Acoustic imaging underwater target identification method based on contrast calculation

Abstract

The invention relates to an acoustic imaging underwater target recognition method based on contrast calculation, and the method comprises the steps: 1, obtaining the scale and position information of all suspected targets according to an intelligent recognition algorithm, including a real target and a false alarm judged by the intelligent recognition algorithm; 2, carrying out sound imaging background normalization; and step 3, using a mean filter to remove salt and pepper noise in the acoustic imaging data. According to the method, the processing method is combined with an intelligent identification method, and through multi-threshold setting, false alarms are reduced, and the target identification accuracy is improved.

Description

Technical field:

[0001] The invention belongs to the technical field of underwater acoustic engineering, and in particular relates to an acoustic imaging underwater target recognition method based on contrast calculation. Background technology:

[0002] Underwater target identification and positioning are becoming increasingly important in the field of hydroacoustics. Imaging sonar, especially side-scan sonar and synthetic aperture sonar, occupies a very important position in the identification of small underwater targets. When identifying static underwater targets, imaging sonar mainly analyzes the target's acoustic imaging reflection characteristics.

[0003] In the process of underwater target acoustic imaging, the transmitted signal can be used to construct the acoustic imaging result of the target after being reflected by the target and undergoing time delay compensation and spatial motion error compensation. The position of the target and the scale of the imaged target can be calculated based on the imaging distance and azimuth resolution of the sonar. However, when the underwater environment is complex, target recognition in the imaging sonar will be more difficult, and a large number of terrain features similar to those of small targets can easily be identified as targets. At present, in the process of underwater acoustic imaging target recognition based on intelligence, there is a problem of high repeated false alarms in complex terrain. Summary of the invention:

[0004] The technical problem to be solved by the present invention is to provide an acoustic imaging underwater target recognition method based on contrast calculation, which utilizes the high reflection intensity of the target signal and uses target characteristic values ​​similar to the signal-to-noise ratio to suppress false alarms in complex terrain and improve the accuracy of target recognition.

[0005] The technical solution of the present invention is to provide an acoustic imaging underwater target recognition method based on contrast calculation, the steps of the method comprising:

[0006] Step 1: Obtain the scale and location information of all suspected targets based on the intelligent recognition algorithm, including real targets and false alarms determined by the intelligent recognition algorithm.

[0007] Step 2: Normalize the acoustic imaging background. Due to the energy attenuation of signal reflection during the process, the acoustic imaging background needs to be normalized and balanced. Assuming that the size of the acoustic imaging area is M×N, the window is set to an m×n separation window according to the target scale size. The background equalization processing function is as follows:

[0008] Weight=conv(data M×N ,Window m×n )

[0009] Data = data.*(1 / Weight)

[0010] Where data is the input data, Window is the separation window, Weight is the calculated weight value, and Data is the data after background equalization.

[0011] Step 3: Use a mean filter to remove salt and pepper noise from the acoustic imaging data. The filter size is set to k×l. The processing function is as follows:

[0012]

[0013] Data = conv(Data,kernal k×l , stride=1)

[0014] Step 4: Extract the target information obtained in step 1, integrate the target position information, and calculate the target strength. To reduce the amount of calculation, set windows on the upper, middle, and lower sides of the target area center axis based on the selected target information, calculate the average, and then take the maximum value of the average as the target strength. The specific calculation process is as follows:

[0015] Target=Data target

[0016] T=max(mean(Target top ),mean(Target mid ),mean(Target down ))

[0017] Where T is the strength of the target.

[0018] Step 5: Calculate the intensity of the surrounding background based on the target information in step 3. The specific calculation process is as follows:

[0019] Background=Data background

[0020] B_mean=mean(Background)

[0021]

[0022] When calculating Background, the target area needs to be eliminated, and Num is the pixel value of the surrounding background.

[0023] Step 6: Calculate the contrast between the target and the surrounding environment. The specific calculation formula is as follows:

[0024] TBR=abs(T-B_mean) / B_inf

[0025] Step 7: Set the target confidence weight according to the target scale obtained in step 1. The specific weight settings are as follows, in meters:

[0026]

[0027] Step 8: weight the contrast according to the weight value, and set a threshold for the weighted contrast to achieve the effect of suppressing false alarms.

[0028] That is, TBR=20*lg(TBR*c).

[0029] Compared with the prior art, the present invention has the following advantages:

[0030] Aiming at the problem of high false alarm of complex terrain in underwater target acoustic imaging recognition, the present invention proposes an acoustic imaging underwater target recognition method based on contrast calculation, combines the processing method with an intelligent recognition method, reduces false alarms and improves the target recognition accuracy through multi-threshold setting. Description of the drawings:

[0031] Figure 1 It is a work flow chart of the present invention;

[0032] Figure 2 It is the target contrast result calculated by the present invention based on actual synthetic aperture acoustic imaging data. Specific implementation method:

[0033] The present invention will be further described below with reference to the accompanying drawings:

[0034] like Figure 1 As shown, the present invention provides an acoustic imaging underwater target recognition method based on contrast calculation. Based on imaging sonar data, the position and scale information of a large number of targets are obtained through an intelligent recognition and positioning algorithm. According to the obtained target information, the target intensity information and background information are obtained. The contrast calculation method proposed in the method is used to perform preliminary screening on a large number of recognition targets.

[0035] Specifically, the steps of the underwater target scale prediction method include:

[0036] Step 1: Obtain the scale and location information of all suspected targets based on the intelligent recognition algorithm, including real targets and false alarms determined by the intelligent recognition algorithm.

[0037] Step 2: Normalize the acoustic imaging background. Due to the energy attenuation of signal reflection during the process, the acoustic imaging background needs to be normalized and balanced. Assuming that the size of the acoustic imaging area is M×N, the window is set to an m×n separation window according to the target scale size. The background equalization processing function is as follows:

[0038] Weight=conv(data M×N ,Window m×n )

[0039] Data = data.*(1 / Weight)

[0040] Where data is the input data, M is 512, N is 1024, Window is the separation window, m is 50, n is 200, Weight is the calculated weight value, and Data is the data after background equalization.

[0041] Step 3: Use a mean filter to remove salt and pepper noise from the acoustic imaging data. The filter size is set to k×l. The processing function is as follows:

[0042]

[0043] Data = conv(Data,kernal k×l , stride=1)

[0044] Among them, k is 5 and l is 12.

[0045] Step 4: Extract the target information obtained in step 1, integrate the target position information, and calculate the target strength. To reduce the amount of calculation, set windows j×k on the upper, middle, and lower sides of the target area center axis according to the selected target information, calculate the average, and then take the maximum value of the average as the target strength. The specific calculation process is as follows:

[0046] Target=Data target

[0047] T=max(mean(Target top ),mean(Target mid ),mean(Target down ))

[0048] Where T is the intensity of the target, j is 6 and k is 24 in the window for extracting target information.

[0049] Step 5: Calculate the intensity of the surrounding background based on the target information in step 3. The specific calculation process is as follows:

[0050] Background=Data background

[0051] B_mean=mean(Background)

[0052]

[0053] When calculating Background, the target area needs to be eliminated, and Num is the pixel value of the surrounding background.

[0054] Step 6: Calculate the contrast between the target and the surrounding environment. The specific calculation formula is as follows:

[0055] TBR=abs(T-B_mean) / B_inf

[0056] Step 7: Set the target confidence weight according to the target scale obtained in step 1. The specific weight settings are as follows, in meters:

[0057]

[0058] Step 8: weight the contrast according to the weight value, and set a threshold for the weighted contrast to achieve the effect of suppressing false alarms.

[0059] TBR=20*lg(TBR*c)

[0060] like Figure 2 As shown, the present invention obtains target information based on acoustic imaging data and intelligent recognition and positioning algorithms, calculates the contrast of the target, further screens a large number of targets according to threshold information, and reduces false alarms in the underwater target recognition process.

[0061] The above is only an explanation of the preferred embodiments of the present invention, which should not be construed as a limitation on the claims. Any equivalent process changes made using the present invention specification are included in the patent protection scope of the present invention.

Claims

1. An acoustic imaging underwater target recognition method based on contrast calculation, characterized in that: The following steps are included: Step 1: Obtain the scale and location information of all suspected targets based on the intelligent recognition algorithm; Step 2: Normalize the acoustic imaging background. Assuming that the size of the acoustic imaging area is M×N, set the window to be m×n according to the target scale. The background equalization processing function is as follows: Weight=conv(data M×N ,Window m×n ) Data = data.*(1 / Weight) Where data is the input data, Window is the separation window, Weight is the calculated weight value, and Data is the data after background equalization; Step 3: Use a mean filter to remove salt and pepper noise from the acoustic imaging data. The filter size is set to k×l, and the processing function is as follows: Data=conv(Data,kernal k×l ,stride=1) Step 4: Extract the target information obtained in step 1, integrate the target position information, and calculate the target strength. The specific calculation process is as follows: Target=Data target T=max(mean(Target top ),mean(Target mid ),mean(Target down )) Where T is the intensity of the target; Step 5: Calculate the intensity of the surrounding background based on the target information in step 3. The specific calculation process is as follows: Background=Data background B_mean=mean(Background) Step 6: Calculate the contrast between the target and the surrounding environment. The specific calculation formula is as follows: TBR=abs(T-B_mean) / B_inf Step 7: Set the target confidence weight according to the target scale obtained in step 1. The specific weight settings are as follows, in meters: Step 8: weight the contrast according to the weight value, and set a threshold for the weighted contrast to achieve the effect of suppressing false alarms.

2. The method for underwater target recognition based on acoustic imaging and contrast calculation according to claim 1, characterized in that: In step 1, the suspected targets include real targets and false alarms determined by the intelligent recognition algorithm.

3. The method for underwater target recognition based on acoustic imaging and contrast calculation according to claim 1, characterized in that: In step 4, in order to reduce the amount of calculation, windows are set on the upper, middle and lower sides of the central axis of the target area according to the target information selected by the box, the average is calculated, and the maximum value of the average is taken as the intensity of the target.

4. The method for underwater target recognition based on acoustic imaging and contrast calculation according to claim 1, characterized in that: When calculating Background, the target area needs to be eliminated, and Num is the pixel value of the surrounding background.

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