Normalization method for active sonar imaging data

Abstract

The invention relates to the technical field of underwater acoustic information processing, and provides an active sonar imaging data normalization method, which comprises the following steps: S1, construction of an azimuth distance matrix: acquiring azimuth-distance data through a sonar system, and storing the azimuth-distance data in a polar coordinate system; s2, data smoothing: after the azimuth distance matrix is constructed, smoothing processing is performed on each row of data so as to reduce the influence of noise; s3, expected echo intensity is calculated, wherein after smoothing processing is completed, the expected echo intensity of each detection point is calculated through a data driving method; performing normalization processing on each row of data according to the corresponding expected echo intensity; and S4, coordinate system conversion: after normalization processing, converting the processed azimuth distance matrix data from a polar coordinate system to a rectangular coordinate system. According to the invention, interference of background reverberation and noise on long-distance small target detection can be reduced.

Description

Technical Field

[0001] The invention relates to the technical field of underwater acoustic information processing, and in particular to a normalization method for active sonar imaging data. Background Art

[0002] In the coastal defense system, it is crucial to ensure the safety of ports and important waters. In recent years, with the continuous evolution of underwater threats, detection tasks in shallow water environments have become increasingly challenging. Typical threat targets include underwater intrusion devices such as small submersibles and frogmen. These targets are highly concealed and highly maneuverable, posing a serious threat to ports, ships and important underwater facilities. Active sonar is the main means of detecting these underwater targets. Using the principle of sound wave propagation, the sonar system determines the location and characteristics of the target by emitting sound waves and receiving echoes. However, in practical applications, sonar detection often faces complex background noise and reverberation problems. Reverberation refers to the reflection and scattering of active sonar transmission signals by various scatterers in the marine environment. These scattered signals are superimposed on the echo, forming a strong background noise, which seriously interferes with the identification of target echoes. In shallow water environments, the strong scattering effect of the seabed is the main source of reverberation, especially in areas where the seabed is irregular or densely covered with various sediments. The seabed reverberation is highly similar to the target echo signal, making the target detection task more complicated. In addition, the echo intensity usually changes significantly with the distance of the target from the sonar system. Generally, the closer the target is to the sonar system, the stronger its echo intensity is; however, the intensity of reverberation also has a similar decreasing trend with distance, which makes it difficult to distinguish between the target and the reverberation. Directly using the original echo intensity to generate sonar images will result in an inability to accurately reflect the impact of reverberation, and it will also make it difficult for subsequent target detection algorithms to set appropriate detection thresholds. Therefore, how to reduce the impact of the irregular distribution of reverberation with distance and angle on imaging quality and detection results through effective normalization methods is an urgent problem to be solved in the field of sonar detection. Summary of the invention

[0003] The present invention provides a normalization method for active sonar imaging data, which can reduce the interference of background reverberation and noise on the detection of long-distance small targets.

[0004] A normalization method for active sonar imaging data according to the present invention comprises the following steps:

[0005] S1. Construction of azimuth-distance matrix: Obtain azimuth-distance data through the sonar system and store it in a polar coordinate system;

[0006] S2, data smoothing: after constructing the azimuth distance matrix, each row of data is smoothed to reduce the impact of noise;

[0007] S3, calculating the expected echo intensity: after completing the smoothing process, the expected echo intensity of each detection point is calculated by a data-driven method; each row of data is normalized according to the corresponding expected echo intensity;

[0008] S4, coordinate system conversion: After normalization, the processed azimuth distance matrix data is converted from the polar coordinate system to the rectangular coordinate system.

[0009] Preferably, in S1, the rows of the azimuth distance matrix represent different distance dimensions, the columns represent different azimuth angles, each element (i, j) represents the echo intensity at the i-th distance and the j-th angle, and the echo intensity is obtained by beamforming and matched filtering. The azimuth distance matrix is ​​the basis for normalization processing.

[0010] Preferably, in S2, the smoothing method includes a weighted average method of K adjacent rows, and the weights are a rectangular window, a Hann window or a Hamming window.

[0011] Preferably, in S2, specifically:

[0012] Smoothing is performed by distance to reduce the noise of distance sonar data; for each row of the azimuth distance matrix M, K adjacent rows are selected for weighted averaging. The matrix after row smoothing is recorded as M'. The calculation process is expressed as:

[0013]

[0014] in[·] i,: represents the i-th row of the matrix, [·] j,: represents the jth row of the matrix, a j Represents a weighted value.

[0015] As a preferred embodiment, in S3, if only the distance factor is considered, specifically:

[0016] A) Calculate the expected echo intensity at the same distance; calculate the average value of each row of M' to obtain the expected echo intensity at the distance The calculation process is expressed as:

[0017]

[0018] Where Mean means calculating the average value of the elements in the vector;

[0019] B) Normalize the data using the expected echo intensity at each distance, and the normalized matrix is: It is calculated as:

[0020]

[0021] Where δ is a small positive number to avoid the denominator being 0 during normalization;

[0022] If distance and orientation factors are taken into consideration, the details are as follows:

[0023] a) Divide the matrix M' into N blocks by column, each block has L columns, and calculate the expected echo intensity at the same distance in each block; calculate the average value of each row of M' by block to obtain the expected echo intensity in the angle region under the distance The calculation process is expressed as:

[0024]

[0025] in[·] i,j-(L-1) / 2:j+(L-1) / 2 Represents the elements from j-(L-1) / 2 to j+(L-1) / 2 in the i-th row of the matrix;

[0026] b) Normalize the data using the expected echo intensity at each distance, and the normalized matrix is: It is calculated as:

[0027]

[0028] Preferably, in the process of dividing the sonar imaging matrix into non-overlapping blocks, if the block edges are discontinuous, the following method is used for calculation:

[0029] 1) Construct the circulant matrix M':

[0030] M"=[[M'] :,Q-S+1:Q ,M',[M'] :,1:S ]

[0031] in[·] :,Q-S+1:Q represents the elements of all rows from the Q-S+1th column to the Qth column of the matrix, where S is a positive integer not less than 1 / 2 of the filter length; [·] :,1:S Represents the elements of all rows from the 1st column to the Sth column of the matrix;

[0032] 2) Use an FIR filter of length 2S to filter each row of M”;

[0033] 3) Take the middle Q columns of the M” matrix and record them as matrix N, that is:

[0034] N=[M] :,S+1:S+Q

[0035] [·] :,S+1:S+Q Represents the elements of all rows from the S+1th column to the S+Qth column of the matrix. This step is used to remove the edge effect when filtering the matrix row by row;

[0036] 4) Use matrix N to normalize matrix M', that is:

[0037]

[0038] where ⊙ denotes element-wise division of matrices.

[0039] The beneficial effects of the present invention are as follows:

[0040] The present invention uses normalization processing to compare echo data at different distances and angles under the same benchmark, thereby reducing the irregular distribution effect of reverberation in the image and improving the detectability of the target, especially in complex shallow sea environments. The present invention can simplify the parameter setting in subsequent target detection tasks, reduce the complexity of manual debugging, and improve the overall detection performance of the sonar system.

[0041] The present invention further refines the normalization process according to the reverberation differences in different angle areas, and uses block normalization, edge filtering and other technologies to eliminate the discontinuity of the block edges and improve the accuracy of the normalization process. In addition, the present invention also proposes smoothing continuous sonar data frames in the time dimension to further improve the estimation accuracy of the expected echo intensity, thereby enhancing the stability of the entire system in complex environments. Through the above method, the present invention significantly improves the quality of active sonar imaging data and reduces the interference of reverberation on detection results, which is particularly suitable for long-distance small target detection tasks in complex shallow sea environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 The figure is a flow chart of a method for normalizing active sonar imaging data in an embodiment. DETAILED DESCRIPTION

[0043] In order to further understand the content of the present invention, the present invention is described in detail in conjunction with the accompanying drawings and embodiments. It should be understood that the embodiments are only for explaining the present invention and are not intended to limit it.

[0044] Example

[0045] like Figure 1 As shown, this embodiment provides a normalization method for active sonar imaging data, which includes the following steps:

[0046] S1. Construction of azimuth-distance matrix: Obtain azimuth-distance data through the sonar system and store it in a polar coordinate system;

[0047] In S1, the rows of the azimuth distance matrix represent different distance dimensions, and the columns represent different azimuth angles. Each element (i, j) represents the echo intensity at the i-th distance and the j-th angle. The echo intensity is obtained by beamforming and matched filtering. The azimuth distance matrix is ​​the basis for normalization processing.

[0048] This embodiment uses a sonar system with a circular receiving array. After the transmitting element emits a linear frequency modulation signal, the reflected signals at different angles are collected through beam forming and related matching is performed. For each set receiving angle, a vector of length P can be formed according to the echo delay. If the above process is performed on Q angles in total as shown in 102, a P×Q azimuth-range sonar imaging matrix can be formed as shown in 103. If the above operation is performed continuously, a series of azimuth-range sonar imaging matrices at different times can be formed, that is, a frame of data. The azimuth-range matrix at a specific time is recorded as M.

[0049] S2, data smoothing: after constructing the azimuth distance matrix, each row of data (i.e., echo intensities at different distances at the same azimuth) is smoothed to reduce the impact of noise;

[0050] In S2, the smoothing method includes the weighted average method of adjacent K rows, and the weights are rectangular windows, Hann windows, or Hamming windows. This smoothing process can effectively reduce the high-frequency noise in the distance dimension and obtain smoother background echo data. Due to the slow variation of the reverberation background, the smoothing operation is not limited to the distance dimension, but can also be performed in the time dimension, thereby further eliminating the fluctuation noise in time.

[0051] In S2, specifically:

[0052] Smoothing is performed by distance to reduce the noise of distance sonar data; for each row of the azimuth distance matrix M, K adjacent rows are selected for weighted averaging, and the weights can be constructed using common window functions (such as rectangular windows, Hann windows, and Hamming windows). The matrix after row smoothing is recorded as M', and its calculation process is expressed as:

[0053]

[0054] in[·] i,: represents the i-th row of the matrix, [·] j,: represents the jth row of the matrix, a j Represents a weighted value.

[0055] S3, calculating the expected echo intensity: after completing the smoothing process, the expected echo intensity of each detection point is calculated by a data-driven method; each row of data (i.e., the echo intensity at different distances in the same direction) is normalized according to the corresponding expected echo intensity;

[0056] The expected echo intensity is mainly related to the distance of the detection point, so it can be estimated by calculating the average echo intensity corresponding to each distance. However, the reverberation background may be unevenly distributed in angle, so when calculating the expected echo intensity, a more refined calculation can also be performed based on the characteristics of the angle sector. The purpose of this step is to estimate the background echo intensity at different distances and angles through statistical data, thereby providing a benchmark value for normalization.

[0057] The goal of normalization is to reduce the unevenness caused by the enhancement of close-range echoes or the attenuation of long-range echoes, so that the background reverberation in the sonar image can be balanced and the abnormal echo intensity related to the target can be highlighted. In order to avoid the situation where the denominator is zero during the normalization process, a small positive number \delta is introduced in the calculation to ensure the stability and reliability of the normalization process.

[0058] In S3, if only the distance factor is considered, it is as follows:

[0059] A) Calculate the expected echo intensity at the same distance; since each row of the matrix M' represents the echo reflection intensity at the same distance and different directions, the average value of each row of M' is calculated to obtain the expected echo intensity at the distance The calculation process is expressed as:

[0060]

[0061] Where Mean means calculating the average value of the elements in the vector;

[0062] B) Normalize the data using the expected echo intensity at each distance, and the normalized matrix is: It is calculated as:

[0063]

[0064] Where δ is a small positive number to avoid the denominator being 0 during normalization;

[0065] Considering the characteristics of the sonar deployment environment, the reverberation may have very large differences in different angle areas. At this time, normalization needs to consider not only the distance factor, but also the azimuth factor. If the distance factor and azimuth factor are considered, the specific ones are:

[0066] a) Divide the matrix M' into N blocks by column, each block has L columns (without loss of generality, assume L is an odd number), and calculate the expected echo intensity at the same distance in each block; since each row of the matrix M' represents the echo reflection intensity at different azimuths at the same distance, and each column proves the different echo angles, the average value of each row of M' is calculated by block to obtain the expected echo intensity in the angle region at the distance The calculation process is expressed as:

[0067]

[0068] in[·] i,j-(L-1) / 2:j+(L-1) / 2 Represents the elements from j-(L-1) / 2 to j+(L-1) / 2 in the i-th row of the matrix;

[0069] b) Normalize the data using the expected echo intensity at each distance, and the normalized matrix is: It is calculated as:

[0070]

[0071] When dividing the sonar imaging matrix into non-overlapping blocks, if the block edges are discontinuous, the following method is used for calculation:

[0072] 1) Construct the circulant matrix M':

[0073] M"=[[M'] :,Q-S+1:Q, M',[M'] :,1:S ]

[0074] in[·] :,Q-S+1:Q represents the elements of all rows from the Q-S+1th column to the Qth column of the matrix, where S is a positive integer not less than 1 / 2 of the filter length; [·] :,1:S Represents the elements of all rows from the 1st column to the Sth column of the matrix;

[0075] 2) Use an FIR filter with a length of 2S to filter each row of M”; the value of 2S should not be much larger than the pixels occupied by the possible target in terms of angle. The FIR filter can use a rectangular window or a Hann window;

[0076] 3) Take the middle Q columns of the M” matrix and record them as matrix N, that is:

[0077] N=[M] :,S+1:S+Q

[0078] [·] :,S+1:S+Q Represents the elements of all rows from the S+1th column to the S+Qth column of the matrix. This step is used to remove the edge effect when filtering the matrix row by row;

[0079] 4) Use matrix N to normalize matrix M', that is:

[0080]

[0081] where ⊙ denotes element-wise division of matrices.

[0082] Considering the continuous acquisition of data frames in continuous time, the time frame obtained at time t is M t, the estimation of the expected echo strength can also be further enhanced by time smoothing.

[0083] S4, coordinate system conversion: After normalization, the processed azimuth distance matrix data is converted from the polar coordinate system to the rectangular coordinate system. The rectangular coordinate system is more convenient for subsequent data analysis and visualization, especially when it needs to be fused and analyzed with other types of sensor data.

[0084] This embodiment uses normalization processing to compare echo data at different distances and angles under the same benchmark, thereby reducing the irregular distribution effect of reverberation in the image and improving the detectability of the target, especially in complex shallow sea environments. This embodiment can simplify parameter settings in subsequent target detection tasks, reduce the complexity of manual debugging, and improve the overall detection performance of the sonar system.

[0085] The present invention and its embodiments are described schematically above, and the description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. Therefore, if a person skilled in the art is inspired by it and designs a structural method and an embodiment similar to the technical solution without creativity without departing from the purpose of the invention, they shall all fall within the protection scope of the present invention.

Claims

1. A normalization method for active sonar imaging data, characterized in that: The following steps are involved: S1. Construction of azimuth-distance matrix: Obtain azimuth-distance data through the sonar system and store it in a polar coordinate system; S2, data smoothing: after constructing the azimuth distance matrix, each row of data is smoothed to reduce the impact of noise; S3, calculating the expected echo intensity: after completing the smoothing process, the expected echo intensity of each detection point is calculated by a data-driven method; Normalize each row of data according to the corresponding expected echo intensity; S4, coordinate system conversion: After normalization, the processed azimuth distance matrix data is converted from the polar coordinate system to the rectangular coordinate system.

2. The method for normalizing active sonar imaging data according to claim 1, characterized in that: In S1, the rows of the azimuth distance matrix represent different distance dimensions, and the columns represent different azimuth angles. Each element (i, j) represents the echo intensity at the i-th distance and the j-th angle. The echo intensity is obtained by beamforming and matched filtering. The azimuth distance matrix is ​​the basis for normalization processing.

3. The method for normalizing active sonar imaging data according to claim 2, characterized in that: In S2, the smoothing method includes the weighted average method of K adjacent rows, and the weights are rectangular windows, Hann windows, or Hamming windows.

4. The method for normalizing active sonar imaging data according to claim 3, characterized in that: In S2, specifically: Smoothing is performed by distance to reduce the noise of distance sonar data; for each row of the azimuth distance matrix M, K adjacent rows are selected for weighted averaging. The matrix after row smoothing is recorded as M'. The calculation process is expressed as: in[·] i,: represents the i-th row of the matrix, [·] j,: represents the jth row of the matrix, a j Represents a weighted value.

5. The method for normalizing active sonar imaging data according to claim 4, characterized in that: In S3, if only the distance factor is considered, it is as follows: A) Calculate the expected echo intensity at the same distance; calculate the average value of each row of M' to obtain the expected echo intensity at the distance The calculation process is expressed as: Where Mean means calculating the average value of the elements in the vector; B) Normalize the data using the expected echo intensity at each distance, and the normalized matrix is: It is calculated as: Where δ is a small positive number to avoid the denominator being 0 during normalization; If distance and orientation factors are taken into consideration, the details are as follows: a) Divide the matrix M' into N blocks by column, each block has L columns, and calculate the expected echo intensity at the same distance in each block; calculate the average value of each row of M' by block to obtain the expected echo intensity in the angle region under the distance The calculation process is expressed as: in[·] i,j-(L-1) / 2:j+(L-1) / 2 Represents the elements from j-(L-1) / 2 to j+(L-1) / 2 in the i-th row of the matrix; b) Normalize the data using the expected echo intensity at each distance, and the normalized matrix is: It is calculated as:

6. The method for normalizing active sonar imaging data according to claim 5, characterized in that: When dividing the sonar imaging matrix into non-overlapping blocks, if the block edges are discontinuous, the following method is used for calculation: 1) Construct the circulant matrix M': in[·] :,Q-S+1:Q represents the elements of all rows from the Q-S+1th column to the Qth column of the matrix, where S is a positive integer not less than 1 / 2 of the filter length; [·] :,1:S Represents the elements of all rows from the 1st column to the Sth column of the matrix; 2) Use an FIR filter of length 2S to filter each row of M”; 3) Take the middle Q columns of the M” matrix and record them as matrix N, that is: N=[M] :,S+1:S+Q [·] :,S+1:S+Q Represents the elements of all rows from the S+1th column to the S+Qth column of the matrix. This step is used to remove the edge effect when filtering the matrix row by row; 4) Use matrix N to normalize matrix M', that is: where ⊙ denotes element-wise division of matrices.

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