Unmanned ship multi-beam sonar detection method, system, equipment, product and medium

Abstract

The invention relates to the field of sonar signal processing, and provides an unmanned ship multi-beam sonar detection method, system, device, product and medium, and the method comprises the steps: obtaining a side-scan sonar and a multi-beam sonar, scanning the submarine topography through the side-scan sonar and the multi-beam sonar, and obtaining a multi-beam scanning signal and a side scanning signal; performing Kalman filtering to obtain a first filtering signal, and filtering the first filtering signal through an adaptive filtering threshold to obtain a second filtering signal; constructing a minimization energy function, obtaining an optimized filtering signal through the minimization energy function, and carrying out the iteration of the optimized filtering signal, and obtaining a first terrain signal; constructing a generative adversarial network by using a generator and a discriminator, and performing terrain reconstruction on the first terrain signal through the generative adversarial network to obtain a second terrain signal; and correcting the second topographic signal through sound velocity measurement equipment to obtain a target topographic signal, and constructing an underwater topographic scanning graph according to the target topographic signal.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of sonar signal processing, and in particular to an unmanned surface vehicle multi-beam sonar detection method, system, device, product and medium. BACKGROUND

[0002] An unmanned surface vehicle is a kind of unmanned ship with the characteristics of autonomous navigation and autonomous control. It has wide application value in the fields of ocean exploration, ocean environment monitoring and sea rescue, etc. Multi-beam sonar is a kind of high-precision underwater detection equipment, which can provide information such as underwater target position, seabed topography and ocean depth. Loading multi-beam sonar on unmanned surface vehicle can realize the measurement and mapping of seabed topography. It provides support for the safe navigation of surface ships and submarines, and also helps to detect seabed resources. These ocean measurement unmanned vehicles can be widely used in ocean observation and research, and provide important data support for the development of ocean science and related fields.

[0003] How to reconstruct seabed topography information from existing multi-beam sonar data is the key and difficulty. However, due to the complex and changeable motion state of the unmanned surface vehicle and the underwater environment, the detection data of the multi-beam sonar has noise, error and deviation, which needs to be filtered, optimized and fused. In addition, the density of seawater at different depths is different, which leads to different sound speeds in seawater, which will also have a certain impact on the detection results of the sonar. SUMMARY

[0004] The present application aims to at least solve one of the technical problems in the related art. To this end, the present application provides an unmanned surface vehicle multi-beam sonar detection method, system, device, product and medium, which realizes the construction of high-precision underwater topographic scanning map.

[0005] The present application provides an unmanned surface vehicle multi-beam sonar detection method, which comprises: S1: acquiring a side-scan sonar and a multi-beam sonar, using the side-scan sonar and the multi-beam sonar to scan the seabed topography, and obtaining multi-beam scanning signals and side-scan signals; S2: Kalman filtering the multi-beam scanning signals and the side-scan signals to obtain a first filtered signal, calculating an adaptive filtering threshold, filtering the first filtered signal through the adaptive filtering threshold to obtain a second filtered signal; S3: calculating an edge weight using the second filtered signal, constructing a minimum energy function through the edge weight and the second filtered signal, obtaining an optimized filtered signal through the minimum energy function, and iterating the optimized filtered signal to obtain a first topographic signal; S4: acquiring a generator and a discriminator, constructing a generative adversarial network using the generator and the discriminator, and reconstructing the first topographic signal through the generative adversarial network to obtain a second topographic signal; S5: Acquire a sound velocity measurement device, correct the second terrain signal using the sound velocity measurement device to obtain a target terrain signal, and construct an underwater terrain scanning map based on the target terrain signal.

[0006] According to the unmanned boat multi-beam sonar detection method provided by the present invention, in step S1, a side-scan sonar and a multi-beam sonar are obtained, the side-scan sonar is placed into the water by towing a bracket, the multi-beam sonar is used to scan to obtain a multi-beam signal, and the side-scan sonar is used to scan to obtain a side-scan signal.

[0007] According to the unmanned boat multi-beam sonar detection method provided by the present invention, in step S2, after obtaining the first filtered signal, the adaptive coefficient is determined, a local scanning signal is obtained from the first filtered signal, the local standard deviation of the local scanning signal is calculated, and the adaptive filtering threshold is calculated based on the local standard deviation and the adaptive coefficient.

[0008] According to the unmanned boat multi-beam sonar detection method provided by the present invention, step S3 further includes: S31: Determine a scale parameter, and calculate edge weights using the scale parameter and the second filtered signal; S32: determining a regularization parameter, and constructing the minimization energy function by using the regularization parameter, the second filtered signal, and the edge weight; S33: Determine the iteration stop condition, select an optimization algorithm, substitute the minimized energy function into the optimization algorithm to obtain the optimized filtered signal and iterate the optimized filtered signal until the iteration stop condition is met, and use the optimized filtered signal after iteration as the first terrain signal.

[0009] According to the unmanned boat multi-beam sonar detection method provided by the present invention, in step S4, the noise distribution of the first terrain signal is determined, the first terrain signal distribution is determined based on the first terrain signal, and a generative adversarial network is constructed using the noise distribution, the first terrain signal distribution, the generator and the discriminator. The generator and the discriminator are updated, and the first terrain signal is reconstructed by the updated generator to obtain the second terrain signal.

[0010] According to the unmanned boat multi-beam sonar detection method provided by the present invention, step S5 further includes: S51: obtaining a sound velocity measurement device including a sound velocity profiler and a surface sound velocity meter, installing the sound velocity profiler at the installation location of the multi-beam sonar, and hanging the surface sound velocity meter in the water; S52: Obtain a first correction parameter through the sound velocity profiler, obtain a second correction parameter through the surface sound velocity meter, correct the second terrain signal according to the first correction parameter and the second correction parameter to obtain a target terrain signal, and construct an underwater terrain scanning map according to the target terrain signal.

[0011] The present invention also provides an unmanned boat multi-beam sonar detection system, comprising: Sonar scanning module: used to obtain side scan sonar and multi-beam sonar, use side scan sonar and multi-beam sonar to scan the seabed terrain and obtain multi-beam scanning signals and side scan signals; A second filtering signal module is configured to perform Kalman filtering on the multi-beam scanning signal and the side scanning signal to obtain a first filtered signal, calculate an adaptive filtering threshold, and filter the first filtered signal using the adaptive filtering threshold to obtain a second filtered signal; First terrain signal module: used to calculate edge weights using the second filtered signal, construct a minimization energy function using the edge weights and the second filtered signal, obtain an optimized filtered signal by minimizing the energy function, and iterate the optimized filtered signal to obtain a first terrain signal; Second terrain signal module: used to obtain a generator and a discriminator, use the generator and the discriminator to build a generative adversarial network, and use the generative adversarial network to reconstruct the terrain of the first terrain signal to obtain a second terrain signal; Underwater terrain scanning map module: used to obtain a sound velocity measurement device, correct the second terrain signal through the sound velocity measurement device, obtain a target terrain signal, and construct an underwater terrain scanning map based on the target terrain signal.

[0012] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of any of the above-described unmanned boat multi-beam sonar detection methods are implemented.

[0013] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of any of the above-mentioned unmanned boat multi-beam sonar detection methods are implemented.

[0014] The present invention also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer can perform the steps of any of the above-mentioned unmanned boat multi-beam sonar detection methods.

[0015] The above one or more technical solutions in the embodiments of the present invention have at least one of the following technical effects: The unmanned boat multi-beam sonar detection method, system, equipment, product and medium provided by the present invention have good adaptability and robustness by double filtering the multi-beam scanning signal and correcting the signal by minimizing the energy function and generating an adversarial network. It can realize high-precision and high-reliability bathymetric data measurement. By correcting the sound speed, the purpose of correcting the multi-beam bathymetric system is achieved, thereby improving the accuracy and efficiency of seabed topography and landform measurement carried out by ocean measurement unmanned boats equipped with multi-beam sonar.

[0016] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned by practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 It is a flow chart of the multi-beam sonar detection method for an unmanned boat provided by the present invention.

[0019] Figure 2 This is an underwater terrain scanning map of the unmanned boat multi-beam sonar detection method provided by the present invention.

[0020] Figure 3 It is a structural schematic diagram of the multi-beam sonar detection system for an unmanned boat provided by the present invention.

[0021] Figure 4 It is a structural schematic diagram of the multi-beam sonar detection device for an unmanned boat provided by the present invention.

[0022] Reference numerals: 100, sonar scanning module; 200, second filtering signal module; 300, first terrain signal module; 400, second terrain signal module; 500, underwater terrain scanning map module; 810, processor; 820, communication interface; 830, memory; 840, communication bus. DETAILED DESCRIPTION

[0023] To make the purpose, technical solutions and advantages of the present invention clearer, the technical solutions in the present invention will be clearly and completely described below. Obviously, the embodiments described are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. The following embodiments are used to illustrate the present invention, but are not used to limit the scope of the present invention.

[0024] In the description of the embodiments of the present invention, it should be noted that the terms "center", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing the embodiments of the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore should not be understood as limiting the embodiments of the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only and should not be understood as indicating or implying relative importance.

[0025] In the description of the embodiments of the present invention, it should be noted that, unless otherwise specified or limited, the terms "connected" and "connection" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integral connections; mechanical connections, electrical connections; and direct connections or indirect connections through an intermediary. Those skilled in the art will understand the specific meanings of the above terms in the embodiments of the present invention based on the specific circumstances.

[0026] In the description of this specification, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the embodiment of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0027] The following combination Figures 1 to 4 Describe the specific embodiments of the present invention, Figure 1 The following is a flow chart of the multi-beam sonar detection method for unmanned boats provided by the present invention, including: S1: Acquire side scan sonar and multi-beam sonar, use side scan sonar and multi-beam sonar to scan the seabed topography and obtain multi-beam scanning signals and side scan signals; Furthermore, the purpose of this stage is to use side-scan sonar and multi-beam sonar to scan the seabed topography and obtain multi-beam scanning signals and side-scan signals. Specifically, in step S1, the side-scan sonar and multi-beam sonar are obtained, the side-scan sonar is placed into the water by towing a bracket, and the multi-beam sonar is used to scan to obtain a multi-beam signal, and the side-scan sonar is used to scan to obtain a side-scan signal.

[0028] Regarding the above steps, the specific implementation methods in this embodiment are as follows: First, it is necessary to obtain side-scan sonar and multi-beam sonar, and set up a moon pool and flange structure at the front of the surface unmanned boat, so that the multi-beam sonar can be installed in the flange structure. This can reduce the impact of cavitation generated during the navigation of the surface unmanned boat on the multi-beam sonar. In addition, a towing bracket and a matching winch are provided on the side of the surface unmanned boat. The side-scan sonar is connected to the winch cable and the cable is arranged on the towing bracket. The winch can be used to control the retraction and extension of the side-scan sonar and put the side-scan sonar into the water. Then, the multi-beam sonar is used to scan the terrain of the seabed to obtain a multi-beam scanning signal. The side-scan sonar is used to scan the same piece of seabed terrain to obtain a side-scan signal.

[0029] S2: performing Kalman filtering on the multi-beam scanning signal and the side scanning signal to obtain a first filtered signal, calculating an adaptive filtering threshold, and filtering the first filtered signal using the adaptive filtering threshold to obtain a second filtered signal; Furthermore, the purpose of this stage is to perform dual filtering on the multi-beam scanning signal and the side scanning signal to obtain a second filtered signal. Specifically, in step S2, after obtaining the first filtered signal, an adaptive coefficient is determined, a local scanning signal is obtained from the first filtered signal, the local standard deviation of the local scanning signal is calculated, and the adaptive filtering threshold is calculated based on the local standard deviation and the adaptive coefficient.

[0030] Regarding the above steps, the specific implementation methods in this embodiment are as follows: First, the multi-beam scanning signal and the side scanning signal need to be Kalman filtered to perform the first layer of the dual filtering. After the Kalman filter, the first filtered signal is obtained. Then, the first filtered signal needs to be filtered a second time. Here, the adaptive coefficient α must be determined. The adaptive coefficient is determined based on experience and the hydrological environment of the sea area. If the hydrological environment is highly disturbed or the water depth is deep, the adaptive coefficient can be appropriately increased to improve the filtering strength. Otherwise, the adaptive coefficient can be appropriately reduced.

[0031] Then the local scanning signal is obtained from the first filtered signal, and the local scanning signal is obtained by determining a reasonable point radius according to the complexity of the seabed topography, then selecting one of the scanning points of the multi-beam scanning signal and the side scanning signal as the center of the sphere, that is, a sphere is obtained, and all scanning points in the sphere are taken as the local scanning signal in the kth scanning area, and the standard deviation is obtained, that is, the local standard deviation of the kth scanning area , and then the adaptive filtering threshold of the kth scanning area is calculated according to the local standard deviation and the adaptive coefficient : Then the first filtered signal is filtered by the adaptive filtering threshold, and the specific method is to calculate the absolute value of the difference between the scanning point in the scanning area and the numerical average of all scanning points in the scanning area, and compare the obtained value with the adaptive filtering threshold. If it is greater than the adaptive filtering threshold, it is considered that the value of the scanning point is abnormal and should be replaced by the numerical average of all scanning points in the scanning area, otherwise it is considered that the scanning point can be retained, so the filtering is completed. All scanning areas in the seabed topography are filtered in the above manner, and the second filtered signal is obtained.

[0032] S3: using the second filtered signal to calculate the edge weight, constructing the minimum energy function by the edge weight and the second filtered signal, obtaining the optimized filtering signal by the minimum energy function, and iterating the optimized filtering signal to obtain the first topography signal; Further, the purpose of this stage is to construct the minimum energy function by the edge weight and the second filtered signal, so as to obtain the optimized filtering signal and iterate, and finally obtain the first topography signal. Specifically, step S3 further comprises: S31: determining the scale parameter, and calculating the edge weight using the scale parameter and the second filtered signal; S32: determining the regularization parameter, constructing the minimum energy function by the regularization parameter, the second filtered signal and the edge weight; S33: determining the iteration stopping condition, selecting an optimization algorithm, and substituting the minimum energy function into the optimization algorithm, so as to obtain the optimized filtering signal and iterate the optimized filtering signal until the iteration stopping condition is met. The optimized filtering signal after iteration is taken as the first topography signal.

[0033] For the above steps, the specific implementation in this embodiment is as follows: First, the scale parameter for calculating the edge weight needs to be determined according to experience , and then the second filtered signal data of the ith scanning point from the multi-beam scanning signal is extracted from the second filtered signal and the second filtered signal data of the jth scan point from the side scan signal in the second filtered signal so that the edge weight between them can be calculated where exp() represents the exponential operation on the content in the parentheses, represents the calculation of the Euclidean distance between the parameters inside it. Calculate the edge weight between all scan points from the multi-beam scan signal and the scan points from the side scan signal in the second filtered signal.

[0034] Then determine the regularization parameter and then the minimum energy function can be constructed by the regularization parameter, the second filtered signal and the edge weight where X is the set of optimized filtered signals, is the ith optimized filtered signal, that is, the optimized filtered signal from the ith scan point in the multi-beam scan signal in the last iteration, if the optimized filtered signal has not been iterated before, it takes the value of the second filtered signal data from the ith scan point in the multi-beam scan signal, is the jth optimized filtered signal, that is, the optimized filtered signal from the jth scan point in the side scan signal in the last iteration, if the optimized filtered signal has not been iterated before, it takes the value of the second filtered signal data from the jth scan point in the side scan signal.

[0035] Then substitute the minimum energy function into the optimization algorithm such as simulated annealing algorithm, particle swarm optimization, etc., so that the value of the minimum energy function is as small as possible, that is, the new optimized filtered signal is obtained, input the new optimized filtered signal into the minimum energy function to get a new minimum energy function to perform a cycle, so as to realize the iteration of the optimized filtered signal. Then determine the iteration stopping condition, for example, when the predetermined number of iterations is reached, at this time the value of the minimum energy function is small enough, that is, the iteration can be stopped, and the optimized filtered signal after iteration is taken as the first terrain signal.

[0036] S4: Obtain the generator and the discriminator, use the generator and the discriminator to construct the generative adversarial network, and perform terrain reconstruction on the first terrain signal through the generative adversarial network to obtain the second terrain signal; ​​Furthermore, the purpose of this stage is to construct a generative adversarial network using the generator and discriminator, thereby reconstructing the first terrain signal to obtain a second terrain signal. Specifically, in step S4, the noise distribution of the first terrain signal is determined, and the first terrain signal distribution is determined based on the first terrain signal. The noise distribution, the first terrain signal distribution, the generator, and the discriminator are used to construct a generative adversarial network. The generator and the discriminator are then updated. The updated generator is used to reconstruct the first terrain signal to obtain the second terrain signal.

[0037] Regarding the above steps, the specific implementation methods in this embodiment are as follows: First, the noise distribution of the first terrain signal is determined empirically , that is, the mathematical expectation function of the noise of the first terrain signal, and the first terrain signal distribution is determined according to the first terrain signal , which is the mathematical expectation function of the first terrain signal, obtain the generator G and the discriminator D, and thus use the noise distribution, the first terrain signal distribution, the generator and the discriminator to construct a generative adversarial network: in, is the value function of the minimax game objective between the discriminator and the generator, is the discriminator network function, G() is the generator network function, Indicates that the probability value output by the generator is minimized. Indicates that the probability value output by the discriminator is maximized, z is the noise of the first terrain signal, For the first terrain signal, a minimax game is performed through a generative adversarial network to train the generator and discriminator in the generative adversarial network. In this way, the trained generator can reconstruct the terrain of the first terrain signal according to the distribution of the first terrain signal, and generate a second terrain signal that is close to the real situation and eliminates noise interference.

[0038] S5: Acquire a sound velocity measurement device, correct the second terrain signal using the sound velocity measurement device to obtain a target terrain signal, and construct an underwater terrain scanning map based on the target terrain signal.

[0039] Furthermore, the purpose of this stage is to correct the second terrain signal by using the sound velocity measurement device, thereby constructing an underwater terrain scanning map. Specifically, step S5 further includes: S51: obtaining a sound velocity measurement device including a sound velocity profiler and a surface sound velocity meter, installing the sound velocity profiler at the installation location of the multi-beam sonar, and hanging the surface sound velocity meter in the water; S52: obtaining a first correction parameter through the sound velocity profiler, obtaining a second correction parameter through the surface sound velocity meter, correcting the second terrain signal according to the first correction parameter and the second correction parameter to obtain a target terrain signal, and constructing an underwater terrain scanning map according to the target terrain signal.

[0040] For the above steps, the specific implementation in this embodiment is as follows: Firstly, a sound velocity measuring device including a sound velocity profiler and a surface sound velocity meter needs to be obtained. The sound velocity profiler needs to be installed at the installation position of the multi-beam sonar, that is, inside the flange structure. The surface sound velocity meter needs to be hung in the water below the surface unmanned vehicle through the moon pool on the surface unmanned vehicle, and the surface sound velocity meter is connected with the winch, so that the sound velocity at different depths of the underwater can be measured by the winch.

[0041] Then, the sound velocity profiler can measure the sound velocity at the installation position of the multi-beam sonar, so as to obtain a first correction parameter according to the sound velocity at the installation position of the multi-beam sonar. Similarly, the surface sound velocity meter can measure the sound velocity at different depths of the underwater, so as to obtain a second correction parameter according to the sound velocity at different depths of the underwater. The first correction parameter and the second correction parameter can correct the second terrain signal to correct the influence of the change of the sound velocity on the second terrain signal, so that the target terrain signal can be obtained. Finally, the underwater terrain scanning map can be constructed according to the target terrain signal, as shown in Figure 2

[0042] The present application can effectively realize high-precision and high-reliability depth data measurement, and exclude the influence of sound velocity, so as to generate a high-precision underwater terrain scanning map.

[0043] The unmanned vehicle multi-beam sonar detection device provided by the present application will be described below. The unmanned vehicle multi-beam sonar detection device described below can be referred to each other corresponding to the unmanned vehicle multi-beam sonar detection method described above.

[0044] Figure 3 The structure diagram of the unmanned vehicle multi-beam sonar detection system is shown in Figure 3 The unmanned vehicle multi-beam sonar detection system is used to execute the unmanned vehicle multi-beam sonar detection method as described above, and includes: The sonar scanning module 100 is used to obtain the side scan sonar and the multi-beam sonar, and scan the seabed terrain using the side scan sonar and the multi-beam sonar to obtain the multi-beam scanning signal and the side scanning signal. The second filtering signal module 200 is used to Kalman filter the multi-beam scanning signal and the side scanning signal to obtain the first filtering signal, calculate the adaptive filtering threshold, and filter the first filtering signal through the adaptive filtering threshold to obtain the second filtering signal. ​The first terrain signal module 300 is configured to calculate an edge weight using the second filtered signal, construct a minimum energy function by the edge weight and the second filtered signal, obtain an optimized filtered signal by minimizing the energy function, and obtain the first terrain signal by iterating the optimized filtered signal. The second terrain signal module 400 is configured to obtain a generator and a discriminator, construct a generative adversarial network using the generator and the discriminator, perform terrain reconstruction on the first terrain signal by the generative adversarial network, and obtain the second terrain signal. The underwater terrain scanning map module 500 is configured to obtain a sound velocity measuring device, correct the second terrain signal by the sound velocity measuring device to obtain a target terrain signal, and construct an underwater terrain scanning map according to the target terrain signal.

[0045] Figure 4 An example of an entity structure diagram of an electronic device is shown in FIG. 8. Figure 4 As shown in FIG. 8, the electronic device can include a processor 810, a communications interface 820, a memory 830, and a communications bus 840, wherein the processor 810, the communications interface 820, and the memory 830 can communicate with each other through the communications bus 840. The processor 810 can invoke a computer program in the memory 830 to execute an unmanned surface vehicle multi-beam sonar detection method, which includes the following steps: S1: Obtain a side scan sonar and a multi-beam sonar, use the side scan sonar and the multi-beam sonar to scan the seabed terrain, and obtain a multi-beam scanning signal and a side scanning signal; S2: Perform Kalman filtering on the multi-beam scanning signal and the side scanning signal to obtain a first filtered signal, calculate an adaptive filtering threshold, and filter the first filtered signal by the adaptive filtering threshold to obtain a second filtered signal; S3: Calculate an edge weight using the second filtered signal, construct a minimum energy function by the edge weight and the second filtered signal, obtain an optimized filtered signal by minimizing the energy function, and obtain the first terrain signal by iterating the optimized filtered signal; S4: Obtain a generator and a discriminator, construct a generative adversarial network using the generator and the discriminator, perform terrain reconstruction on the first terrain signal by the generative adversarial network, and obtain the second terrain signal; S5: Obtain a sound velocity measuring device, correct the second terrain signal by the sound velocity measuring device to obtain a target terrain signal, and construct an underwater terrain scanning map according to the target terrain signal.

[0046] Furthermore, the computer program in the aforementioned memory 830 can be implemented as a software functional unit and, when sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0047] On the other hand, the present invention further provides a computer program product, comprising a computer program stored on a non-transitory computer-readable storage medium, wherein the computer program comprises program instructions. When the program instructions are executed by a computer, the computer is capable of performing the unmanned boat multi-beam sonar detection method provided by the above methods, the method comprising: S1: Acquire side scan sonar and multi-beam sonar, use side scan sonar and multi-beam sonar to scan the seabed topography, and obtain multi-beam scanning signals and side scan signals; S2: performing Kalman filtering on the multi-beam scanning signal and the side scanning signal to obtain a first filtered signal, calculating an adaptive filtering threshold, and filtering the first filtered signal using the adaptive filtering threshold to obtain a second filtered signal; S3: Calculate edge weights using the second filtered signal, construct a minimization energy function using the edge weights and the second filtered signal, obtain an optimized filtered signal by minimizing the energy function, and iterate the optimized filtered signal to obtain a first terrain signal; S4: Obtain a generator and a discriminator, use the generator and the discriminator to construct a generative adversarial network, and perform terrain reconstruction on the first terrain signal through the generative adversarial network to obtain a second terrain signal; S5: Acquire a sound velocity measurement device, correct the second terrain signal using the sound velocity measurement device to obtain a target terrain signal, and construct an underwater terrain scanning map based on the target terrain signal.

[0048] In another aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the above-mentioned unmanned boat multi-beam sonar detection method, the method comprising: S1: acquire a side scan sonar and a multi-beam sonar, scan a seabed terrain using the side scan sonar and the multi-beam sonar, and obtain a multi-beam scanning signal and a side scanning signal; S2: Kalman filter the multi-beam scanning signal and the side scanning signal to obtain a first filtered signal, calculate an adaptive filtering threshold, filter the first filtered signal through the adaptive filtering threshold to obtain a second filtered signal; S3: calculate an edge weight using the second filtered signal, construct a minimum energy function through the edge weight and the second filtered signal, obtain an optimized filtered signal through the minimum energy function, and iterate the optimized filtered signal to obtain a first terrain signal; S4: acquire a generator and a discriminator, construct a generative adversarial network using the generator and the discriminator, perform terrain reconstruction on the first terrain signal through the generative adversarial network to obtain a second terrain signal; S5: acquire a sound velocity measuring device, correct the second terrain signal through the sound velocity measuring device to obtain a target terrain signal, and construct an underwater terrain scanning map according to the target terrain signal.

[0049] The apparatus embodiments described above are merely illustrative, wherein the units described as separate components can or can not be physically separate, and the components displayed as units can or can not be physical units, i.e., can be located in one place, or can be distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the embodiment scheme according to actual needs. Those skilled in the art can understand and implement without creative labor.

[0050] From the above description of the embodiments, those skilled in the art can clearly understand that the embodiments can be realized by means of software plus necessary general hardware platforms, and of course can also be realized by hardware. Based on such understanding, the above technical solutions can be embodied in the form of software products, which can be stored in a computer readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and include a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods described in each embodiment or some parts of the embodiments.

[0051] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. The multi-beam sonar detection method for unmanned boats is characterized by: include: S1: Acquire side scan sonar and multi-beam sonar, use side scan sonar and multi-beam sonar to scan the seabed topography, and obtain multi-beam scanning signals and side scan signals; S2: performing Kalman filtering on the multi-beam scanning signal and the side scanning signal to obtain a first filtered signal, calculating an adaptive filtering threshold, and filtering the first filtered signal using the adaptive filtering threshold to obtain a second filtered signal; S3: Calculate edge weights using the second filtered signal, construct a minimization energy function using the edge weights and the second filtered signal, obtain an optimized filtered signal by minimizing the energy function, and iterate the optimized filtered signal to obtain a first terrain signal; S4: Obtain a generator and a discriminator, use the generator and the discriminator to construct a generative adversarial network, and perform terrain reconstruction on the first terrain signal through the generative adversarial network to obtain a second terrain signal; S5: Acquire a sound velocity measurement device, correct the second terrain signal using the sound velocity measurement device to obtain a target terrain signal, and construct an underwater terrain scanning map based on the target terrain signal.

2. The unmanned boat multi-beam sonar detection method according to claim 1, characterized in that: In step S1, a side scan sonar and a multi-beam sonar are obtained, the side scan sonar is placed into water by towing a bracket, the multi-beam sonar is used to scan to obtain a multi-beam signal, and the side scan sonar is used to scan to obtain a side scan signal.

3. The unmanned boat multi-beam sonar detection method according to claim 1, characterized in that: In step S2, after obtaining the first filtered signal, an adaptive coefficient is determined, a local scan signal is obtained from the first filtered signal, a local standard deviation of the local scan signal is calculated, and the adaptive filtering threshold is calculated based on the local standard deviation and the adaptive coefficient.

4. The unmanned boat multi-beam sonar detection method according to claim 1, characterized in that: Step S3 further comprises: S31: Determine a scale parameter, and calculate edge weights using the scale parameter and the second filtered signal; S32: determining a regularization parameter, and constructing the minimization energy function by using the regularization parameter, the second filtered signal, and the edge weight; S33: Determine the iteration stop condition, select an optimization algorithm, substitute the minimized energy function into the optimization algorithm to obtain the optimized filtered signal and iterate the optimized filtered signal until the iteration stop condition is met, and use the optimized filtered signal after iteration as the first terrain signal.

5. The unmanned boat multi-beam sonar detection method according to claim 1, characterized in that: In step S4, the noise distribution of the first terrain signal is determined, the first terrain signal distribution is determined based on the first terrain signal, a generative adversarial network is constructed using the noise distribution, the first terrain signal distribution, the generator and the discriminator, and the generator and the discriminator are updated. The first terrain signal is reconstructed by the updated generator to obtain the second terrain signal.

6. The unmanned boat multi-beam sonar detection method according to claim 1, characterized in that: Step S5 further comprises: S51: obtaining a sound velocity measurement device including a sound velocity profiler and a surface sound velocity meter, installing the sound velocity profiler at the installation location of the multi-beam sonar, and hanging the surface sound velocity meter in the water; S52: Obtain a first correction parameter through the sound velocity profiler, obtain a second correction parameter through the surface sound velocity meter, correct the second terrain signal according to the first correction parameter and the second correction parameter to obtain a target terrain signal, and construct an underwater terrain scanning map according to the target terrain signal.

7. An unmanned boat multi-beam sonar detection system, configured to execute the unmanned boat multi-beam sonar detection method according to any one of claims 1 to 6, characterized in that: include: Sonar scanning module: used to obtain side scan sonar and multi-beam sonar, use side scan sonar and multi-beam sonar to scan the seabed terrain and obtain multi-beam scanning signals and side scan signals; A second filtering signal module is configured to perform Kalman filtering on the multi-beam scanning signal and the side scanning signal to obtain a first filtered signal, calculate an adaptive filtering threshold, and filter the first filtered signal using the adaptive filtering threshold to obtain a second filtered signal; First terrain signal module: used to calculate edge weights using the second filtered signal, construct a minimization energy function using the edge weights and the second filtered signal, obtain an optimized filtered signal by minimizing the energy function, and iterate the optimized filtered signal to obtain a first terrain signal; Second terrain signal module: used to obtain a generator and a discriminator, use the generator and the discriminator to build a generative adversarial network, and use the generative adversarial network to reconstruct the terrain of the first terrain signal to obtain a second terrain signal; Underwater terrain scanning map module: used to obtain a sound velocity measurement device, correct the second terrain signal through the sound velocity measurement device, obtain a target terrain signal, and construct an underwater terrain scanning map based on the target terrain signal.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the unmanned boat multi-beam sonar detection method according to any one of claims 1 to 6 are implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the unmanned boat multi-beam sonar detection method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, characterized in that: When the program instructions are executed by a computer, the computer can perform the steps of the unmanned boat multi-beam sonar detection method as described in any one of claims 1 to 6.

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