CN110794410A - Method for detecting probability of scanning line target by side-scan sonar - Google Patents
Method for detecting probability of scanning line target by side-scan sonar Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 23
- 238000012545 processing Methods 0.000 claims abstract description 19
- 238000007476 Maximum Likelihood Methods 0.000 claims abstract description 17
- 238000010606 normalization Methods 0.000 claims abstract description 16
- 238000000605 extraction Methods 0.000 claims abstract description 4
- 238000004364 calculation method Methods 0.000 claims description 6
- 238000005315 distribution function Methods 0.000 claims description 3
- 238000001514 detection method Methods 0.000 abstract description 11
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S15/00—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems
- G01S15/88—Sonar systems specially adapted for specific applications
- G01S15/89—Sonar systems specially adapted for specific applications for mapping or imaging
- G01S15/8902—Side-looking sonar
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/52—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S15/00
- G01S7/539—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S15/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
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Abstract
A method for detecting the probability of a side-scan sonar scanning line target comprises the following steps: carrying out normalization processing on sonar scanning line data by adopting a moving average method, and minimizing sonar data change caused by different seabed and inclined ranges; taking the set logarithmic energy parameter, the distribution shape parameter and the distribution error parameter as characteristic parameters, and performing characteristic extraction on the sonar scanning line data; combining the 3 characteristic parameters in the step 2) through a maximum likelihood classification detector to obtain a maximum likelihood hypothesis after the 3 characteristic parameters are combined, namely the probability of the scanning line target of the side-scan sonar. The invention utilizes the judgment of the scanning line of the side-scan sonar, designs the combination characteristics of logarithmic energy value, distribution shape parameter function and distribution error as detection, and has high detection accuracy. Compared with direct side-scan sonar image processing, the method has the advantages that the existence probability of the scanning line target is judged, and the generated navigation information is good in real-time performance by combining the information of the autonomous underwater vehicle.
Description
Technical Field
The invention relates to a target probability detection method. In particular to a method for detecting the probability of a side-scan sonar scanning line target.
Background
Sonar detection technology is a key means in the field of ocean mapping. The side-scan sonar transmits sound waves to the seabed and then receives the reflected sound waves in sequence, and a strip for describing the topography and landform of the seabed is drawn by utilizing the principle that different seabed geologies reflect different sound wave intensities. And the underwater acoustic channel has complex and variable characteristics, and the acoustic wave has projection and scattering characteristics, so that the quality of the obtained image is poor.
Autonomous Underwater Vehicles (AUVs) are typically equipped with side-scan sonar to produce an image of the sea floor. Sonar signals received after backscatter and reflection from the seafloor are typically enhanced by amplification that varies with time. The basic process of scanline formation is to propagate an acoustic signal through the sea water, with subsequent energy scattering from the seafloor (backscatter signal) and received by the transducer. The energy returned to the transducer is then used to create a scan line and displayed as a line of pixels with gray values.
The number of each pixel, determined by the currently returned intensity, is particularly important in selecting the appropriate feature for target detection.
Disclosure of Invention
The invention aims to solve the technical problem of providing a method for detecting the probability of a side-scan sonar scanning line target, which utilizes a side-scan sonar imaging graph to perform landmark image processing to position and generate navigation data with time delay.
The technical scheme adopted by the invention is as follows: a method for detecting the probability of a side-scan sonar scanning line target comprises the following steps:
1) the method adopts a moving average method to carry out normalization processing on sonar scanning line data, minimizes sonar data changes caused by different seabed and inclined ranges, and has the following normalization processing formula:
where W is the window length, t is the time, Sr(t) Sonar scan line data for t points on a scan line, Sn(t) is sonar scanning line data of t points after normalization processing, and (t +/-n) represents sonar scanning line data of nth points before or after the t points on the scanning lines;
2) taking the set logarithmic energy parameter, the distribution shape parameter and the distribution error parameter as characteristic parameters, and performing characteristic extraction on the sonar scanning line data;
3) combining the 3 characteristic parameters in the step 2) through a maximum likelihood classification detector to obtain a maximum likelihood hypothesis after the 3 characteristic parameters are combined, namely the probability of the scanning line target of the side-scan sonar.
Step 2) the following steps:
(1) logarithmic energy parameter EmIs expressed as:
where ε is the small normal added to prevent the computation of the logarithm of zeros, b is the selected processed sonar scan line data length, SFIs the frequency domain representation of the sonar scan line data of the m-th point in the b length after normalization processing;
(2) the distribution shape parameter M is the shape parameter of the Nakagami density function, represents the statistical distribution of the signals, and the calculation formula is expressed as:
wherein,
expressing expectation of the sonar scanning line data after normalization processing;
(3) the distribution Error parameter Error, which is the difference between the scan line signal histogram and the Nakagami distribution fit, is calculated as:
where N is the Nakagami probability distribution function, G is the gray scale level, and G represents the signal histogram.
The calculation formula of the maximum likelihood hypothesis h of each characteristic parameter in the step 3) is as follows:
where P is a probability function, FiIs logarithmic energyA parameter or a distribution shape parameter or a distribution error parameter;
when the values of the corresponding feature parameters are known, the maximum likelihood hypothesis h for each feature parameter is calculated as follows:
the maximum likelihood hypothesis formula after the combination of the 3 characteristic parameters is as follows:
h＝argmax{P(Tp)*P(Em,M,Error|Tp),P(Tab)*P(Em,M,Error|Tab)} (7)
where h is the side-scan sonar scan-line object probability, P (Tp) represents the probability that the object is present in the selected sonar scan-line data, and P (T)ab) Is the probability that the object is not present in the selected sonar scan line data, EmIs a logarithmic energy parameter; m is a distribution shape parameter and Error is a distribution Error parameter.
The method for detecting the target probability of the scanning line of the side-scan sonar utilizes the judgment of the scanning line of the side-scan sonar, designs the combination characteristics of logarithmic energy values, distribution shape parameter functions and distribution errors as detection, and has high detection accuracy. Compared with direct side-scan sonar image processing, the method has the advantages that the existence probability of the scanning line target is judged, and the generated navigation information is good in real-time performance by combining the information of the autonomous underwater vehicle.
Drawings
Fig. 1 is a flow chart of a method for detecting the probability of a side-scan sonar scan-line target according to the present invention.
Detailed Description
The following describes a method for detecting the probability of a scan line target of a side-scan sonar according to the present invention in detail with reference to the following embodiments and accompanying drawings.
As shown in fig. 1, the method for detecting the probability of a scan line target of a side-scan sonar according to the present invention includes the following steps:
1) the method adopts a moving average method to carry out normalization processing on sonar scanning line data, minimizes sonar data changes caused by different seabed and inclined ranges, and has the following normalization processing formula:
where W is the window length, t is the time, Sr(t) Sonar scan line data for t points on a scan line, Sn(t) is sonar scanning line data of t points after normalization processing, and (t +/-n) represents sonar scanning line data of nth points before or after the t points on the scanning lines;
2) taking the set logarithmic energy parameter, the distribution shape parameter and the distribution error parameter as characteristic parameters, and performing characteristic extraction on the sonar scanning line data; it is as follows:
(1) logarithmic energy parameter EmIs expressed as:
where ε is the small normal added to prevent the computation of the logarithm of zeros, b is the selected processed sonar scan line data length, SFIs the frequency domain representation of the sonar scan line data of the m-th point in the b length after normalization processing;
(2) the distribution shape parameter M is the shape parameter of the Nakagami density function, represents the statistical distribution of the signals, and the calculation formula is expressed as:
wherein,expressing expectation of the sonar scanning line data after normalization processing;
the profile shape parameter M conforms to the Nakagami profile and is originally used to describe statistical data of the echo envelope in radar and wireless communication systems, and the parameter is easier to estimate and is used to simulate the scattering signal of sand on the sea floor and the hard sea floor.
(3) The distribution Error parameter Error, which is the difference between the scan line signal histogram and the Nakagami distribution fit, is calculated as:
where N is the Nakagami probability distribution function, G is the gray scale level, and G represents the signal histogram.
3) Combining the 3 characteristic parameters in the step 2) through a maximum likelihood classification detector to obtain a maximum likelihood hypothesis after the 3 characteristic parameters are combined, namely the probability of the scanning line target of the side-scan sonar.
Firstly, calculating a maximum likelihood hypothesis h of each characteristic parameter, wherein the calculation formula is as follows:
where P is a probability function, FiIs a logarithmic energy parameter or a distribution shape parameter or a distribution error parameter;
when the values of the corresponding feature parameters are known, the maximum likelihood hypothesis h for each feature parameter is calculated as follows:
and then, calculating the maximum likelihood hypothesis after the combination of the 3 characteristic parameters, wherein the calculation formula is as follows:
h＝argmax{P(Tp)*P(Em,M,Error|Tp),P(Tab)*P(Em,M,Error|Tab)} (7)
where h is the side-scan sonar scan-line object probability, P (Tp) represents the probability that the object is present in the selected sonar scan-line data, and P (T)ab) Is the probability that the object is not present in the selected sonar scan line data, EmIs a logarithmic energy parameter; m is a distribution shape parameter and Error is a distribution Error parameter.
The comparison of the performance of different combinations of logarithmic energy parameters, distribution shape parameters and distribution error parameters is shown in table 1:
TABLE 1 comparison of Performance of various combinations of test features
In the table, 1, 2 and 3 correspond to a logarithmic energy parameter, a distribution shape parameter and a distribution error parameter, respectively.
The combination of the parameters 1 and 2 is common selection characteristics of the traditional detection, the parameter 3 is provided, various characteristics are combined, and the combination of 1&2&3 is selected as the detection characteristics through experimental comparison. As shown in the above table, the present invention provides a distribution error parameter, which is denoted by 3, the distribution error parameter 3 is combined with the parameter 1 or 2 and the distribution error parameter 3 is combined with the parameters 1 and 2, respectively, and under the same side scan sonar scan line data, the accuracy of the combination of the distribution error parameter 3 and the parameters 1 and 2 is significantly higher than that of other combination modes, so that the detection rate of the side scan sonar scan line target probability detection method of the present invention is high.
Claims (3)
1. A method for detecting the probability of a side-scan sonar scanning line target is characterized by comprising the following steps:
1) the method adopts a moving average method to carry out normalization processing on sonar scanning line data, minimizes sonar data changes caused by different seabed and inclined ranges, and has the following normalization processing formula:
where W is the window length, t is the time, Sr(t) Sonar scan line data for t points on a scan line, Sn(t) is sonar scanning line data of t points after normalization processing, and (t +/-n) represents sonar scanning line data of nth points before or after the t points on the scanning lines;
2) taking the set logarithmic energy parameter, the distribution shape parameter and the distribution error parameter as characteristic parameters, and performing characteristic extraction on the sonar scanning line data;
3) combining the 3 characteristic parameters in the step 2) through a maximum likelihood classification detector to obtain a maximum likelihood hypothesis after the 3 characteristic parameters are combined, namely the probability of the scanning line target of the side-scan sonar.
2. The method for detecting the probability of the side-scan sonar scan-line target according to claim 1, wherein the step 2) comprises:
(1) logarithmic energy parameter EmIs expressed as:
where ε is the small normal added to prevent the computation of the logarithm of zeros, b is the selected processed sonar scan line data length, SFIs the frequency domain representation of the sonar scan line data of the m-th point in the b length after normalization processing;
(2) the distribution shape parameter M is the shape parameter of the Nakagami density function, represents the statistical distribution of the signals, and the calculation formula is expressed as:
wherein,expressing expectation of the sonar scanning line data after normalization processing;
(3) the distribution Error parameter Error, which is the difference between the scan line signal histogram and the Nakagami distribution fit, is calculated as:
where N is the Nakagami probability distribution function, G is the gray scale level, and G represents the signal histogram.
3. The method for detecting the probability of the scan line target of the side-scan sonar according to claim 1, wherein the maximum likelihood hypothesis h for each feature parameter in step 3) is calculated as follows:
where P is a probability function, FiIs a logarithmic energy parameter or a distribution shape parameter or a distribution error parameter;
when the values of the corresponding feature parameters are known, the maximum likelihood hypothesis h for each feature parameter is calculated as follows:
the maximum likelihood hypothesis formula after the combination of the 3 characteristic parameters is as follows:
h＝argmax{P(Tp)*P(Em,M,Error|Tp),P(Tab)*P(Em,M,Error|Tab)} (7)
where h is the side-scan sonar scan-line object probability, P (Tp) represents the probability that the object is present in the selected sonar scan-line data, and P (T)ab) Is the probability that the object is not present in the selected sonar scan line data, EmIs a logarithmic energy parameter; m is a distribution shape parameter and Error is a distribution Error parameter.
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| US4025919A (en) * | 1975-11-14 | 1977-05-24 | Westinghouse Electric Corporation | Automatic target detector |
| US20030055640A1 (en) * | 2001-05-01 | 2003-03-20 | Ramot University Authority For Applied Research & Industrial Development Ltd. | System and method for parameter estimation for pattern recognition |
| CN105372663A (en) * | 2015-12-01 | 2016-03-02 | 宁波工程学院 | Resampling method facing images of sidescan sonar |
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Patent Citations (3)
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|---|---|---|---|---|
| US4025919A (en) * | 1975-11-14 | 1977-05-24 | Westinghouse Electric Corporation | Automatic target detector |
| US20030055640A1 (en) * | 2001-05-01 | 2003-03-20 | Ramot University Authority For Applied Research & Industrial Development Ltd. | System and method for parameter estimation for pattern recognition |
| CN105372663A (en) * | 2015-12-01 | 2016-03-02 | 宁波工程学院 | Resampling method facing images of sidescan sonar |
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| 吕良;周超烨;陈春;金绍华;熊传梁;孙新轩;: "基于虚警函数的侧扫声纳水下目标实时检测方法", no. 04 * |
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