CN113343914A - Radiation noise line spectrum frequency domain self-adaptive enhancement method - Google Patents
Radiation noise line spectrum frequency domain self-adaptive enhancement method Download PDFInfo
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Abstract
The invention discloses a radiation noise line spectrum frequency domain self-adaptive enhancement method, which comprises the following steps: 1. reading in underwater acoustic target radiation noise data, and initializing parameters and iteration time n of an adaptive linear spectrum enhancer (ALE); 2. starting iteration, and determining the input of the ALE at the n moment according to the delay and the order of the ALE; 3. converting the input into a frequency domain, calculating the output of the ALE at the n moment according to the weight of the ALE, and obtaining the error with the original data; 4. through error feedback, updating the weight of the ALE at the next moment in the frequency domain by using the self-adaptive algorithm; (5) entering the next moment n is n +1, if n is smaller than the length of the radiation noise data, returning to the step (2) to continue iteration; otherwise, the line spectrum enhancement process is finished, and the output of the ALE is obtained and can be further used for line spectrum detection and extraction.
Description
Technical Field
The invention belongs to the technical field of extraction and identification of characteristics of underwater acoustic target radiation noise signals, and particularly relates to a radiation noise line spectrum frequency domain self-adaptive enhancement method.
Background
The underwater sound target identification is a large core problem in ocean science research, and refers to analyzing underwater sound signals received by sonar and extracting target characteristics to distinguish target types and kinds of information. Submarine, naval vessel and other underwater acoustic targets inevitably produce radiation noise in the navigation, therefore the radiation noise of underwater acoustic target is the main information source of passive sonar work at present, and the characteristics of radiation noise are the main basis of passive sonar target identification. The power spectrum of the radiated noise is composed of a continuous spectrum and narrow-band components (line spectrum) at discrete frequencies. The line spectrum component is usually generated by mechanical vibration of the ship, propeller operation and the like, and can embody main characteristics of the target, such as ship type, rotating speed, blade number and the like. The radiation noise of underwater acoustic targets such as submarines, commercial ships, torpedoes and the like contains abundant line spectrum components, particularly low-frequency line spectrum therein, and has the characteristics of long propagation distance and relatively high energy. Therefore, the line spectrum feature is widely applied to passive sonar detection and identification, and is an important research content in the field of underwater sound.
However, due to the complex and variable underwater environment and the increasing concealment of the ship, the line spectrum components are usually submerged in strong background noise, and the generally used conventional power spectrum analysis method often has misjudgment and missed judgment on the line spectrum of the radiation noise, thereby having serious influence on the subsequent analysis result. Therefore, accurate line spectrum extraction under the conditions of complex background and low signal to noise ratio becomes a key link and is also a big problem in the underwater sound technology.
The self-adaptive line spectrum enhancement is a self-adaptive filtering method, and can be used for enhancing line spectrum components in radiation noise and inhibiting background noise. The self-adaption method has the characteristic that parameters can be self-adjusted along with the change of external input under the condition of no need of prior conditions, and the optimal filtering result is always kept. At present, the most classical algorithm is the Least Mean Square (LMS) -based algorithm which is most applied, but the LMS algorithm has obvious performance reduction under the conditions of low signal-to-noise ratio and non-white background, and great research significance is provided for further improving the performance of the adaptive line spectrum enhancement algorithm.
Disclosure of Invention
The invention aims to provide a radiation noise line spectrum frequency domain self-adaptive enhancement method to solve the technical problem of radiation noise line spectrum feature extraction.
In order to solve the technical problems, the specific technical scheme of the invention is as follows:
a radiation noise line spectrum frequency domain self-adaptive enhancement method comprises the following steps:
step 1, reading in underwater acoustic target radiation noise data, and initializing parameters of an adaptive line spectrum enhancer and an iteration moment n;
step 2, starting iteration, and determining the input of the adaptive line spectrum enhancer at the n moment according to the time delay and the order of the adaptive line spectrum enhancer;
step 3, converting the input into a frequency domain, calculating the output of the adaptive line spectrum enhancer at the n moment according to the weight of the adaptive line spectrum enhancer, and obtaining the error with the original data;
step 4, updating the weight of the adaptive line spectrum enhancer at the next moment in a frequency domain by using an adaptive algorithm through error feedback;
step 5, entering the next moment n is n +1, and if n is smaller than the total length of the radiation noise, returning to the step 2 to continue iteration; if n is larger than or equal to the total length of the radiation noise, the line spectrum enhancement process is finished, and the output of the self-adaptive line spectrum enhancer is obtained.
Further, step 1 comprises the following steps:
step 1.1, reading target radiation noise data to obtain a sequence x (K) with the length of K, wherein K is 0,1, … and K-1;
step 1.2, setting the order L and the time delay delta of the adaptive line spectrum enhancer;
step 1.3, setting parameters mu, alpha, rho and beta of the adaptive algorithm;
step 1.4, initializing a frequency domain weight vector of the adaptive line spectrum enhancer; initializing an adaptive line spectrum enhancer; the output sequence y (K) 0, K0, 1, …, K-1;
and step 1.5, initializing iteration time n as L-1.
Further, the step 2 specifically comprises the following steps: delaying x (k) by delta points to obtain a delayed radiation noise sequence x (k-delta), wherein k-delta represents the radiation noise sequenceSubscripts of the columns, K- Δ ═ - Δ, - Δ +1, …, - Δ + K-1, when K- Δ<When 0, let x (k- Δ) be 0. Starting iteration, and taking the first L points at the time n as the input of the adaptive line spectrum enhancer for the delayed radiation noise sequence, namely the input vector of the adaptive line spectrum enhancer is x (n-delta) ═ x (n-delta), x (n-delta-1),.. once, x (n-delta-L +1)]T。
Further, step 3 specifically includes the following steps:
step 3.1, transforming the input vector to a frequency domain:
wherein is a normalized discrete fourier transform matrix:
where e is the natural index and j is the unit of imaginary number.
Step 3.2, calculating the output y (n) and the error e (n) of the adaptive line spectrum enhancer at the time n:
e(n)＝x(n)-y(n)
wherein (·)HRepresenting a conjugate transpose operator.
Further, step 4 specifically includes the following steps: the weights for the next time instant of the adaptive line spectrum enhancer are updated in the frequency domain using the following adaptive algorithm:
wherein the content of the first and second substances,
a frequency domain weight vector representing the ALE at time n,
for variance estimation of error e (n):
where | · | represents taking the vector element-wise modulo, sgn (·) represents taking the vector element-wise signed, i.e.:
the radiation noise line spectrum frequency domain self-adaptive enhancement method has the following advantages: the method combines the adaptive filtering technology with the radiation noise frequency domain characteristic analysis, fully utilizes the sparsity of ideal frequency response of an adaptive line spectrum enhancer (ALE) and an adaptive algorithm based on high-order cumulant, updates the weight vector of the ALE in the frequency domain, does not need prior information of line spectrum frequency and marine environment parameters, and is simple to implement, clear in physical significance and obvious in line spectrum enhancement effect.
Drawings
FIG. 1 is a schematic flow chart of a method for adaptively enhancing a line spectrum frequency domain of radiation noise according to the present invention;
FIG. 2 is a schematic structural diagram of an adaptive line spectrum enhancer in the radiation noise line spectrum frequency domain adaptive enhancement method according to the present invention;
FIG. 3(a) is a schematic diagram of an input waveform in embodiment 1 of the present invention;
FIG. 3(b) is a schematic diagram of an output waveform in embodiment 1 of the present invention;
FIG. 4(a) is an input power spectrum in example 1 of the present invention;
FIG. 4(b) is an output power spectrum in example 1 of the present invention;
FIG. 5(a) is a schematic diagram of an input waveform in example 2;
FIG. 5(b) is a schematic diagram showing an output waveform in example 2;
FIG. 6(a) is an input power spectrum in example 2;
FIG. 6(b) is an output power spectrum in example 2;
Detailed Description
In order to better understand the purpose, structure and function of the present invention, the following describes a radiation noise line spectrum frequency domain adaptive enhancement method in detail with reference to the accompanying drawings. Wherein FIG. 1 is a flow chart of the method of the present invention; FIG. 2 is the structure of ALE in the present invention, wherein z-ΔThe delay of delta point is shown, the FFT is the fast Fourier transform of the sequence, and sigma is the summation sign.
The invention carries out self-adaptive enhancement on the radiation noise line spectrum, uses a self-adaptive algorithm in a frequency domain, and obtains the radiation noise signal after line spectrum enhancement and noise suppression through the iterative computation of a self-adaptive line spectrum enhancer (ALE).
Example 1:
a radiation noise line spectrum frequency domain adaptive enhancement method, as shown in fig. 1 and fig. 2, includes the following steps:
the step 1 specifically comprises the following steps:
(1.1) reading in target radiation noise data 1 measured at sea, wherein the data are known to contain three line spectrums of 40 Hz, 116 Hz and 136Hz in a lower frequency band (within 200 Hz). The sampling rate is 2000Hz, the time length is 100s, the total point number K is 200000, and the sequence x (K) is obtained, K is 0,1, … and K-1;
(1.2) the order L of ALE may be 1000-4000, where L is 2000; the delay delta can be 50-200, where delta is 100.
(1.3) setting parameters μ ═ 5e-5, α ═ 0.9999, ρ ═ 5e-10, and β ═ 5 of the adaptive algorithm. The above parameters are related to the input signal power and can be adjusted according to actual conditions.
(1.4) initializing frequency domain weight vectors of ALEs
The ALE output sequence y is initialized (K) 0, K0, 1, …, K-1.
(1.5) initializing iteration time n ═ L-1.
The step 2 specifically comprises the following steps: delaying x (K) by delta points to obtain a delayed radiation noise sequence x (K-delta), wherein K-delta represents subscript of the radiation noise sequence, K-delta, -delta +1, …, -delta + K-1, and when K-delta<When 0, let x (k- Δ) be 0. Starting iteration, and taking the first L points at the time n as the input of the adaptive line spectrum enhancer for the delayed radiation noise sequence, namely taking the input vector of the ALE as x (n-delta) ═ x (n-delta), x (n-delta-1),.. once, x (n-delta-L +1)]T。
The step 3 specifically comprises the following steps:
(3.1) transforming the input vector to the frequency domain:
wherein is a normalized discrete fourier transform matrix:
where e is the natural index and j is the unit of imaginary number.
(3.2) calculating the output y (n) and the error e (n) of the ALE at the current time n:
e(n)＝x(n)-y(n) (4)
wherein (·)HRepresenting a conjugate transpose operator.
The step 4 specifically comprises the following steps:
updating the weights of the ALE at the next moment in the frequency domain using the following adaptive algorithm:
wherein the content of the first and second substances,
a frequency domain weight vector representing the ALE at time n,
for variance estimation of error e (n):
where | · | represents taking the vector element-wise modulo, sgn (·) represents taking the vector element-wise signed, i.e.:
the step 5 specifically comprises the following steps:
(5.1) entering the next moment n ═ n + 1;
(5.2) if n is less than the total length K of the radiation noise, returning to the step (2) to continue iteration;
and (5.3) otherwise, the iteration of all data is finished, and the line spectrum enhancement process is finished. The output y (K) of ALE, K0, 1, …, K-1, constitutes the line spectrum enhanced signal.
Fig. 3(a) is an input waveform diagram of the ALE in embodiment 1, and fig. 3(b) is an output waveform diagram of the ALE in embodiment 1.
Fig. 4(a) is a graph comparing the power spectrum of the ALE input in example 1, and fig. 4(b) is a graph comparing the power spectrum of the ALE output in example 1. The used analysis method is to take 10s data and use a periodogram method to estimate the power spectrum and observe the line spectrum enhancement effect. Shown here are the power spectrum estimates for the 60 th to 70 th data. It is obvious from the figure that the method of the invention has good noise suppression capability and obvious line spectrum enhancement effect.
Example 2:
a radiation noise line spectrum frequency domain adaptive enhancement method, as shown in fig. 1 and fig. 2, includes the following steps:
the step 1 specifically comprises the following steps:
(1.1) introducing target radiation noise data 2 measured at sea, wherein the data are known to comprise 89, 128 and 139Hz three line spectrums in a lower frequency band (within 200 Hz). The sampling rate is 2000Hz, the time length is 100s, the total point number K is 200000, and the sequence x (K) is obtained, K is 0,1, … and K-1;
(1.2) the order L of ALE may be 1000-4000, where L is 2000; the delay delta can be 50-200, where delta is 100.
(1.3) setting parameters μ ═ 5e-5, α ═ 0.9999, ρ ═ 5e-10, and β ═ 5 of the adaptive algorithm. The above parameters are related to the input signal power and can be adjusted according to actual conditions.
(1.4) initializing frequency domain weight vectors of ALEs
The ALE output sequence y is initialized (K) 0, K0, 1, …, K-1.
(1.5) initializing iteration time n ═ L-1.
The step 2 specifically comprises the following steps: delaying x (K) by delta points to obtain a delayed radiation noise sequence x (K-delta), wherein K-delta represents subscript of the radiation noise sequence, K-delta, -delta +1, …, -delta + K-1, and when K-delta<When 0, let x (k- Δ) be 0. Start ofIteration, for the delayed radiation noise sequence, the first L points at the time n are taken as the input of the adaptive line spectrum enhancer, namely the input vector of the ALE is x (n-delta) ═ x (n-delta), x (n-delta-1),.. once, x (n-delta-L +1)]T。
The step 3 specifically comprises the following steps:
(3.1) transforming the input vector to the frequency domain:
wherein is a normalized discrete fourier transform matrix:
where e is the natural index and j is the unit of imaginary number.
(3.2) calculating the output y (n) and the error e (n) of the ALE at the current time n:
e(n)＝x(n)-y(n) (12)
wherein (·)HRepresenting a conjugate transpose operator.
The step 4 specifically comprises the following steps:
updating the weights of the ALE at the next moment in the frequency domain using the following adaptive algorithm:
wherein the content of the first and second substances,
a frequency domain weight vector representing the ALE at time n,
for variance estimation of error e (n):
where | · | represents taking the vector element-wise modulo, sgn (·) represents taking the vector element-wise signed, i.e.:
the step 5 specifically comprises the following steps:
(5.1) entering the next moment n ═ n + 1;
(5.2) if n is less than the total length K of the radiation noise, returning to the step (2) to continue iteration;
and (5.3) otherwise, the iteration of all data is finished, and the line spectrum enhancement process is finished. The output y (K) of ALE, K0, 1, …, K-1, constitutes the line spectrum enhanced signal.
Fig. 5(a) is an input waveform diagram of the ALE in embodiment 2, and fig. 5(b) is an output waveform diagram of the ALE in embodiment 2.
Fig. 6(a) is a graph comparing the power spectrum of the ALE input in example 2, and fig. 6(b) is a graph comparing the power spectrum of the ALE output in example 2. The used analysis method is to take 10s data and use a periodogram method to estimate the power spectrum and observe the line spectrum enhancement effect. Shown here are the power spectrum estimates for the 60 th to 70 th data. It is obvious from the figure that the method of the invention has good noise suppression capability and obvious line spectrum enhancement effect.
The embodiment shows that the method can effectively enhance the low-frequency line spectrum of the underwater sound target radiation noise, has obvious effect of inhibiting background noise, and is beneficial to the subsequent line spectrum detection and extraction work. It is to be understood that the present invention has been described with reference to certain embodiments, and that various changes in the features and embodiments, or equivalent substitutions may be made therein by those skilled in the art without departing from the spirit and scope of the invention. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the invention without departing from the essential scope thereof. Therefore, it is intended that the invention not be limited to the particular embodiment disclosed, but that the invention will include all embodiments falling within the scope of the appended claims.
Claims (5)
1. A radiation noise line spectrum frequency domain self-adaptive enhancement method is characterized by comprising the following steps:
step 1, reading in underwater acoustic target radiation noise data, and initializing parameters of an adaptive line spectrum enhancer and an iteration moment n;
step 2, starting iteration, and determining the input of the adaptive line spectrum enhancer at the n moment according to the time delay and the order of the adaptive line spectrum enhancer;
step 3, converting the input into a frequency domain, calculating the output of the adaptive line spectrum enhancer at the n moment according to the weight of the adaptive line spectrum enhancer, and obtaining the error with the original data;
step 4, updating the weight of the adaptive line spectrum enhancer at the next moment in a frequency domain by using an adaptive algorithm through error feedback;
step 5, entering the next moment n is n +1, and if n is smaller than the total length of the radiation noise, returning to the step 2 to continue iteration; if n is larger than or equal to the total length of the radiation noise, the line spectrum enhancement process is finished, and the output of the self-adaptive line spectrum enhancer is obtained.
2. The radiation noise line spectrum frequency domain adaptive enhancement method according to claim 1, wherein the step 1 comprises the following steps:
step 1.1, reading target radiation noise data to obtain a sequence x (K) with the length of K, wherein K is 0,1, … and K-1;
step 1.2, setting the order L and the time delay delta of the adaptive line spectrum enhancer;
step 1.3, setting parameters mu, alpha, rho and beta of the adaptive algorithm;
step 1.4, initializing a frequency domain weight vector of the adaptive line spectrum enhancer; initializing an adaptive line spectrum enhancer; the output sequence y (K) 0, K0, 1, …, K-1;
and step 1.5, initializing iteration time n as L-1.
3. The line spectral frequency domain adaptive enhancement method for radiation noise according to claim 1, wherein the step 2 specifically comprises the following steps: delaying x (K) by delta points to obtain a delayed radiation noise sequence x (K-delta), wherein K-delta represents subscript of the radiation noise sequence, K-delta, -delta +1, …, -delta + K-1, and when K-delta<When 0, let x (k- Δ) be 0; starting iteration, and taking the first L points at the time n as the input of the adaptive line spectrum enhancer for the delayed radiation noise sequence, namely the input vector of the adaptive line spectrum enhancer is x (n-delta) ═ x (n-delta), x (n-delta-1),.. once, x (n-delta-L +1)]T。
4. The line spectral frequency domain adaptive enhancement method for radiation noise according to claim 1, wherein the step 3 specifically comprises the following steps:
step 3.1, transforming the input vector to a frequency domain:
wherein
For a normalized discrete fourier transform matrix:
wherein e is a natural index and j is an imaginary unit;
step 3.2, calculating the output y (n) and the error e (n) of the adaptive line spectrum enhancer at the time n:
e(n)＝x(n)-y(n)
wherein (·)HRepresenting a conjugate transpose operator.
5. The line spectral frequency domain adaptive enhancement method for radiation noise according to claim 1, wherein the step 4 specifically comprises the following steps: the weights for the next time instant of the adaptive line spectrum enhancer are updated in the frequency domain using the following adaptive algorithm:
wherein the content of the first and second substances,
a frequency domain weight vector representing the ALE at time n,
for variance estimation of error e (n):
where | · | represents taking the vector element-wise modulo, sgn (·) represents taking the vector element-wise signed, i.e.:
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| CN112462352A (en) * | 2020-10-30 | 2021-03-09 | 哈尔滨工程大学 | Line spectrum enhancement method suitable for low signal-to-noise ratio condition |
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| CN111582026A (en) * | 2020-03-31 | 2020-08-25 | 中国科学院声学研究所 | Sparse drive ALE-based underwater target detection method and system of support vector machine |
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