2, characterized in that: The specific steps are as follows: If time domain signals are required, By performing iFFT transformation, new time domain data with a higher signal-to-noise ratio than R[n] can be obtained.

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