Low-noise target detection and direction estimation method and storage medium

Abstract

The application discloses a low-noise target detection and azimuth estimation method and a storage medium. Firstly, a low-frequency part is divided into a plurality of subbands, high-resolution beam forming is respectively performed on the subbands, and then the beam forming results are counted. If the number of the beam forming results in a peak value gathering angle range reaches a threshold, it is determined that there is a target, and the detection of the low-noise target is realized. The beam forming results in the peak value gathering angle range are weighted and summed, and the peak position of the processing result is the target azimuth, so that the azimuth estimation of the low-noise target is realized. Compared with the traditional low-noise target detection method through a line spectrum, the method does not need to detect the line spectrum, and realizes the detection and azimuth estimation of the low-noise target without a stable line spectrum or with an unobvious line spectrum.

Description

Technical Field

[0001] This invention relates to the field of underwater target detection technology, specifically to a low-noise target detection, orientation estimation method, and storage medium. Background Technology

[0002] Low-noise target detection has broad application prospects and has always been a challenging problem in the field of passive sonar target detection. Low-noise targets tend to concentrate a large amount of energy in the low-frequency range, as does environmental noise energy on the sea / lake, thus increasing the difficulty of detecting them. Currently, beamforming is generally used to detect low-frequency stable line spectra to achieve low-noise target detection. However, many low-noise targets exhibit characteristics such as the absence of stable line spectra or indistinct line spectra, thus this method has certain limitations in low-noise target detection. Summary of the Invention

[0003] To address the aforementioned problems, the inventors have provided a method for target detection and azimuth estimation without the need to detect line spectra, enabling the detection and azimuth estimation of low-noise targets with unstable or indistinct line spectra.

[0004] According to a first aspect, the present invention provides a low-noise target orientation estimation method, comprising:

[0005] Step S1: Receive data from multiple hydrophone array elements and perform a fast Fourier transform.

[0006] Step S2: Determine the frequency band of interest based on the data after Fast Fourier Transform, and divide the frequency band of interest into several sub-bands at equal intervals;

[0007] Step S3: Perform MUSIC algorithm processing on each sub-band to obtain the MUSIC results for each sub-band;

[0008] Step S4: Select a fixed angle range Δθ, and with Δθ / 2 as the step size, perform maximum value statistics on the MUSIC results of each sub-band, and determine whether there is a target within the angle range with the largest number of maximum values.

[0009] Step S5: If there is a target within the angle range with the most maximum values, sum, normalize, and logarithmize the MUSIC results of each sub-band within that angle range to obtain the target's maximum value. The angle corresponding to the target's maximum value is the azimuth estimation result of that target.

[0010] Furthermore, in step S2, the divided sub-bands satisfy the narrowband condition:

[0011] B / f0 < 0.1

[0012] Where B is the subband bandwidth and f0 is the subband center frequency.

[0013] Furthermore, in step S4, the condition for determining whether there is a target within the angle range with the largest number of statistically significant values ​​is:

[0014] If the number of maximum values ​​ / the total number of sub-bands is greater than the threshold, then there is a target within the angle range with the highest number of maximum values.

[0015] Further, in step S3, the step of performing MUSIC algorithm processing on each sub-band includes:

[0016] Step S31: Calculate the covariance matrix R of the sub-band spectral data x ;

[0017]

[0018] Among them, X i Let n be the spectral data within the i-th sub-band, and n be the number of array elements.

[0019] Step S32: For R x Perform eigenvalue decomposition and determine the number of signal sources based on the eigenvalues;

[0020] Step S33: Convert the covariance matrix R x Arranging the eigenvalues ​​by size, we get λ1≥λ2≥…λ P ≥λ P+1 ≥λ P+2 ≥…≥λ n The first P eigenvalues ​​correspond to the signal, and their eigenvectors form the signal subspace U. s = [U1, U2, ..., U P The last MP eigenvalues ​​correspond to noise, and their corresponding eigenvectors form the noise subspace U. n =[U P+1 U P+2 , ..., U n ].

[0021] Step S34: Based on the signal subspace U s and noise subspace U n Find the MUSIC result.

[0022] Furthermore, the MUSIC result P MUSIC for:

[0023]

[0024] Where A(θ) is the array direction vector; A H Let A(θ) be the conjugate transpose of matrix A; A(θ) is represented as:

[0025] A(θ)=[a(θ1), a(θ2),…, a(θk ), …, a(θ) K )]

[0026]

[0027] f is the center frequency of the sub-band, d is the element spacing, and θ is the center frequency of the sub-band. k The k-th angle is used for angle scanning, where c is the speed of sound and n is the number of array elements.

[0028] According to a second aspect, the present invention also provides a computer-readable storage medium having a computer program stored thereon, the computer program being executable by a processor to implement the steps of the method described above.

[0029] Compared with existing technologies, the working principle and beneficial effects of this invention are as follows:

[0030] This invention divides the low-frequency region into several sub-bands, performs high-resolution beamforming on each sub-band, and then statistically analyzes the beamforming results. If the number of beamforming results within a peak focusing angle range reaches a threshold, a target is identified, thus achieving the detection of low-noise targets. The beamforming results within the peak focusing angle range are weighted and summed; the peak position of the result is then used to determine the target's azimuth, enabling azimuth estimation of low-noise targets. Compared to traditional methods that detect low-noise targets using line spectra, this invention eliminates the need for line spectrum detection, achieving the detection and azimuth estimation of low-noise targets with unstable or indistinct line spectra. Attached Figure Description

[0031] Figure 1 This is a flowchart of the low-noise target detection and orientation estimation method of the present invention;

[0032] Figure 2 The following are the MUSIC results for each subband in Example 1;

[0033] Figure 3 This is a diagram showing the target orientation estimation results in Example 1. Detailed Implementation

[0034] The present invention will now be described in further detail with reference to specific embodiments and accompanying drawings.

[0035] Example 1

[0036] like Figure 1As shown, this invention provides a method for low-noise target detection and azimuth estimation. First, the low-frequency region is divided into several sub-bands, and high-resolution beamforming is performed on each sub-band. Then, the beamforming results are statistically analyzed. If the number of beamforming results within the peak focusing angle range reaches a threshold, a target is identified, thus achieving low-noise target detection. The beamforming results within the peak focusing angle range are then weighted and summed; the peak position of the sum is the target's azimuth, thus achieving azimuth estimation of the low-noise target.

[0037] Specifically, in this embodiment, taking actual ship low-speed navigation radiated noise data as an example, the hydrophone array element spacing is 15cm, the number of array elements is 120, the sound speed is calculated as 1500m / s, and the sampling rate is 50kHz.

[0038] The specific process is as follows:

[0039] Step S1: Receive data from the 120 hydrophone array elements through 120 channels and perform a fast Fourier transform.

[0040] Step S2: Based on the data after Fast Fourier Transform, the frequency band of interest is determined to be 500Hz to 1kHz. This is then divided into 50 subbands at 10Hz intervals, each satisfying the narrowband condition.

[0041] B / f0 < 0.1

[0042] Where B is the subband bandwidth and f0 is the subband center frequency.

[0043] Step S3: Process each sub-band using the MUSIC algorithm. The MUSIC algorithm scans at angles ranging from -30° to 30°, with an angle interval of 0.1°. The MUSIC algorithm is as follows:

[0044] Calculate the covariance matrix R of the subband spectral data x .

[0045]

[0046] Among them, X i Let n be the spectral data within the i-th sub-band, and n be the number of array elements.

[0047] For R x Perform eigenvalue decomposition and determine the number of signal sources based on the eigenvalues.

[0048] The covariance matrix R x Arranging the eigenvalues ​​by size, we get λ1≥λ2≥…λ P ≥λ P+1 ≥λ P+2 ≥…≥λ nThe first P eigenvalues ​​correspond to the signal, and their eigenvectors form the signal subspace U. s = [U1, U2, ..., U P The last MP eigenvalues ​​correspond to noise, and their corresponding eigenvectors form the noise subspace U. n =[U P+1 U P+2 , ..., U n ].

[0049] Based on signal subspace U s and noise subspace U n Find the MUSIC result P MUSIC .

[0050]

[0051] Where A(θ) is the array direction vector; A H Let A(θ) be the conjugate transpose of matrix A; A(θ) is represented as:

[0052] A(θ)=[a(θ1), a(θ2),…, a(θ k ), …, a(θ) K )]

[0053]

[0054] f is the center frequency of the sub-band, d is the element spacing, and θ is the center frequency of the sub-band. k The k-th angle is used for angle scanning, where c is the speed of sound and n is the number of array elements.

[0055] The MUSIC results for each subband are as follows Figure 2 As shown.

[0056] Step S4: Select a fixed angle range Δθ of 3°, and use Δθ / 2 = 1.5° as the step size to count the maximum values ​​of the MUSIC results for each sub-band. The statistics show that the angle range with the most maximum values ​​is 25.5° to 28.5°, with a total of 22 values. At this time, the number of maximum values ​​ / the total number of sub-bands = 22 / 50 = 44%, which exceeds the threshold of 40%. Therefore, there is a target within this angle range, and the target detection is achieved.

[0057] Step S5: Summate, normalize, and logarithmize the MUSIC results for each sub-band within this angle range. The processing results are as follows: Figure 3 As shown, the target's azimuth estimate is therefore 27.1°.

[0058] The above examples illustrate the present invention only to aid in understanding it and are not intended to limit the scope of the invention. Those skilled in the art can make various simple deductions, modifications, or substitutions based on the principles of this invention.

Claims

1. A low-noise target detection and azimuth estimation method, characterized in that, include: Step S1: Receive data from multiple hydrophone array elements and perform Fourier transform; Step S2: Determine the frequency band of interest based on the Fourier transform data, and divide the frequency band of interest into several sub-bands at equal intervals; Step S3: Perform MUSIC algorithm processing on each sub-band to obtain the MUSIC results for each sub-band; Step S4: Select a fixed angle range ,by Using the step size, the maximum value of the MUSIC results for each sub-band is counted, and it is determined whether there is a target within the angle range with the largest number of counted maximum values. The condition for determining whether there is a target within the angle range with the largest number of counted maximum values ​​is: if the number of counted maximum values ​​ / the total number of sub-bands is greater than a threshold, then there is a target within the angle range with the largest number of counted maximum values. Step S5: If there is a target within the angle range with the most maximum values, sum, normalize, and logarithmize the MUSIC results of each sub-band within that angle range to obtain the target's maximum value. The angle corresponding to the target's maximum value is the azimuth estimation result of that target.

2. The method as described in claim 1, characterized in that, In step S2, the divided sub-bands satisfy the narrowband condition: ； in, For subband bandwidth, This is the center frequency of the sub-band.

3. The method as described in claim 1, characterized in that, In step S3, the step of performing MUSIC algorithm processing on each sub-band includes: Step S31: Calculate the covariance matrix of the sub-band spectral data ; Step S32: For Perform eigenvalue decomposition and determine the number of signal sources based on the eigenvalues; Step S33: Convert the covariance matrix Arrange the eigenvalues ​​by size to find the signal subspace. and noise subspace ; Step S34: Based on signal subspace and noise subspace Find the MUSIC result.

4. The method as described in claim 3, characterized in that, MUSIC Results for: ； in, The array direction vector; Let be the conjugate transpose of matrix A.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that, The computer program can be executed by a processor to implement the steps of the method as described in any one of claims 1-4.

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