CN117595943A - Method, system, equipment and medium for rapid backtracking analysis of target characteristic frequency points - Google Patents
Method, system, equipment and medium for rapid backtracking analysis of target characteristic frequency points Download PDFInfo
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Abstract
The application relates to a target characteristic frequency point quick backtracking analysis method, a system, equipment and a medium, wherein the target characteristic frequency point quick backtracking analysis method comprises the following steps: acquiring omnibearing wave beam forming data based on underwater sound signals acquired by a wet end; performing spectrum extraction on the omnibearing wave beam forming data according to a preset time interval to obtain omnibearing LOFAR spectrum data; inquiring time azimuth history data of an all-time range and an all-direction range corresponding to the characteristic frequency points from the all-direction LOFAR frequency spectrum data based on the specified characteristic frequency points, and generating a LOFAR narrow-band azimuth history chart; and comparing the LOFAR narrowband azimuth calendar with a target azimuth calendar, and judging whether the characteristic frequency point belongs to a target characteristic frequency point. The problem of the underwater sound target discovery and the recognition inefficiency is solved, and the underwater sound target discovery and the recognition efficiency is improved.
Description
Technical Field
The application relates to the technical field of underwater acoustic signal data processing, in particular to a target characteristic frequency point quick backtracking analysis method, a system, equipment and a medium.
Background
With the rapid development of underwater acoustics, rapid discovery and identification of underwater acoustic targets is an important requirement for underwater acoustic detection. The characteristic of much interference of the underwater acoustic signal limits the speed of target discovery and identification, and the signal can be judged to belong to the target or interference after comprehensively comparing and analyzing various data characteristics. The rapid provision of data features that determine the type of signal becomes a target for discovery and identification of service pain points that are urgently needed to be addressed.
Aiming at the problem of low efficiency of underwater sound target discovery and identification in the related art, no effective solution is proposed at present.
Disclosure of Invention
Based on this, it is necessary to provide a method, a system, a device and a medium for fast backtracking analysis of target characteristic frequency points according to the above technical problems.
In a first aspect, an embodiment of the present application provides a method for fast traceback analysis of a target feature frequency point, where the method includes:
acquiring omnibearing wave beam forming data based on underwater sound signals acquired by a wet end;
performing spectrum extraction on the omnibearing wave beam forming data according to a preset time interval to obtain omnibearing LOFAR spectrum data;
inquiring time azimuth history data of an all-time range and an all-direction range corresponding to the characteristic frequency points from the all-direction LOFAR frequency spectrum data based on the specified characteristic frequency points, and generating a LOFAR narrow-band azimuth history chart;
and comparing the LOFAR narrowband azimuth calendar with a target azimuth calendar, and judging whether the characteristic frequency point belongs to a target characteristic frequency point.
In one embodiment, the obtaining the omni-directional beam forming data of the underwater acoustic signal based on the underwater acoustic signal acquired by the wet end includes:
receiving underwater sound signals issued by a wet end according to time sequence; the underwater sound signal comprises array element domain data and array depth array direction data of a plurality of sensors;
and accumulating the array element domain data and the array depth array direction data in time sequence, and carrying out omnibearing wave beam formation on the newly accumulated array element domain data and the newly accumulated array depth array direction data every second to obtain omnibearing wave beam formation data of the underwater sound signal.
In one embodiment, comparing the local narrowband azimuth calendar with the target azimuth calendar, and determining whether the characteristic frequency point belongs to the characteristic frequency point of the target includes:
and comparing the LOFAR narrowband azimuth calendar with a target azimuth calendar, and judging whether the characteristic frequency point belongs to the characteristic frequency point of the target based on consistency.
In one embodiment, comparing the local narrowband azimuth calendar with the target azimuth calendar, and determining whether the characteristic frequency point belongs to the characteristic frequency point of the target based on consistency includes:
obtaining similarity based on the LOFAR narrowband azimuth calendar and the target azimuth calendar;
if the similarity is larger than a preset value, judging that the characteristic frequency point belongs to the characteristic frequency point of the target;
and if the similarity is smaller than the preset value, judging that the characteristic frequency point does not belong to the characteristic frequency point of the target.
In one embodiment, the extracting the omni-directional beam forming data according to a preset time interval to obtain a plurality of omni-directional local spectrum data further includes:
each time taking the timestamp of the first second beam forming data of the preset time interval as the data timestamp of the LOFAR spectrum.
In one embodiment, the method further comprises:
and storing each omnibearing LOFAR spectrum data and corresponding data time stamps in a memory according to time sequence.
In one embodiment, the obtaining the omni-directional beam forming data based on the underwater acoustic signal collected by the wet end further includes:
and acquiring underwater sound signals acquired by the wet end, and carrying out noise reduction treatment on the underwater sound signals.
In a second aspect, an embodiment of the present application further provides a target feature frequency point fast backtracking analysis system, where the system includes:
the acquisition module is used for acquiring all-dimensional beam forming data based on the underwater acoustic signals acquired by the wet end;
the extraction module is used for extracting the omnibearing wave beam forming data according to a preset time interval to obtain omnibearing LOFAR frequency spectrum data;
the query module is used for querying the time azimuth history data of the full time range and the full azimuth range corresponding to the characteristic frequency points from the full azimuth LOFAR frequency spectrum data based on the specified characteristic frequency points, and generating a LOFAR narrow-band azimuth history chart;
and the comparison module is used for comparing the LOFAR narrowband azimuth calendar with the target azimuth calendar and judging whether the characteristic frequency point belongs to the characteristic frequency point of the target.
In a third aspect, embodiments of the present application also provide a computer device comprising a memory having a computer program stored therein and a processor arranged to run the computer program to perform the method according to the first aspect described above.
In a fourth aspect, embodiments of the present application further provide a storage medium having a computer program stored therein, where the computer program, when executed by a processor, implements a method as described in the first aspect above.
The target characteristic frequency point rapid backtracking analysis method, system, equipment and medium are used for acquiring omnibearing wave beam forming data based on underwater sound signals acquired by a wet end; performing spectrum extraction on the omnibearing wave beam forming data according to a preset time interval to obtain omnibearing LOFAR spectrum data; inquiring time azimuth history data of an all-time range and an all-direction range corresponding to the characteristic frequency points from the all-direction LOFAR frequency spectrum data based on the specified characteristic frequency points, and generating a LOFAR narrow-band azimuth history chart; and comparing the LOFAR narrowband azimuth calendar with a target azimuth calendar, and judging whether the characteristic frequency point belongs to a target characteristic frequency point. The problem of the underwater sound target discovery and the recognition inefficiency is solved, and the underwater sound target discovery and the recognition efficiency is improved.
The details of one or more embodiments of the application are set forth in the accompanying drawings and the description below to provide a more thorough understanding of the other features, objects, and advantages of the application.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute an undue limitation to the application. In the drawings:
fig. 1 is a hardware structural block diagram of a terminal of a target characteristic frequency point quick backtracking analysis method according to an embodiment of the present application;
fig. 2 is a flow chart of a target characteristic frequency point quick backtracking analysis method according to an embodiment of the present application;
fig. 3 is a flow chart of a target characteristic frequency point quick backtracking analysis method according to a preferred embodiment of the present application;
fig. 4 is a block diagram of a target feature frequency point fast backtracking analysis system according to an embodiment of the present application;
fig. 5 is a schematic diagram of a computer device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described and illustrated below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden on the person of ordinary skill in the art based on the embodiments provided herein, are intended to be within the scope of the present application.
It is apparent that the drawings in the following description are only some examples or embodiments of the present application, and it is possible for those of ordinary skill in the art to apply the present application to other similar situations according to these drawings without inventive effort. Moreover, it should be appreciated that while such a development effort might be complex and lengthy, it would nevertheless be a routine undertaking of design, fabrication, or manufacture for those of ordinary skill having the benefit of this disclosure, and thus should not be construed as having the benefit of this disclosure.
Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is to be expressly and implicitly understood by those of ordinary skill in the art that the embodiments described herein can be combined with other embodiments without conflict.
Unless defined otherwise, technical or scientific terms used herein should be given the ordinary meaning as understood by one of ordinary skill in the art to which this application belongs. Reference to "a," "an," "the," and similar terms herein do not denote a limitation of quantity, but rather denote the singular or plural. The terms "comprising," "including," "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, article, or apparatus that comprises a list of steps or modules (elements) is not limited to only those steps or elements but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus. The terms "connected," "coupled," and the like in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The term "plurality" as used herein refers to two or more. "and/or" describes an association relationship of an association object, meaning that there may be three relationships, e.g., "a and/or B" may mean: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship. The terms "first," "second," "third," and the like, as used herein, are merely distinguishing between similar objects and not representing a particular ordering of objects.
The method embodiments provided in the present embodiment may be executed in a terminal, a computer, or similar computing device. For example, the method runs on a terminal, and fig. 1 is a block diagram of a hardware structure of the terminal of the target characteristic frequency point quick backtracking analysis method of the present embodiment. As shown in fig. 1, the terminal may include one or more (only one is shown in fig. 1) processors 102 and a memory 104 for storing data, wherein the processors 102 may include, but are not limited to, a microprocessor MCU, a programmable logic device FPGA, or the like. The terminal may also include a transmission device 106 for communication functions and an input-output device 108. It will be appreciated by those skilled in the art that the structure shown in fig. 1 is merely illustrative and is not intended to limit the structure of the terminal. For example, the terminal may also include more or fewer components than shown in fig. 1, or have a different configuration than shown in fig. 1.
The memory 104 may be used to store a computer program, for example, a software program of application software and a module, such as a computer program corresponding to the target feature frequency point quick trace back analysis method in the present embodiment, and the processor 102 executes the computer program stored in the memory 104 to perform various functional applications and data processing, that is, implement the method described above. Memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory remotely located relative to the processor 102, which may be connected to the terminal via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission device 106 is used to receive or transmit data via a network. The network includes a wireless network provided by a communication provider of the terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, simply referred to as NIC) that can connect to other network devices through a base station to communicate with the internet. In one example, the transmission device 106 may be a Radio Frequency (RF) module, which is configured to communicate with the internet wirelessly.
The embodiment of the application provides a rapid backtracking analysis method for a target characteristic frequency point, as shown in fig. 2, the method comprises the following steps:
s201, acquiring all-dimensional beam forming data based on underwater sound signals acquired by a wet end.
Specifically, the method and the device receive underwater sound signals issued by the wet end in time sequence, and acquire all-dimensional beam forming data based on the underwater sound signals acquired by the wet end.
S202, performing spectrum extraction on the omnibearing wave beam forming data according to a preset time interval to obtain omnibearing LOFAR spectrum data.
Specifically, the method and the device perform spectrum extraction on the omnibearing wave beam forming data according to a preset time interval to obtain omnibearing LOFAR spectrum data. In an example embodiment, the most recently accumulated beamformed data is subjected to an omnidirectional LOFAR spectrum extraction every six seconds, primarily by a fast fourier transform.
S203, inquiring time azimuth history data of an all-time range and an all-azimuth range corresponding to the characteristic frequency point from the all-azimuth LOFAR frequency spectrum data based on the specified characteristic frequency point, and generating a LOFAR narrow-band azimuth history chart.
Specifically, the method stores the omnibearing LOFAR frequency spectrum data and the corresponding data time stamp in a memory according to time sequence, when a user designates a target characteristic frequency point, the time azimuth history data of the omnibearing time range and the omnibearing range of the frequency point are inquired from the stored omnibearing LOFAR frequency spectrum, a time-azimuth angle space coordinate system is established, and a LOFAR narrow-band azimuth history chart is generated based on the time azimuth history data.
S204, comparing the LOFAR narrowband azimuth calendar with a target azimuth calendar, and judging whether the characteristic frequency point belongs to a target characteristic frequency point.
According to the method, the time azimuth track data is obtained by inquiring the designated frequency point from the stored omnibearing LOFAR frequency spectrum, the time azimuth track is compared with the time azimuth track of the target, whether the frequency point is the characteristic of the target or not is screened, the time azimuth track of the designated frequency point can be quickly traced, the inquiring result is not limited by the time range, the method can be used for quickly screening whether the frequency point belongs to the frequency point of the target or not, and the efficiency of underwater sound target discovery and recognition is improved.
In one embodiment, the obtaining the omni-directional beam forming data of the underwater acoustic signal based on the underwater acoustic signal acquired by the wet end includes the following steps:
receiving underwater sound signals issued by a wet end according to time sequence; the underwater sound signal comprises array element domain data and array depth array direction data of a plurality of sensors; the sensor is an underwater acoustic sensor, and the array depth array direction data is the depth and the direction of the underwater acoustic sensor in the underwater environment. In this embodiment, the array element domain data and the array depth array direction data are accumulated in time sequence, and the latest accumulated array element domain data and array depth array direction data are subjected to omnibearing beam forming every second to obtain omnibearing beam forming data of the underwater sound signal.
In one embodiment, comparing the local narrowband azimuth calendar with the target azimuth calendar, and determining whether the characteristic frequency point belongs to the characteristic frequency point of the target includes: and comparing the LOFAR narrowband azimuth calendar with a target azimuth calendar, and judging whether the characteristic frequency point belongs to the characteristic frequency point of the target based on consistency. Specifically, if the consistency is good, the characteristic that the frequency point belongs to the target can be rapidly judged, and if the consistency is poor, the characteristic that the frequency point does not belong to the target can be rapidly judged.
In one embodiment, comparing the local narrowband azimuth calendar with the target azimuth calendar, and determining whether the characteristic frequency point belongs to the characteristic frequency point of the target based on consistency includes the following steps: obtaining similarity based on the LOFAR narrowband azimuth calendar and the target azimuth calendar; if the similarity is larger than a preset value, judging that the characteristic frequency point belongs to the characteristic frequency point of the target; and if the similarity is smaller than the preset value, judging that the characteristic frequency point does not belong to the characteristic frequency point of the target.
Specifically, in this embodiment, an image similarity algorithm may be adopted to obtain a similarity between the local narrowband azimuth calendar and the target azimuth calendar, and a preset value is preset, if the similarity is greater than the preset value, the characteristic frequency point is judged to belong to the characteristic frequency point of the target; if the similarity is smaller than the preset value, judging that the characteristic frequency points do not belong to the characteristic frequency points of the target, and realizing quick judgment of consistency.
In one embodiment, the extracting the omni-directional beam forming data according to a preset time interval to obtain a plurality of omni-directional local spectrum data further includes: and taking the time stamp of the first second beam forming data of the preset time interval as the data time stamp of the LOFAR frequency spectrum each time, namely taking the data time stamp of the first second beam forming data extracted from each frequency spectrum as the data time stamp of the LOFAR frequency spectrum each time, so that a user can designate a target characteristic frequency point, and carrying out time-azimuth process data inquiry of an all-time range and an all-azimuth range from the stored all-azimuth LOFAR frequency spectrum.
In one embodiment, the method further comprises storing each of the omnidirectional LOFAR spectral data and a corresponding data timestamp in a memory in time sequence.
According to the embodiment, all the omnibearing LOFAR frequency spectrum data and the corresponding data time stamps are stored in the memory according to the time sequence, when a user designates a target characteristic frequency point, the omnibearing LOFAR frequency spectrum stored in the memory can be searched for the time azimuth history data of the omnibearing frequency point in the omnibearing time range and the omnibearing range, a LOFAR narrow-band azimuth history chart is generated, and the rapid search of the target characteristic frequency point is realized.
In one embodiment, the obtaining the omni-directional beam forming data based on the underwater acoustic signal collected by the wet end further includes: and acquiring underwater sound signals acquired by the wet end, and carrying out noise reduction treatment on the underwater sound signals. Specifically, in an underwater environment, the underwater sound target signal is often covered by strong interference or background noise, so that noise reduction treatment is required to be performed on the underwater sound signal collected by the wet end to ensure the accuracy of the collected underwater sound signal.
The present embodiment is described and illustrated below by way of preferred embodiments.
Fig. 3 is a preferred flowchart of the target feature frequency point quick backtracking analysis method of the present embodiment, as shown in fig. 3, the target feature frequency point quick backtracking analysis method includes the following steps:
step one: the method comprises the steps of sequentially receiving underwater sound signals issued by a wet end according to time sequence, wherein the underwater sound signals comprise array element domain data and array depth array direction data of a sensor;
step two: accumulating array element domain data and array depth array direction data according to time sequence, carrying out omnibearing wave beam formation on the latest accumulated array element domain data and array depth array direction data every second, and generating a wave beam formation data time stamp;
step three: accumulating the beam forming data according to the time sequence, carrying out omnibearing LOFAR spectrum extraction on the newly accumulated beam forming data every six seconds, and taking the data timestamp of the first second beam forming data extracted by each spectrum as the data timestamp of the LOFAR spectrum;
step four: storing the omnibearing LOFAR spectrum data and the data time stamp in a memory according to time sequence;
step five: the user designates a target characteristic frequency point, and inquires time azimuth history data of the full time range and the full azimuth range of the frequency point from the stored full azimuth LOFAR frequency spectrum to generate a LOFAR narrow-band azimuth history chart;
step six: the user compares the searched LOFAR narrow-band azimuth calendar with azimuth calendar formed by other types of time azimuth tracks, if the consistency is good, the characteristic that the frequency point belongs to the target can be rapidly judged, and if the consistency is poor, the characteristic that the frequency point does not belong to the target can be rapidly judged.
According to the method, the time azimuth track data is obtained by inquiring the designated frequency point from the stored omnibearing LOFAR frequency spectrum, the time azimuth track is compared with the time azimuth track of the target, whether the frequency point is the characteristic of the target or not is screened, the time azimuth track of the designated frequency point can be quickly traced, the inquiring result is not limited by the time range, the method can be used for quickly screening whether the frequency point belongs to the frequency point of the target or not, and the efficiency of underwater sound target discovery and recognition is improved.
In a second aspect, the embodiment of the present application further provides a target feature frequency point fast backtracking analysis system, as shown in fig. 4, where the system includes an obtaining module 10, an extracting module 20, a querying module 30, and a comparing module 40.
The obtaining module 10 is used for obtaining all-dimensional beam forming data based on underwater acoustic signals collected by the wet end;
the extracting module 20 is configured to extract the omni-directional beam forming data according to a preset time interval to obtain omni-directional LOFAR spectrum data;
the query module 30 is configured to query, based on a specified feature frequency point, time azimuth history data of an all-time range and an all-azimuth range corresponding to the feature frequency point from the all-azimuth lowar spectrum data, and generate a lowar narrowband azimuth history map;
the comparison module 40 is configured to compare the local narrowband azimuth calendar with the target azimuth calendar, and determine whether the characteristic frequency point belongs to the characteristic frequency point of the target.
In one embodiment, the obtaining module 10 is further configured to receive the underwater acoustic signal issued by the wet end in time sequence; the underwater sound signal comprises array element domain data and array depth array direction data of a plurality of sensors; and accumulating the array element domain data and the array depth array direction data in time sequence, and carrying out omnibearing wave beam formation on the newly accumulated array element domain data and the newly accumulated array depth array direction data every second to obtain omnibearing wave beam formation data of the underwater sound signal.
In one embodiment, the comparing module 40 is further configured to compare the local narrowband azimuth calendar with the target azimuth calendar, and determine whether the feature frequency point belongs to the feature frequency point of the target based on consistency.
In one embodiment, the comparing module 40 is further configured to obtain a similarity based on the local narrowband azimuth calendar map and the target azimuth calendar map; if the similarity is larger than a preset value, judging that the characteristic frequency point belongs to the characteristic frequency point of the target; and if the similarity is smaller than the preset value, judging that the characteristic frequency point does not belong to the characteristic frequency point of the target.
In one embodiment, the extracting module 20 is further configured to use a timestamp of the first second beamforming data of the preset time interval as a data timestamp of the LOFAR spectrum each time.
In one embodiment, the system further includes a storage module configured to store each of the omnidirectional LOFAR spectrum data and a corresponding data timestamp in a memory according to a time sequence.
In one embodiment, the obtaining module 10 is further configured to obtain an underwater sound signal collected by the wet end, and perform noise reduction processing on the underwater sound signal.
The above-described respective modules may be functional modules or program modules, and may be implemented by software or hardware. For modules implemented in hardware, the various modules described above may be located in the same processor; or the above modules may be located in different processors in any combination.
In one embodiment, a computer device is provided, which may be a terminal, and the internal structure of which may be as shown in fig. 5. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. The computer program is executed by a processor to realize a rapid backtracking analysis method of the target characteristic frequency point. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by those skilled in the art that the structure shown in fig. 5 is merely a block diagram of some of the structures associated with the present application and is not limiting of the computer device to which the present application may be applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor implements the steps of any of the message pushing method or message forwarding method embodiments described above.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, or the like. Volatile memory can include random access memory (RandomAccess Memory, RAM) or external cache memory. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like.
The technical features of the above-described embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above-described embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples merely represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the invention. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application is to be determined by the claims appended hereto.
Claims (10)
1. The rapid backtracking analysis method for the target characteristic frequency points is characterized by comprising the following steps of:
acquiring omnibearing wave beam forming data based on underwater sound signals acquired by a wet end;
performing spectrum extraction on the omnibearing wave beam forming data according to a preset time interval to obtain omnibearing LOFAR spectrum data;
inquiring time azimuth history data of an all-time range and an all-direction range corresponding to the characteristic frequency points from the all-direction LOFAR frequency spectrum data based on the specified characteristic frequency points, and generating a LOFAR narrow-band azimuth history chart;
and comparing the LOFAR narrowband azimuth calendar with a target azimuth calendar, and judging whether the characteristic frequency point belongs to a target characteristic frequency point.
2. The method of claim 1, wherein the obtaining omni-directional beamforming data of the underwater acoustic signal based on the underwater acoustic signal acquired by the wet end comprises:
receiving underwater sound signals issued by a wet end according to time sequence; the underwater sound signal comprises array element domain data and array depth array direction data of a plurality of sensors;
and accumulating the array element domain data and the array depth array direction data in time sequence, and carrying out omnibearing wave beam formation on the newly accumulated array element domain data and the newly accumulated array depth array direction data every second to obtain omnibearing wave beam formation data of the underwater sound signal.
3. The method of claim 1, wherein comparing the LOFAR narrowband azimuth lineage map with a target azimuth lineage map, determining whether the feature frequency point belongs to a feature frequency point of a target includes:
and comparing the LOFAR narrowband azimuth calendar with a target azimuth calendar, and judging whether the characteristic frequency point belongs to the characteristic frequency point of the target based on consistency.
4. The method of claim 1, wherein comparing the LOFAR narrowband azimuth lineage graph with a target azimuth lineage graph, determining whether the feature frequency point belongs to a feature frequency point of a target based on consistency includes:
obtaining similarity based on the LOFAR narrowband azimuth calendar and the target azimuth calendar;
if the similarity is larger than a preset value, judging that the characteristic frequency point belongs to the characteristic frequency point of the target;
and if the similarity is smaller than the preset value, judging that the characteristic frequency point does not belong to the characteristic frequency point of the target.
5. The method of claim 1, wherein extracting the omni-directional beamforming data at preset time intervals to obtain a plurality of omni-directional LOFAR spectral data further comprises:
each time taking the timestamp of the first second beam forming data of the preset time interval as the data timestamp of the LOFAR spectrum.
6. The method of claim 5, wherein the method further comprises:
and storing each omnibearing LOFAR spectrum data and corresponding data time stamps in a memory according to time sequence.
7. The method of claim 1, wherein obtaining omni-directional beamforming data based on the underwater acoustic signal acquired at the wet end further comprises:
and acquiring underwater sound signals acquired by the wet end, and carrying out noise reduction treatment on the underwater sound signals.
8. A target feature frequency point quick backtracking analysis system, the system comprising:
the acquisition module is used for acquiring all-dimensional beam forming data based on the underwater acoustic signals acquired by the wet end;
the extraction module is used for extracting the omnibearing wave beam forming data according to a preset time interval to obtain omnibearing LOFAR frequency spectrum data;
the query module is used for querying the time azimuth history data of the full time range and the full azimuth range corresponding to the characteristic frequency points from the full azimuth LOFAR frequency spectrum data based on the specified characteristic frequency points, and generating a LOFAR narrow-band azimuth history chart;
and the comparison module is used for comparing the LOFAR narrowband azimuth calendar with the target azimuth calendar and judging whether the characteristic frequency point belongs to the characteristic frequency point of the target.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the method of any one of claims 1 to 7 when executing the computer program.
10. A computer readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, implements the method of any one of claims 1 to 7.
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Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH05346366A (en) * | 1992-06-16 | 1993-12-27 | Toshiba Corp | Computing equipment of floor response spectrum |
| CN1804612A (en) * | 2005-12-29 | 2006-07-19 | 西北工业大学 | Detection method for damage of random vibration structure based on correlation function amplitude vector |
| CN103529441A (en) * | 2012-07-02 | 2014-01-22 | 中国科学院声学研究所 | Method and system for detecting and distinguishing passive synthetic aperture target signal |
| US9869752B1 (en) * | 2016-04-25 | 2018-01-16 | Ocean Acoustical Services And Instrumentation Systems, Inc. | System and method for autonomous joint detection-classification and tracking of acoustic signals of interest |
| CN111220158A (en) * | 2020-01-15 | 2020-06-02 | 哈尔滨工程大学 | Line spectrum target motion parameter estimation method based on time azimuth history chart |
| CN111948607A (en) * | 2020-06-17 | 2020-11-17 | 中国船舶重工集团公司第七一五研究所 | Deep sea target passive positioning and particle filtering tracking method |
| CN112257521A (en) * | 2020-09-30 | 2021-01-22 | 中国人民解放军军事科学院国防科技创新研究院 | CNN underwater acoustic signal target identification method based on data enhancement and time-frequency separation |
| CN112946650A (en) * | 2021-04-15 | 2021-06-11 | 中山大学 | One-station fixed double-station low-frequency ultra-wideband SAR moving target detection and imaging method |
| CN114048165A (en) * | 2021-12-07 | 2022-02-15 | 中科长城海洋信息系统有限公司长沙分公司 | Underwater acoustic signal processing system and method based on DPU |
| CN115097429A (en) * | 2022-06-29 | 2022-09-23 | 中国电子科技集团公司第十四研究所 | Passive sonar target detection and track backtracking method based on CLEAN algorithm |
| CN115508817A (en) * | 2022-09-15 | 2022-12-23 | 浙江咸临智能科技有限责任公司 | Target tracking and distinguishing method suitable for underwater vehicle and application thereof |
| CN116299176A (en) * | 2023-04-04 | 2023-06-23 | 西北工业大学 | Target space feature extraction and fusion positioning method based on Hough transformation |
| CN116819432A (en) * | 2023-01-03 | 2023-09-29 | 杭州电子科技大学 | Single-vector hydrophone underwater multi-target high-stability direction finding method and system based on characteristic spectrum tracking |
-
2024
- 2024-01-17 CN CN202410067215.9A patent/CN117595943B/en active Active
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH05346366A (en) * | 1992-06-16 | 1993-12-27 | Toshiba Corp | Computing equipment of floor response spectrum |
| CN1804612A (en) * | 2005-12-29 | 2006-07-19 | 西北工业大学 | Detection method for damage of random vibration structure based on correlation function amplitude vector |
| CN103529441A (en) * | 2012-07-02 | 2014-01-22 | 中国科学院声学研究所 | Method and system for detecting and distinguishing passive synthetic aperture target signal |
| US9869752B1 (en) * | 2016-04-25 | 2018-01-16 | Ocean Acoustical Services And Instrumentation Systems, Inc. | System and method for autonomous joint detection-classification and tracking of acoustic signals of interest |
| CN111220158A (en) * | 2020-01-15 | 2020-06-02 | 哈尔滨工程大学 | Line spectrum target motion parameter estimation method based on time azimuth history chart |
| CN111948607A (en) * | 2020-06-17 | 2020-11-17 | 中国船舶重工集团公司第七一五研究所 | Deep sea target passive positioning and particle filtering tracking method |
| CN112257521A (en) * | 2020-09-30 | 2021-01-22 | 中国人民解放军军事科学院国防科技创新研究院 | CNN underwater acoustic signal target identification method based on data enhancement and time-frequency separation |
| CN112946650A (en) * | 2021-04-15 | 2021-06-11 | 中山大学 | One-station fixed double-station low-frequency ultra-wideband SAR moving target detection and imaging method |
| CN114048165A (en) * | 2021-12-07 | 2022-02-15 | 中科长城海洋信息系统有限公司长沙分公司 | Underwater acoustic signal processing system and method based on DPU |
| CN115097429A (en) * | 2022-06-29 | 2022-09-23 | 中国电子科技集团公司第十四研究所 | Passive sonar target detection and track backtracking method based on CLEAN algorithm |
| CN115508817A (en) * | 2022-09-15 | 2022-12-23 | 浙江咸临智能科技有限责任公司 | Target tracking and distinguishing method suitable for underwater vehicle and application thereof |
| CN116819432A (en) * | 2023-01-03 | 2023-09-29 | 杭州电子科技大学 | Single-vector hydrophone underwater multi-target high-stability direction finding method and system based on characteristic spectrum tracking |
| CN116299176A (en) * | 2023-04-04 | 2023-06-23 | 西北工业大学 | Target space feature extraction and fusion positioning method based on Hough transformation |
Non-Patent Citations (5)
| Title |
|---|
| XU YANG等: "Seismic Performance Analysis of ±800kV UHV Delta-connection Converter Transformer System", 《2019 IEEE 3RD CONFERENCE ON ENERGY INTERNET AND ENERGY SYSTEM INTEGRATION (EI2)》, 9 April 2020 (2020-04-09) * |
| 周胜增;杜选民;: "快速收敛最小方差无畸变响应算法研究及应用", 声学学报(中文版), no. 06, 15 November 2009 (2009-11-15) * |
| 杨普;陈励军;: "基于时延波束形成与短时倒谱算法的目标通过特性分析", 声学与电子工程, no. 02, 15 June 2018 (2018-06-15) * |
| 辛吉荣: "时频/时空域目标信号检测与跟踪技术研究", 《博士电子期刊出版信息》, 15 January 2021 (2021-01-15) * |
| 马启明;王宣银;杜栓平;: "基于频谱幅度起伏特性的微弱信号检测方法研究", 电子与信息学报, no. 11, 15 November 2008 (2008-11-15) * |
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