CN109000779A - A kind of ocean acoustic propagational reliability model Rapid matching system - Google Patents
A kind of ocean acoustic propagational reliability model Rapid matching system Download PDFInfo
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- CN109000779A CN109000779A CN201810764729.4A CN201810764729A CN109000779A CN 109000779 A CN109000779 A CN 109000779A CN 201810764729 A CN201810764729 A CN 201810764729A CN 109000779 A CN109000779 A CN 109000779A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
- G01H5/00—Measuring propagation velocity of ultrasonic, sonic or infrasonic waves, e.g. of pressure waves
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Abstract
The present invention realizes an ocean acoustic propagational reliability model Rapid matching system, comprising: submersible flag type scene Underwater Acoustic Environment Quick measurer: obtaining there and then marine environment data and noise vector information；Data are fitted correction module: being corrected based on actual measurement marine environment data to history environment data；Ocean propagation model computing module: a variety of underwater sound propagation models in underwater sound propagation model library, the Acoustic Wave Propagation forecast are resolved using GPU parallel calculating method near real-time；Acoustic propagation reliability model matching module: by the Acoustic Wave Propagation comparing that is calculated from different underwater sound propagation models of multiple spot actual measurement underwater sound propagation loss data, the most reliable model at the smallest model of variance as scene sea area moment at that time.The reliable acoustic propagation computation model based on live marine environment data information fast selecting sea area at that time may be implemented in system, is effectively improved that ocean propagation model ambient adaptability is bad, ocean acoustic propagation calculates the not high realistic problem of confidence level.
Description
Technical field
The present invention relates to propagation model resolving under a kind of ocean water and acoustic fields and its model fast matching method, belong to
Yu Haiyang field of acoustics.
Background technique
With broadband acoustic propagation, shallow sea sound inverting, Matched Field positioning, underwater environment emulation the problems such as further investigation,
To the calculating speed of underwater sound propagation model, more stringent requirements are proposed with precision.But sound is propagated along curve under water and by scene
Hydrological environment condition is affected, and there are strong spatial asymmetry and change in time and space for underwater acoustic propagation, although being currently directed to
Up to hundred kinds of the underwater acoustic propagation computation model of different marine environment, but different models have respective use scope,
In the scope of application and under the premise of input parameters precision is met the requirements, it can generally guarantee the reliability of the model calculation.And
Under reality, since Underwater Acoustic Environment is especially complex, not only underwater propagation model input parameters precision is difficult to meet acoustic propagation
The requirement of calculating is also more difficult to ensure the acoustic propagation computation model applicable according to the selection of live marine environment, based on scene sea
The foreign reliable ocean acoustic propagation computation model of environmental selection is always the difficulties that marine acoustics field faces practical application.
Summary of the invention
In view of the above-mentioned problems, being calculated to realize based on the live most reliable ocean acoustic propagation of marine environment feature fast selecting
Model improves the confidence level that the underwater acoustic propagation of there and then calculates, the underwater sound field environment of accurate description, technology contents of the invention
Include:
Submersible flag type scene Underwater Acoustic Environment Quick measurer, real-time measurement scene marine environment are built, component part includes,
(1) it buoy antenna: acts on emission measurement scene marine environment data and receives control instruction；(2) floating body: effect guarantees antenna dew
Sea out；(3) the close chain of thermohaline: effect Measuring Oceanic thermohaline it is close-depth section；(4) buoyancy subsurface buoy: effect is carried digitlization water and is listened
Device.
The number hydrophone composition includes: hydrophone, low-noise preamplifier, the high pass filter as Sensor section
Wave device, 24 Sigma-Delta type ADC and signal processor composition.The number hydrophone carries out electricity to analog power
Source isolation and filtering processing, use digital signal isolating chip between ADC and processor, analog portion and numerical portion is isolated,
Switching noise is obstructed to signal interference, the preamplifier of low noise is targetedly selected, takes into account current noise and voltage is made an uproar
The influence of sound inhibits the current noise of low-frequency range.
The submersible flag type scene Underwater Acoustic Environment Quick measurer field measurement obtain the temperature, salinity of marine environment at that time and
Density data, which enters data fitting correction module and is modified to marine environment historical data, using two
To data discrete point and discontinuously, place carries out data process of fitting treatment to secondary batten difference approach, and guarantee data standard is unified, format is advised
Model meets underwater Sound speed profile and calculates requirement.
The ocean propagation model computing module receives the close ocean of standardization thermohaline of data fitting correction module output
Environmental data calculates underwater Sound speed profile live at that time, using GPU parallel calculating method to the sound in the propagation model library of ocean
Propagation model realizes that near real-time resolves, and forecasts live Acoustic Wave Propagation.Tradition resolves underwater sound propagation model calculation amount using CPU
Greatly, time-consuming, for guarantee system can near real-time resolve the underwater sound propagation models of various complexity, counted parallel using advanced GPU
Calculation method resolves a variety of models in underwater sound propagation model library.
The ocean acoustic propagational reliability model fitting module is by comparing field measurement environment Acoustic Wave Propagation number at that time
According to the Acoustic Wave Propagation data calculated with model scene each in ocean water propagation model library, counts, compares the two variance size,
Choose the most reliable model that the smallest model of variance is the live sea area local moment.
Present invention has the advantage that the marine environment actual measurement using GPU parallel computing, by scene at that time, local
During data application to the selection of ocean acoustic propagation computation model, by surveying environment Acoustic Wave Propagation number using live multiple spot
The propagation loss comparing calculated according to the scene with model in underwater sound propagation model library, the selection the smallest model of variance are the sea
The most reliable model at area's locality moment enhances underwater Acoustic Wave Propagation so that the timeliness for the forecasting model chosen greatly improves
The reliability of forecast information, ocean propagation model environment is suitable caused by preferably resolving the complexity due to marine environment
With the problems such as property is bad, acoustic propagation forecast reliability is not high, the confidence level of ocean acoustic field calculating is improved.
Detailed description of the invention
The structural block diagram that Fig. 1 illustrates the technology of the present invention realization means and system is constituted.
In figure, 1 is submersible flag type scene Underwater Acoustic Environment Quick measurer, and 2 are fitted correction module for data, and 3 pass for ocean acoustic
Model computation module is broadcast, 4 be ocean acoustic propagational reliability model fitting module.
Fig. 2 illustrates submersible flag type scene Underwater Acoustic Environment Quick measurer working principle diagram of the invention.
Fig. 3 illustrates the digital hydrophone circuit structure of the submersible flag type scene Underwater Acoustic Environment Quick measurer.
Fig. 4 illustrates CUDA and seeks characteristic value grid thread allocation plan parallel.
Fig. 5 illustrates the realization module map of the WKBZ model based on GPU.
Fig. 6 illustrates the overall framework figure based on the GPU BDRM model realized.
Fig. 7 illustrates FOR3D and splits step-stepping method schematic diagram.
Specific embodiment
A kind of ocean acoustic propagational reliability model Rapid matching system in order to further illustrate the present invention, with reference to the accompanying drawing
Present invention is further described in detail with specific embodiment, apparent elaboration system and implementation method of the invention, and
Explain the present invention, but not as a limitation of the invention.
A kind of ocean acoustic propagational reliability model Rapid matching system as shown in Figure 1, ocean propagation model calculate mould
Block 3 is carried out based on GPU parallel computing, calculates resulting underwater forecast Acoustic Wave Propagation and field measurement Acoustic Wave Propagation number
According to being compared, the smallest model of variance is the most reliable model of local sea area at that time, and ocean acoustic propagational reliability model is fast
Fast matching system is acquired according to data, data fitting amendment, ocean propagation model calculates, ocean propagation model matches in batches
Secondary progress:
1) submersible flag type scene Underwater Acoustic Environment Quick measurer 1 is workbench, 15 He of integrated digital hydrophone with subsurface buoy 14
Temperature, salt, close sensor 13 realize space concurrent, synchronous acquisition seanoise Vector Message and together with ocean depth-temperature, salt, close
Degree section and temporal information combine, and form data file and are stored in CF card.
2) data fitting correction module 2 receives the multiple spot actual measurement sea that submersible flag type scene Underwater Acoustic Environment Quick measurer 1 measures
Foreign environmental data carries out the fusion of data using quadratic spline difference arithmetic and further repairs in conjunction with marine environment historical data
Just, the discrete and discontinuous problem of measured data and historical data is solved, the marine environment number of standard unification, format specification is constructed
According to collection.
3) ocean propagation model computing module 3 receives the close ocean of standardization thermohaline that data fitting correction module 2 exports
Environmental data calculates the underwater Sound speed profile in scene at that time in real time, uses GPU parallel calculating method pair based on calculated result
Propagation model in the propagation model library of ocean realizes that near real-time resolves, and forecasts live Acoustic Wave Propagation.
Further, for the normal mode class model in underwater propagation model, using the parallel rapid solving based on GPU
Characteristic value method solves characteristic value using CUDA design polychotomy.Polychotomy can calculate multiple functional values simultaneously, then find two
A adjacent and functional value contrary sign independent variable, and using them as new boundary, multiple spot evaluation is being carried out, successively iteration is finally asked
Obtain Function Solution.
Implementation process such as Fig. 4 constructs the kernel of a solution characteristic value in CPU host, accelerates spy parallel using GPU hardware
Point distributes a thread grid for the kernel in GPU, and how many a characteristic values just distribute how many a thread blocks for the grid, and
Thread Count in block is identical as evaluation number in polychotomy, and multiple characteristic value Parallel implementations are achieved, and drops overall time-consuming significantly
It is low.The absolute calculation amount of polychotomy solution procedure increases, but convergence rate is accelerated, and the number of iterations is reduced, and uses CUDA parallel computation
Multiple characteristic values can be calculated simultaneously, are amounted to evaluation time and are greatly reduced.
Further, for the WKBZ model in underwater propagation model, realization frame such as Fig. 5 based on GPU, the frame
Two parallel processing grids are divided in GPU, comprising the following steps:
Step 501: grid 1 solves characteristic value using polychotomy, completes to calculate；
Step 502: grid 2 is made of eigenfunction computing module and integrated displaying modular, will be tied after calculating eigenfunction
Fruit is converted into texture and is shown.
Further, for the BDRM model in underwater propagation model, GPU realizes frame such as Fig. 6, including following step
It is rapid:
Step 601: grid 1 solves local characteristic value using polychotomy, completes to calculate；
Step 602: grid 2 is made of local eigenfunction computing module and integrated displaying modular, calculates local intrinsic letter
Texture is converted by result after number to show.
Further, it for FOR3D the and PE model in underwater propagation model, is solved using step-stepping method is split,
Split step-stepping method resolution principle such as Fig. 7.Depth is divided into M layers, the number of plies is indicated with m, and every layer of depth is Δ z；Argument
It is divided into L sector, sector number l, the argument of each sector is Δ θ；Then step delta r is taken to carry out stepping on the direction distance r
Operation, the distance of requirement is stepped to from initial fields, indicates number of steps with j.
Further, it for the coupling normal mode in underwater propagation model-Parabolic Equation (CMPE) model, equally answers
Characteristic value and the high-effect matrix solution of GPU is asked to carry out GPU Parallel Implementation with GPU multiple spot.
Ocean acoustic propagational reliability model Rapid matching system provided by the invention, gives full play to GPU parallel computation
The advantage of technology, it is creative that marine environment measured data is applied in the selection of ocean propagation model in sea area at that time,
It realizes based on the reliable sound field computation model of live marine environment data information fast selecting, preferably resolves due to ocean ring
Ocean propagation model ambient adaptability is bad caused by the complexity in border, ocean acoustic propagation calculates that confidence level is not high to ask
Topic.
The above is only a preferred embodiment of the present invention, it should be noted that ocean propagation model up to hundreds of,
With the respective scope of application and constraint condition, the present invention carries out technical solution for several common ocean propagation models
Illustrate, but is not intended to limit it, it is all to belong to the near real-time resolving that ocean propagation model is realized using GPU parallel computing
And the selection for realizing reliable propagation model is further matched with measured data, the spirit and scope of the present invention should all be belonged to
It is interior.Without departing from the principle of the present invention, it is passed for several improvement of marine environment measurement means, retouching and to ocean acoustic
Broadcast the variations such as the expansion of model library, application, embodiment also within the spirit and scope of the present invention.
Claims (5)
1. a kind of ocean acoustic propagational reliability model Rapid matching system, which is characterized in that system includes:
Submersible flag type scene Underwater Acoustic Environment Quick measurer (1): the module is used to obtain the ocean Underwater Acoustic Environment in live sea area at that time
Data, the ocean Underwater Acoustic Environment data packet include seanoise Vector Message, ocean thermohaline it is close-depth section and the time letter
Breath；
Data are fitted correction module (2): the module is to field measurement ocean Underwater Acoustic Environment data and ocean Underwater Acoustic Environment history number
According to collecting and synchronization process, data correction fitting is carried out using quadratic spline difference approach, the data after amendment fitting are for sea
Foreign propagation model calculates；
Ocean propagation model computing module (3): the module include but is not limited to KRAKEN model, WKBZ model, BDRM model,
A variety of ocean water propagation models such as FOR3D (PE) model and CMPE model, the ocean water propagation model are respectively suitable for
Ocean underwater sound propagation under varying environment calculates, using GPU concurrent operation method based on the data correction fitting module (2)
The ocean Underwater Acoustic Environment data of offer at that time, the Acoustic Wave Propagation of local marine environment carry out near real-time forecast calculation；
Ocean acoustic propagational reliability model fitting module (4): the module is by the submersible flag type scene Underwater Acoustic Environment rapid survey
The sound that the actual measurement ocean Acoustic Wave Propagation and ocean propagation model computing module (3) near real-time that device (1) provides are forecast
Calculating is compared in propagation loss data, counts, both compares the size of variance, by the smallest model of variance be determined as at that time when
Most reliable ocean acoustic propagation computation model under ground environment.
2. a kind of ocean acoustic propagational reliability model Rapid matching system as described in claim 1, which is characterized in that described
Submersible flag type scene Underwater Acoustic Environment Quick measurer (1) includes: that buoy antenna (11), floating body (12), the close chain of thermohaline (13), buoyancy are latent
Mark (14), digitlization hydrophone (15)；
Buoy antenna (11) the emission measurement field measurement data guarantee day with control instruction, the floating body (12) is received
Line exposes sea, and the close chain of the thermohaline (13) Measuring Oceanic thermohaline is close-depth section, and the buoyancy subsurface buoy (14) carries number
Word hydrophone, the digital hydrophone (15) acquire seanoise Vector Message.
3. a kind of ocean acoustic propagational reliability model Rapid matching system as described in claim 1, which is characterized in that described
Data fitting correction module (2) is based on multiple spot actual measurement oceanographic data and is further corrected to marine environment historical data, uses
Quadratic spline difference arithmetic solves the discrete and discontinuous problem of data, kisses live marine environment data more with actual conditions
It closes.
4. a kind of ocean acoustic propagational reliability model Rapid matching system as described in claim 1, which is characterized in that described
Ocean propagation model computing module (3) is real to the common model in the propagation model library of ocean using GPU parallel calculating method
Existing near real-time resolves, and forecasts live Acoustic Wave Propagation, the ocean propagation model include but is not limited to KRAKEN model,
WKBZ model, BDRM model, FOR3D (PE) model, CMPE model etc., the scope of application cover high frequency acoustic propagation, horizontal slice sea
The various application occasions such as foreign environment acoustic propagation, broadband acoustic propagation, deep-marine-environment acoustic propagation and neritic environment acoustic propagation.
5. a kind of ocean acoustic propagational reliability model Rapid matching system as described in claim 1, which is characterized in that described
Ocean acoustic propagational reliability model fitting module (4) passes live multiple spot actual measurement ambient water Acoustic Wave Propagation data and the ocean underwater sound
The propagation loss data for broadcasting the scene calculating of model in model library are compared, and choosing the smallest model of variance is that live sea area is worked as
The most reliable model at ground moment.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109443516A (en) * | 2018-12-25 | 2019-03-08 | 西北工业大学 | A kind of passive acquisition methods of Bottom sound speed based on the vertical vibration velocity signal of noise field |
| CN109489799A (en) * | 2018-12-25 | 2019-03-19 | 西北工业大学 | A kind of Bottom sound speed substep inversion method based on double vector hydrophones |
| CN110968830A (en) * | 2019-12-23 | 2020-04-07 | 哈尔滨工程大学 | Deep sea complete sound channel lower focal dispersion line convergence region gain calculation method based on ray normal wave theory |
| CN111442871A (en) * | 2019-01-17 | 2020-07-24 | 天津大学青岛海洋技术研究院 | Method for measuring net buoyancy loss of underwater glider at different water depths |
| CN113259034A (en) * | 2021-07-08 | 2021-08-13 | 自然资源部第一海洋研究所 | Parallel coupled marine acoustic forecasting system and operation method |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7307914B1 (en) * | 2005-11-30 | 2007-12-11 | The United States Of America As Represented By The Secretary Of The Navy | Hypothesized range and depth sonar processing method |
| CN101604019A (en) * | 2009-07-13 | 2009-12-16 | 中国船舶重工集团公司第七一五研究所 | A kind of marine environment and sound field be the sign of certainty and the quick calculation method of transmission not |
| CN102353515A (en) * | 2011-06-22 | 2012-02-15 | 中国海洋大学 | Method and system for acoustically monitoring neritic non-linear internal waves |
| CN104180891A (en) * | 2014-08-26 | 2014-12-03 | 中国船舶重工集团公司第七一五研究所 | Method for measuring ocean sound transmission loss in real time based on sound matrix |
| CN107329141A (en) * | 2017-08-03 | 2017-11-07 | 厦门大学 | A kind of boat body faint radiated noise signals detection method under marine environment |
-
2018
- 2018-07-12 CN CN201810764729.4A patent/CN109000779B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7307914B1 (en) * | 2005-11-30 | 2007-12-11 | The United States Of America As Represented By The Secretary Of The Navy | Hypothesized range and depth sonar processing method |
| CN101604019A (en) * | 2009-07-13 | 2009-12-16 | 中国船舶重工集团公司第七一五研究所 | A kind of marine environment and sound field be the sign of certainty and the quick calculation method of transmission not |
| CN102353515A (en) * | 2011-06-22 | 2012-02-15 | 中国海洋大学 | Method and system for acoustically monitoring neritic non-linear internal waves |
| CN104180891A (en) * | 2014-08-26 | 2014-12-03 | 中国船舶重工集团公司第七一五研究所 | Method for measuring ocean sound transmission loss in real time based on sound matrix |
| CN107329141A (en) * | 2017-08-03 | 2017-11-07 | 厦门大学 | A kind of boat body faint radiated noise signals detection method under marine environment |
Non-Patent Citations (2)
| Title |
|---|
| STAN E. DOSSO 等: "Estimation of OceadBottom Properties by Matched-Field Inversion of Acoustic Field Data", 《IEEE JOURNAL OF OCEANIC ENGINEERING》 * |
| 章新华 等: "基于CUDA的深海声场预报技术研究", 《声学技术》 * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109443516A (en) * | 2018-12-25 | 2019-03-08 | 西北工业大学 | A kind of passive acquisition methods of Bottom sound speed based on the vertical vibration velocity signal of noise field |
| CN109489799A (en) * | 2018-12-25 | 2019-03-19 | 西北工业大学 | A kind of Bottom sound speed substep inversion method based on double vector hydrophones |
| CN111442871A (en) * | 2019-01-17 | 2020-07-24 | 天津大学青岛海洋技术研究院 | Method for measuring net buoyancy loss of underwater glider at different water depths |
| CN110968830A (en) * | 2019-12-23 | 2020-04-07 | 哈尔滨工程大学 | Deep sea complete sound channel lower focal dispersion line convergence region gain calculation method based on ray normal wave theory |
| CN110968830B (en) * | 2019-12-23 | 2023-04-11 | 哈尔滨工程大学 | Deep sea complete sound channel lower focal-dispersion line convergence region gain calculation method based on ray normal wave theory |
| CN113259034A (en) * | 2021-07-08 | 2021-08-13 | 自然资源部第一海洋研究所 | Parallel coupled marine acoustic forecasting system and operation method |
| CN113259034B (en) * | 2021-07-08 | 2021-09-28 | 自然资源部第一海洋研究所 | Parallel coupled marine acoustic forecasting system and operation method |
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