Underwater target detection device and method

Abstract

The invention provides an underwater target detection device and method, relates to the technical field of target detection, and is used for realizing distance dimension and azimuth dimension synchronous high-resolution processing. The device comprises a sonar probe and a rotating shaft. The sonar probe rotates at a constant speed on the horizontal plane around the rotating shaft; the sonar probe comprises an acoustic array and a target imaging module; the transmitting array comprises a transmitting transducer, and the transmitting transducer transmits single-frequency pulse signals according to a preset period. The receiving array adopts a sparse array arrangement mode, the receiving array collects echo signals to obtain multi-channel array digital signals, orthogonal demodulation and down-sampling processing are carried out on the multi-channel array digital signals, and a multi-channel receiving sequence is obtained. The target imaging module carries out calculation according to a Capon algorithm to obtain an orientation estimation result of each segmented sequence; and combining the azimuth estimation results of the plurality of segmented sequences in each period to obtain sector data corresponding to each period, and combining the sector data of each period in the rotary scanning process to obtain an underwater target imaging result.

Description

Technical Field

[0001] The present application relates to the field of target detection technology, and in particular to an underwater target detection device and method. Background Art

[0002] With the growth of business demands such as underwater construction in muddy waters and oil and gas recovery, there is an urgent need for high-resolution imaging sonar systems to detect underwater targets. In related technologies, multi-beam imaging sonar uses linear array or planar array reception. For example, forward-looking imaging sonar uses echo processing to give a two-dimensional image of the shape and contour of underwater targets. Three-dimensional sonar uses a planar array to receive echo signals, and achieves vertical and horizontal azimuth resolution through two-dimensional beamforming to obtain a three-dimensional image. However, the imaging resolution of such products that use wide-angle transmission and conventional beamforming methods is limited by the aperture of the receiving array.

[0003] For example, when observing underwater oil tree holes, it is necessary to observe and locate the 43 mm diameter jack on the operating panel at a distance of 4-5 meters. In order to meet the requirements for imaging resolution, higher frequencies and larger acoustic apertures are needed to achieve high-resolution underwater observations, but the cost of sonar systems is much higher than the cost of underwater exploration by divers.

[0004] In related technologies, the azimuth estimation performance of the array can also be improved through synthetic aperture methods and high-resolution azimuth estimation methods. The synthetic aperture method requires precise position compensation and is limited by the application scenario, making it difficult to deploy in a narrow space; the high-resolution azimuth estimation algorithm has been extensively studied and applied in the field of passive sonar. In actual engineering applications, this type of high-resolution method is affected by array errors and channel distortion, resulting in poor robustness.

[0005] Since active sonar images include range and azimuth dimensions, increasing the number of stationary snapshots of echoes in the range dimension is contradictory to range resolution. In order to achieve a certain range resolution, the number of stationary snapshots of the echo sequence is usually insufficient to support covariance estimation, resulting in poor high-resolution azimuth estimation performance of active sonar. Secondly, active sonar requires covariance estimation and inversion / eigenvalue decomposition for each range unit. When the number of array elements is high, the amount of calculation is far greater than that of passive sonar high-resolution processing under the same array element conditions. Summary of the invention

[0006] The present application proposes an underwater target detection device and method, which realizes synchronous high-resolution processing of distance dimension and azimuth dimension, improves imaging calculation efficiency, and stabilizes imaging performance.

[0007] In order to achieve the above purpose, this application adopts the following technical solutions: In the first aspect, an underwater target detection device is provided, which includes a sonar probe and a rotating shaft. The sonar probe rotates at a uniform speed on a horizontal plane around the rotating shaft; the sonar probe includes an acoustic array and a target imaging module, and the acoustic array includes a transmitting array and a receiving array; the transmitting array includes a transmitting transducer, and the transmitting transducer transmits a single-frequency pulse signal according to a preset period; the receiving array adopts a sparse array method, the receiving array collects echo signals, obtains a multi-channel array digital signal, and performs orthogonal demodulation and downsampling processing on the multi-channel array digital signal to obtain a multi-channel receiving sequence; the target imaging module segments the multi-channel receiving sequence according to the signal receiving time to obtain multiple segmented sequences, and calculates the azimuth estimation result of each segmented sequence according to the Capon algorithm; the target imaging module combines the azimuth estimation results of the multiple segmented sequences in each period to obtain the sector data corresponding to each period, and combines the sector data of each period in the rotating scanning process to obtain the underwater target imaging result.

[0008] In some embodiments, the beam formed by the single-frequency pulse signal has an opening angle range of 1°-5° in the horizontal direction and 15°-40° in the vertical direction, and the pulse width range of the single-frequency pulse signal is 15-100 microseconds.

[0009] In some embodiments, the angle between the azimuth angle of the first-order grating lobe of the receiving array and the positive horizontal direction of the receiving array is greater than five times the transmission sector angle, and the transmission sector angle is the angle of the beam formed by the single-frequency pulse signal in the horizontal direction.

[0010] In the second aspect, an underwater target detection method is provided, which is applied to the underwater target detection device proposed in the first aspect. The method includes: a transmitting transducer transmits a single-frequency pulse signal according to a preset period. The receiving array collects echo signals to obtain a multi-channel array digital signal, and performs orthogonal demodulation and downsampling processing on the multi-channel array digital signal to obtain a multi-channel receiving sequence. The multi-channel receiving sequence is segmented according to the signal receiving time to obtain multiple segmented sequences; the azimuth estimation result of each segmented sequence is calculated according to the Capon algorithm; the azimuth estimation results of the multiple segmented sequences in each period are combined to obtain the sector data corresponding to each period; the sector data of each period in the rotation scanning process is combined to obtain the underwater target imaging result.

[0011] In some embodiments, the baseband form of the signal in the multi-channel receiving sequence is ;in, Indicates The received time domain echo signal of the pulse corresponding to the array element, is the array element number of the receiving array element, is the time domain sampling point.

[0012] In some embodiments, the above-mentioned segmenting the multi-channel receiving sequence according to the signal receiving time to obtain multiple segmented sequences includes: sorting the signals in the multi-channel receiving sequence according to the signal receiving time; segmenting the multi-channel receiving sequence based on a preset number of segmented signals and a signal overlap ratio to obtain multiple segmented sequences, and the ratio of the number of repeated signals between any two adjacent segmented sequences in the multiple segmented sequences to the number of signals included in the segmented sequences is the signal overlap ratio.

[0013] In some embodiments, the above-mentioned calculation according to the Capon algorithm to obtain the azimuth estimation result of each segmented sequence includes: for each segmented sequence, performing covariance estimation on the segmented sequence to obtain the covariance matrix corresponding to the segmented sequence; based on the average energy of the signal in the segmented sequence, diagonally loading the covariance matrix to obtain the loaded covariance matrix; calculating the inverse of the loaded covariance matrix according to the LDLT method; calculating the steering vector of the segmented sequence according to the distance corresponding to the segmented sequence; based on the inverse of the loaded covariance matrix and the steering vector, using the Capon algorithm to calculate the azimuth estimation result of the segmented sequence.

[0014] In some embodiments, the above-mentioned calculation of the steering vector of the segmented sequence according to the distance corresponding to the segmented sequence includes: determining the judgment distance according to the sonar aperture of the sonar probe and the wavelength of the single-frequency pulse signal; when the distance corresponding to the segmented sequence is greater than or equal to the judgment distance, calculating the steering vector of the segmented sequence based on the far-field steering vector calculation formula; when the distance corresponding to the segmented sequence is less than the judgment distance, calculating the steering vector of the segmented sequence based on the near-field steering vector calculation formula.

[0015] In some embodiments, the above far-field steering vector calculation formula is: The calculation formula of the near-field steering vector is: in, For the The distance between an array element and the origin of the array, is the speed of sound, is the signal frequency, is the guiding angle, is the distance between the focus point and the array origin, is the distance between the focus point and each array element.

[0016] In some embodiments, the above-mentioned sector data of each cycle in the rotational scanning process are combined to obtain underwater target imaging results, including: performing average filtering processing on the overlapping areas of the sector data of adjacent cycles in multiple cycles; performing bilinear interpolation processing on the sector data of each adjacent preset number of cycles; and combining the processed sector data of each cycle to obtain underwater target imaging results.

[0017] The underwater target detection device and method provided by the present application have the following outstanding advantages and effects when performing underwater target detection. First, the receiving array in the sonar probe adopts a sparse array method, the hardware circuit is simple, and the system integration cost is low; second, the order of the covariance matrix is ​​low, generally 8-24 orders, the complexity of matrix inversion is small, and the calculation cost of the active high-resolution imaging method is low; third, the horizontal angle of the transmitting beam is small, the spatial range corresponding to the covariance matrix estimation is small, and the number of signal snapshots required for estimation is also small, thus ensuring the distance resolution capability; fourth, the diagonal loading value refers to the average energy of the segmented sequence, because the array receives signals from a narrow angle spatial range, and the average energy of each orientation is different, thus having an adaptive diagonal loading amount, ensuring the stability of the target imaging quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A schematic diagram of the structure of an underwater target detection device provided in an embodiment of the present application; Figure 2 A schematic diagram of an underwater target detection method provided in an embodiment of the present application Figure 1 ; Figure 3 A schematic diagram of an underwater target detection method provided in an embodiment of the present application Figure 2 ; Figure 4 A schematic diagram of an underwater target detection method provided in an embodiment of the present application Figure 3 ; Figure 5 A conventional beamforming simulation effect diagram provided for an embodiment of the present application; Figure 6 A simulation effect diagram of an underwater target detection method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application.

[0020] In the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0021] In the description of this application, unless otherwise specified, " / " means "or", for example, A / B can mean A or B. "And / or" in this article is merely a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, "at least one" and "plurality" refer to two or more. The words "first", "second", etc. do not limit the quantity and execution order, and the words "first", "second", etc. do not limit them to be different.

[0022] The present application provides an underwater target detection device, which transmits a horizontal narrow beam to illuminate a small angle area and obtains an image with a large horizontal field of view by mechanical scanning. The receiving array adopts a sparse array method, which greatly reduces the number of receiving array elements under the same aperture conditions. In this way, the order of the covariance matrix is ​​greatly reduced, and the number of snapshots required for covariance estimation is also reduced, which greatly reduces the amount of calculation. Based on the Capon algorithm, high-resolution processing is performed for small angle areas. Under adverse conditions, the angle resolution results are also constrained in this small angle area, which enhances the robustness of imaging.

[0023] Figure 1 1 is a schematic diagram of the structure of an exemplary underwater target detection device, wherein the underwater target detection device 100 includes a sonar probe 110 and a rotating shaft 120, and the sonar probe 110 includes an acoustic array and a target imaging module.

[0024] During the operation of the underwater target detection device, the sonar probe 110 rotates at a uniform speed around the rotation axis 120. The acoustic base station includes a transmitting array and a receiving array, wherein the transmitting array includes a transmitting transducer, and the transmitting transducer is used to transmit a single-frequency pulse signal according to a preset period; the receiving array adopts a sparse array method (for example, 8-24 uniformly distributed linear arrays can be used) to collect echo signals, obtain multi-channel array digital signals, and perform orthogonal demodulation and downsampling processing on the multi-channel array digital signals to obtain a multi-channel receiving sequence. The target imaging module is used to segment the multi-channel receiving sequence according to the signal receiving time to obtain multiple segmented sequences, and calculate the azimuth estimation result of each segmented sequence according to the Capon algorithm; further, the target imaging module combines the azimuth estimation results of the multiple segmented sequences in each period to obtain the sector data corresponding to each period, and combines the sector data of each period in the rotating scanning process to obtain the underwater target imaging result.

[0025] like Figure 1 As shown, the acoustic base station includes a transmitting array 111 and a receiving array 112 .

[0026] Among them, a transmitting transducer is deployed in the transmitting array 111. The beam formed by the single-frequency pulse signal sent by the transmitting transducer has an opening angle range of 1°-5° in the horizontal direction and an opening angle range of 15°-40° in the vertical direction. The pulse width range of the single-frequency pulse signal is 15-100 microseconds.

[0027] The angle between the azimuth angle of the first-order grating lobe of the receiving array 112 and the positive horizontal direction of the receiving array is greater than five times the transmission sector angle. The transmission sector angle is the angle of the beam formed by the single-frequency pulse signal in the horizontal direction.

[0028] In some embodiments, the rotational angular velocity of the sonar probe 110 rotating uniformly around the rotation axis 120 can be determined according to the detection range, the beam opening angle, and the inter-frame repetition area. For example, when the detection range is 15 meters, the single signal transmission and reception time is about 20 ms, the signal transmission frequency can reach up to 40 Hz, the beam opening angle is 2°, and the repetition angle of the inter-frame repetition area is 1°, the maximum rotational angular velocity is 40° / s.

[0029] In some application examples, in the underwater target detection device 100 provided in the embodiment of the present application, the transmitting array 111 in the sonar probe 110 is deployed with one transmitting transducer, and the receiving array 112 is deployed with a 16-element linear receiving array. The horizontal beam width of the beam transmitted by the transmitting transducer is 2.5°, the vertical beam width is 20°, the frequency of the transmitted single-frequency pulse signal is 1 MHz, the pulse width of the transmitted pulse is 50 microseconds, and the corresponding distance resolution is about 3.75 cm.

[0030] During the scanning and imaging process, the acoustic array plane is 1-3 meters away from the water surface and tilted downward by 30 degrees to avoid interference from the water surface echo signal. The sonar probe 110 rotates around the rotation axis 120 at an angular velocity of 15° / s. The area repetition between the two frames before and after the scan is 10%, and the detection range is 6-30 meters. The transmitting array 111 transmits a 1MHz single-frequency short pulse signal, and the receiving array 112 collects the echo signal, samples it after pre-filtering and amplification, and further performs imaging processing based on the obtained multi-channel receiving sequence.

[0031] It should be noted that the underwater target detection device provided in the embodiment of the present application is used for underwater target detection, which has the following outstanding advantages and effects. First, the receiving array in the sonar probe adopts a sparse array method, the hardware circuit is simple, and the system integration cost is low; second, the order of the covariance matrix is ​​relatively low, generally 8-24 orders, the complexity of matrix inversion is small, and the computational cost of the active high-resolution imaging method is low; third, the horizontal angle of the transmitting beam is small, the spatial range corresponding to the covariance matrix estimation is small, and the number of signal snapshots required for estimation is also small, thus ensuring the distance resolution capability; fourth, the diagonal loading value refers to the average energy of the segmented sequence, because the array receives signals from a narrow angle spatial range, and the average energy of each orientation is different, thus having an adaptive diagonal loading amount, ensuring the stability of the target imaging quality.

[0032] Based on the underwater target detection device provided in the above-mentioned embodiment, the embodiment of the present application also provides an underwater target detection method, which can reduce the computing cost and improve the imaging efficiency and resolution.

[0033] Specifically, Figure 2-3 As shown, the underwater target detection method provided in the embodiment of the present application includes the following steps S1-S6.

[0034] S1. The transmitting transducer transmits a single-frequency pulse signal according to a preset period.

[0035] It should be noted that the preset period, beam width, pulse width and frequency of the single-frequency pulse signal emitted by the transmitting transducer can be set in advance by the operation and maintenance personnel, and the embodiments of the present application do not specifically limit this.

[0036] For example, the preset period can be set to 20ms, and the beam formed by the single-frequency pulse signal has an opening angle range of 1°-5° in the horizontal direction, and can be set to 2° in an exemplary manner; the opening angle range in the vertical direction is 15°-40°, and can be set to 20° in an exemplary manner; the pulse width range of the single-frequency pulse signal is 15-100 microseconds, and can be set to 50 microseconds in an exemplary manner; the frequency can be set to 1MHz.

[0037] S2. The receiving array collects the echo signal to obtain a multi-channel array digital signal, and performs orthogonal demodulation and downsampling processing on the multi-channel array digital signal to obtain a multi-channel receiving sequence.

[0038] In order to reduce the complexity of data processing, the multi-channel array digital signal is orthogonally demodulated according to the center frequency of the single-frequency pulse signal. After downsampling, a multi-channel receiving sequence is obtained, where the baseband form of the signal in the multi-channel receiving sequence is shown in the following formula: ; in, Indicates The received time domain echo signal of the pulse corresponding to the array element, is the array element number of the receiving array element, is the time domain sampling point.

[0039] For example, taking a single-frequency pulse signal with a center frequency of 1 MHz and a pulse width of 50 microseconds as an example, if the echo signal is downsampled and the sampling rate is reduced to 250 kHz, then a 50-microsecond signal has 50 / 4=12.5 sampling points at a sampling rate of 250 kHz.

[0040] S3. Segment the multi-channel receiving sequence according to the signal receiving time to obtain multiple segmented sequences.

[0041] Specifically, they include: S31. Sort the signals in the multi-channel receiving sequence according to the signal receiving time.

[0042] The signal receiving time is the acquisition time of the echo signal collected by the receiving array.

[0043] S32. Segment the multi-channel receiving sequence based on the preset number of segmented signals and the signal overlap ratio to obtain a plurality of segmented sequences.

[0044] The ratio of the number of repeated signals between any two adjacent segmented sequences in the multiple segmented sequences to the number of signals included in the segmented sequences is the signal overlap ratio.

[0045] Exemplarily, if the multi-channel receiving sequence includes 48 signals, the preset number of segmented signals is 16, and the signal overlap ratio is 50%, the obtained multiple segmented sequences are [1-16], [9-24], [16-32], [24-40], and [32-48] respectively. It should be noted that the above example is only used to introduce the method of segmenting a multi-channel receiving sequence, and does not constitute a limitation on the number of signals in the multi-channel receiving sequence. In practical applications, a multi-channel receiving sequence usually includes tens of thousands of signals. When segmenting a multi-channel receiving sequence, the method shown in the above example can be followed to segment according to the preset number of segmented signals and the signal overlap ratio. The embodiment of the present application does not specifically limit this.

[0046] S4. Calculate the azimuth estimation result of each segment sequence according to the Capon algorithm.

[0047] Specifically, for each segment sequence, the method of using the Capon algorithm to calculate the direction estimation result is as follows: Figure 3 As shown, the following steps S41-S45 are included.

[0048] S41. Estimating the covariance of the segmented sequence to obtain a covariance matrix corresponding to the segmented sequence.

[0049] As a possible implementation manner, based on the signal included in the segmented sequence, a preset covariance estimation formula is used to perform calculations to obtain a covariance matrix corresponding to the segmented sequence.

[0050] In some embodiments, the preset covariance estimation formula is: in, is the covariance matrix corresponding to the segmented sequence, M is the number of signal snapshots, and in the embodiment of the present application, the pulse width of the single-frequency pulse signal is taken; The first A signal.

[0051] S42. Based on the average energy of the signal in the segmented sequence, multiple covariance matrices are diagonally loaded to obtain a loaded covariance matrix.

[0052] As a possible implementation, firstly calculate the average energy of the signal in the segmented sequence, and diagonally load the covariance matrix obtained in the above step S41 based on the calculated average energy to obtain a stable covariance matrix, that is, the loaded covariance matrix.

[0053] In some embodiments, the average energy of the signal in the segment sequence can be calculated using the following formula: .

[0054] in, is the number of snapshots, is the number of receiving array elements, Refers to each signal point in each channel. For example, is the first signal point of the first channel, It is the second signal point of the first channel.

[0055] Furthermore, the average energy corresponding to the segmented sequence is calculated Afterwards, the covariance matrix is ​​calculated based on the average energy The following formula can be used for diagonal loading.

[0056] in, The value range is 0.01~0.1, To stabilize the covariance matrix, that is, the covariance matrix after loading, is the covariance matrix corresponding to the segmented sequence, is the average energy corresponding to the segmented sequence, is the identity matrix.

[0057] S43. Calculate the inverse of the loaded covariance matrix according to the LDLT method.

[0058] Among them, the covariance matrix decomposition formula is as follows: The specific calculation formulas for each element are as follows: , , , .

[0059] Then the inverse of the stable covariance matrix is It should be noted that the inverse process only requires the inverse of a lower triangular matrix and the reciprocal of a diagonal matrix.

[0060] Among them, the lower triangular matrix can be solved by the forward substitution method: Assumptions , where Y is also a lower triangular matrix: The diagonal elements remain unchanged. The angle scalar The off-diagonal elements of .

[0061] Calculated one by one The matrix returned This is the lower triangular matrix The inverse matrix of .

[0062] It is understandable that the LDLT method does not require the calculation of square roots, but only relies on linear operations, which naturally matches the pipeline architecture of FPGA, significantly improving the computing efficiency while reducing the complexity of hardware implementation. Its parameterized kernel design supports dynamic order configuration and flexibly adapts to the inversion requirements of matrices of different orders.

[0063] S44. Calculate the guidance vector of the segmented sequence according to the distance corresponding to the segmented sequence.

[0064] Specifically, the steps include: S441. Determine the distance according to the sonar aperture of the sonar probe and the wavelength of the single-frequency pulse signal.

[0065] Determine distance The calculation formula is as follows: in, is the sonar aperture, is the wavelength of the single-frequency pulse signal.

[0066] The determination distance is used to determine whether the signal in the segment sequence is a far-field signal or a near-field signal. When the corresponding distance of the segment sequence is greater than or equal to the determination distance, the signal in the segment sequence is a far-field signal.

[0067] S442. When the distance corresponding to the segmented sequence is greater than or equal to the determination distance, the steering vector of the segmented sequence is calculated based on the far-field steering vector calculation formula.

[0068] In some embodiments, the far-field steering vector calculation formula is as follows: in, For the The distance between an array element and the array origin.

[0069] S443. When the distance corresponding to the segmented sequence is less than the determination distance, the steering vector of the segmented sequence is calculated based on the near-field steering vector calculation formula.

[0070] In some embodiments, the near-field steering vector calculation formula is as follows: in, is the speed of sound, is the signal frequency, is the guiding angle, is the distance between the focus point and the array origin, is the distance between the focus point and each array element.

[0071] In some embodiments, the determined distance may be pre-calculated and stored in the underwater target detection device. For example, when the sonar aperture is 150 mm and the wavelength of the single-frequency pulse signal is 1.5 mm, the determined distance is 15 meters.

[0072] When the detection range is 24 meters and the signal frequency is 250kHz after downsampling, the signal sequence is 8000 points long. The signal is segmented into 12 points, and the number of step points is half of the signal length (6 points). At this time, there are 1332 segment sequences in total. The scanning angle is defined as -1°~1°, with an interval of 0.2°. The storage capacity of the guidance vector is 11*16*16*1332≈3.57MB.

[0073] S45. Based on the inverse of the loaded covariance matrix and the steering vector, the Capon algorithm is used to calculate the azimuth estimation result of the segmented sequence.

[0074] In some embodiments, the calculation formula of the position estimation result is as follows: S5. Combining the azimuth estimation results of multiple segmented sequences in each period to obtain the sector data corresponding to each period.

[0075] S6. Combining the sector data of each cycle in the rotation scanning process to obtain underwater target imaging results.

[0076] S61, performing average filtering processing on the overlapping areas of the sector data of adjacent periods in multiple periods.

[0077] S62, performing bilinear interpolation processing on the sector data of each adjacent preset number of cycles.

[0078] The preset number is pre-set by the operation and maintenance personnel in the underwater target detection device, for example, it can be set to 5, that is, bilinear interpolation processing is performed on every 5 adjacent sector data.

[0079] S63, combining the processed sector data of each cycle to obtain underwater target imaging results.

[0080] In some embodiments, Figure 4 A processing flow diagram of an underwater target imaging method is shown. First, the receiving array performs orthogonal adjustment and downsampling processing on the collected echo signals; further, the multi-channel receiving sequence is segmented to obtain multiple segmented sequences, and the azimuth estimation result is calculated for each segmented sequence, and the sector data corresponding to each cycle is combined, and then the overlapping areas in the adjacent sector data are averaged and filtered to obtain the processed sector data. Finally, multiple sector data are combined to obtain the imaging result of the underwater target.

[0081] In some embodiments, Figure 5 A conventional beamforming simulation effect diagram is shown. Figure 6 A simulation effect diagram of an underwater target detection method provided by an embodiment of the present application is shown. Compared with conventional beamforming methods, the underwater target detection method provided by the embodiment of the present application has higher resolution and more accurate imaging results.

[0082] In some embodiments, the underwater target detection method provided in the embodiments of the present application can also be applied to sonar probes with multiple transmitting transducers and multi-band combined transmission modes, and the embodiments of the present application do not specifically limit this.

[0083] In some embodiments, in the underwater target detection method provided in the embodiments of the present application, high-resolution processing methods such as MUSIC and ESPRIT can also be used to replace the Capon algorithm to perform underwater target detection and imaging processing. The embodiments of the present application do not make specific limitations on this.

[0084] An embodiment of the present application further provides a computer-readable storage medium, which includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute the steps of the above-mentioned underwater target detection method.

[0085] The beneficial effects of the computer-readable storage medium of the present application are equivalent to the beneficial effects of the above-mentioned underwater target detection method, and will not be elaborated here.

[0086] The present application is operable with numerous general purpose or special purpose computer system environments or configurations.

[0087] For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics devices, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, etc.

[0088] The present application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer.

[0089] Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The present application may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communications network.

[0090] In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.

[0091] Specifically, a person skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing related hardware through computer-readable instructions, and the computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the aforementioned storage medium can be a non-volatile storage medium such as a disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0092] It should be understood that although the steps in the flowchart of the accompanying drawings are shown in sequence according to the instructions of the arrows, these steps are not necessarily performed in the order indicated by the arrows. Unless otherwise clearly stated in this document, the execution of these steps is not strictly limited in order and can be performed in other orders.

[0093] Moreover, at least part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The order of execution is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0094] Obviously, the embodiments described above are only some embodiments of the present application, rather than all embodiments. The drawings provide preferred embodiments of the present application, but do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive.

[0095] Although the present application is described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned specific embodiments, or replace some of the technical features therein with equivalents. Any equivalent structure made using the contents of the present application specification and drawings, directly or indirectly used in other related technical fields, is also within the scope of patent protection of this application.

Claims

1. An underwater target detection device, characterized in that: Including sonar probe and rotating shaft; The sonar probe performs uniform rotational motion on a horizontal plane around the rotation axis; The sonar probe includes an acoustic array and a target imaging module, and the acoustic array includes a transmitting array and a receiving array; The transmitting array includes a transmitting transducer, and the transmitting transducer transmits a single-frequency pulse signal according to a preset period; The receiving array adopts a sparse array mode, the receiving array collects echo signals to obtain multi-channel array digital signals, and performs orthogonal demodulation and downsampling processing on the multi-channel array digital signals to obtain a multi-channel receiving sequence; The target imaging module segments the multi-channel receiving sequence according to the signal receiving time to obtain a plurality of segmented sequences, and calculates the azimuth estimation result of each segmented sequence according to the Capon algorithm; The target imaging module combines the azimuth estimation results of multiple segmented sequences in each cycle to obtain the sector data corresponding to each cycle, and combines the sector data of each cycle in the rotation scanning process to obtain the underwater target imaging result.

2. The underwater target detection device according to claim 1, characterized in that: The beam formed by the single-frequency pulse signal has an opening angle range of 1°-5° in the horizontal direction and a vertical opening angle range of 15°-40°, and the pulse width range of the single-frequency pulse signal is 15-100 microseconds.

3. The underwater target detection device according to claim 1, characterized in that: The angle between the azimuth angle of the first-order grating lobe of the receiving array and the positive horizontal direction of the receiving array is greater than five times the transmission sector angle, and the transmission sector angle is the angle of the beam formed by the single-frequency pulse signal in the horizontal direction.

4. A method for underwater target detection, characterized in that: Applied to the underwater target detection device according to any one of claims 1 to 3, the method comprises: The transmitting transducer transmits a single-frequency pulse signal according to a preset period; The receiving array collects the echo signal to obtain a multi-channel array digital signal, and performs orthogonal demodulation and downsampling processing on the multi-channel array digital signal to obtain a multi-channel receiving sequence; Segmenting the multi-channel receiving sequence according to the signal receiving time to obtain a plurality of segmented sequences; The azimuth estimation result of each segment sequence is calculated according to the Capon algorithm; Combining the azimuth estimation results of multiple segmented sequences in each period to obtain the sector data corresponding to each period; The sector data of each cycle in the rotating scanning process are combined to obtain the underwater target imaging result.

5. The underwater target detection method according to claim 4, characterized in that: The baseband form of the signal in the multi-channel receiving sequence is ; in, Indicates The received time domain echo signal of the pulse corresponding to the array element, is the array element number of the receiving array element, is the time domain sampling point.

6. The underwater target detection method according to claim 4, characterized in that: The step of segmenting the multi-channel receiving sequence according to the signal receiving time to obtain a plurality of segmented sequences includes: According to the signal receiving time, the signals in the multi-channel receiving sequence are sorted; Based on the preset number of segmented signals and the signal overlap ratio, the multi-channel receiving sequence is segmented to obtain multiple segmented sequences, and the ratio of the number of signals repeated between any two adjacent segmented sequences in the multiple segmented sequences to the number of signals included in the segmented sequences is the signal overlap ratio.

7. The underwater target detection method according to any one of claim 4, characterized in that: The calculation of the azimuth estimation result of each segment sequence according to the Capon algorithm includes: For each segmented sequence, perform covariance estimation on the segmented sequence to obtain a covariance matrix corresponding to the segmented sequence; Based on the average energy of the signal in the segmented sequence, the covariance matrix is ​​diagonally loaded to obtain a loaded covariance matrix; Calculating the inverse of the loaded covariance matrix according to the LDLT method; Calculating a steering vector of the segmented sequence according to the distance corresponding to the segmented sequence; Based on the inverse of the loaded covariance matrix and the steering vector, the Capon algorithm is used to calculate the azimuth estimation result of the segmented sequence.

8. The underwater target detection method according to claim 7, characterized in that: The step of calculating the steering vector of the segmented sequence according to the distance corresponding to the segmented sequence comprises: Determining the distance according to the sonar aperture of the sonar probe and the wavelength of the single-frequency pulse signal; When the distance corresponding to the segment sequence is greater than or equal to the determination distance, calculating the steering vector of the segment sequence based on a far-field steering vector calculation formula; When the distance corresponding to the segmented sequence is smaller than the determination distance, the steering vector of the segmented sequence is calculated based on a near-field steering vector calculation formula.

9. The underwater target detection method according to claim 8, characterized in that: The far-field steering vector calculation formula is: The near-field steering vector calculation formula is: in, For the The distance between an array element and the origin of the array, is the speed of sound, is the signal frequency, is the guiding angle, is the distance between the focus point and the array origin, is the distance between the focus point and each array element.

10. The underwater target detection method according to any one of claim 4, characterized in that: The sector data of each cycle in the rotation scanning process is combined to obtain underwater target imaging results, including: Performing average filtering on the overlapping areas of the sector data of adjacent cycles in multiple cycles; Perform bilinear interpolation processing on the sector data of each adjacent preset number of cycles; The processed sector data of each cycle are combined to obtain the underwater target imaging result.

Images