CN115248075A - Underwater acoustic detection efficiency evaluation system for multi-source acoustic big data fusion - Google Patents
Underwater acoustic detection efficiency evaluation system for multi-source acoustic big data fusion Download PDFInfo
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Abstract
The embodiment of the invention provides an underwater acoustic detection efficiency evaluation system with multi-source acoustic big data fusion, which comprises a measurement sensing subsystem, a data acquisition subsystem and a data processing subsystem, wherein the measurement sensing subsystem is used for monitoring and acquiring real-time environmental parameter data at least comprising temperature, salinity, depth and environmental noise in a test sea area in real time; the external historical data access module is used for accessing historical data in a historical marine environment database and prediction output information of other models, inputting the historical data and measured data into a marine mode and predicting the distribution of the temperature and salt fields of the sea; the signal processing subsystem is used for managing the acquired real-time environment parameter data and historical data, realizing the forecast of the distribution of the ocean thermal-salt field by analysis, realizing the statistic calculation and algorithm of the sound field uncertainty in the dynamic ocean environment by comparing the forecast data with the actually measured data, carrying out detection efficiency evaluation and providing the optimization suggestion of the system parameters; and the display control subsystem is used for performing man-machine interaction and realizing system parameter configuration and output result display.
Description
Technical Field
The invention relates to the technical field of underwater acoustic detection, in particular to an underwater acoustic detection efficiency evaluation system for multi-source acoustic big data fusion.
Background
The space-time variability and the heterogeneity of the marine environment have great influence on the acoustic detection, so that the performance of the acoustic detection also presents a space-time-frequency change effect, which is mainly reflected in the detection distance of underwater acoustic targets, and the detection distance of the same detection equipment in different seasons and under the hydrographic conditions may differ by dozens of kilometers. However, the dynamic characteristics of the marine environment can be predicted, and by means of the prediction analysis of the marine sound field environment, how to realize the evaluation and prediction of the actual detection capability of the acoustic detection system and guide the configuration and working parameter optimization of the detection system are always difficult problems in the field of underwater acoustic detection efficiency evaluation.
The traditional underwater sound detection efficiency evaluation system mainly takes historical data as main data, and is additionally provided with a plurality of discrete measured data for carrying out detectivity analysis, performance prediction of hydrological environment change in the next few days is lacked, a detection efficiency prediction result is given, and the given real-time evaluation or prediction result adopts a quantitative description mode and lacks the representation of environment uncertainty.
Disclosure of Invention
The embodiment of the invention provides an underwater acoustic detection efficiency evaluation system for multi-source acoustic big data fusion, which is characterized in that an ocean sound field forecasting model and a statistical model of the change characteristics of a sound field are established, the sound field environment of a test sea area is analyzed and forecasted in real time on the established model by acquiring real-time measured ocean environment data, the functional performance of the multi-source acoustic information fusion detection system is simulated, a fusion detection efficiency evaluation method is researched, and the real-time detection capability of the multi-source acoustic fusion detection system is evaluated.
The embodiment of the invention provides an underwater acoustic detection efficiency evaluation system for multi-source acoustic big data fusion, which comprises:
the measurement sensing subsystem is used for monitoring and acquiring marine environment parameters at least including temperature, salinity, depth and environmental noise of a test sea area in real time and providing real-time environment parameter data;
the external historical data access module is used for accessing historical data in the historical marine environment database and prediction output information of other models at least comprising meteorological models, inputting the historical data and measured data into a marine mode and further forecasting the temperature and salt field distribution of the sea;
the signal processing subsystem is used for managing the acquired real-time environment parameter data and the historical data, realizing the forecast of the ocean thermal-salt field distribution through analysis, realizing the statistic calculation and algorithm of the sound field uncertainty in the dynamic ocean environment by comparing the forecast with the actually measured data, carrying out detection efficiency evaluation and providing the optimization suggestion of system parameters;
and the display control subsystem is used for performing man-machine interaction and realizing system parameter configuration and output result display.
In some embodiments of the invention, the measurement sensing subsystem includes at least a thermohalimeter, ambient noise monitoring, and other hydrologic monitoring devices to monitor marine environmental parameters including at least water flux, heat flux, and wind stress.
In some embodiments of the present invention, the signal processing subsystem includes a data service module, which includes a data acquisition card and a hardware interface unit, and is configured to acquire the real-time environment parameter data, store the real-time environment parameter data in a data format same as that of the historical data, and provide the real-time monitoring data and the historical access data after being sorted to the sound field forecasting module, so as to provide data support for implementing a subsequent algorithm.
In some embodiments of the present invention, the signal processing subsystem further includes a sound field forecasting module, which performs forecasting of ocean thermal-salt field distribution by analyzing real-time environmental parameter data and historical data, obtains ocean sound field distribution and sound propagation loss estimation by sound field modeling, and performs statistical calculation of sound field uncertainty in dynamic ocean environment by comparing the forecasted and measured data.
In some embodiments of the present invention, the signal processing subsystem further includes a system modeling module, which is configured to perform acoustic performance modeling on the underwater fusion detection system with dynamic combination and static combination, and perform design including at least acoustic array space gain calculation, active and passive detection signal processing algorithm gain calculation, and detection algorithm and detection threshold.
In some embodiments of the present invention, the signal processing subsystem further includes a performance evaluation module, which is configured to synthesize calculation results of the sound field prediction module and the system modeling module, estimate and predict the detection distance and range of the fusion detection system in real time, perform detection performance evaluation, and further provide an optimization suggestion of system parameters.
In some embodiments of the invention, the display control subsystem comprises:
the parameter configuration module is used for configuring the acoustic parameters of the system and realizing function selection at least comprising typical targets and real-time/forecast;
and the output display module is used for providing a sound field distribution prediction graph and sound field uncertainty representation, fusing the prediction result of the system detection distance, and comparing and analyzing according to the system requirement and the efficiency evaluation result to provide a reasonable parameter optimization suggestion.
The underwater acoustic detection efficiency evaluation system for multi-source acoustic big data fusion provided by the embodiment of the invention has the following advantages: establishing a marine sound field forecasting method and a dynamic marine environment uncertainty characterization method which are adaptive to a refined regional coupling marine mode-sound field model by using marine environment historical data, and establishing a marine sound field forecasting model and a statistical model of the sound field change characteristic; measuring marine environment data in real time by using sensors such as a sound velocity meter, a temperature and depth meter, a noise monitoring hydrophone and the like, and analyzing and forecasting the sound field environment of a test sea area in real time on the established model; establishing a digital simulation model by using a functional model provided by the multi-source acoustic information fusion detection system, and simulating the functional performance of the multi-source acoustic information fusion detection system; under the support of actually measured and predicted ocean sound field information and a typical target acoustic characteristic fusion detection model, a fusion detection efficiency evaluation method is researched to evaluate the real-time detection capability of the multi-source acoustic fusion detection system and predict the detection capability in advance.
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Fig. 1 is a schematic diagram of an acoustic big data fusion underwater acoustic detection performance evaluation system according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the present invention will be further described with reference to the accompanying drawings and detailed description.
The phrases "in one embodiment," "in another embodiment," "in yet another embodiment," "in an embodiment," "in some embodiments," or "in other embodiments" may be used in this specification to refer to one or more of the same or different embodiments in accordance with the invention.
Specific embodiments of the present invention are described hereinafter with reference to the accompanying drawings; however, it is to be understood that the disclosed embodiments are merely exemplary of the invention, which can be embodied in various forms. Well-known and/or repeated functions and constructions are not described in detail to avoid obscuring the invention in unnecessary or unnecessary detail based on the user's historical actions, to discern true intent. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a basis for the claims and as a representative basis for teaching one skilled in the art to variously employ the present invention in virtually any appropriately detailed structure.
The embodiment of the invention provides an underwater acoustic detection efficiency evaluation system for multi-source acoustic big data fusion, as shown in fig. 1, comprising: the system comprises a measurement sensing subsystem, an external historical data access module, a signal processing subsystem and a display control subsystem, wherein the measurement sensing subsystem, the external historical data access module, the signal processing subsystem and the display control subsystem are divided into four parts
The measurement sensing subsystem mainly comprises sensors such as a thermohaline depth gauge, environmental noise monitoring and other hydrological monitoring equipment (used for monitoring marine environmental parameters such as water flux, heat flux, wind stress and the like), and the sensors mainly have the functions of monitoring and acquiring marine environmental parameters such as temperature, salinity, depth, environmental noise and the like of a test sea area in real time and providing real-time environmental parameter data for the whole system;
the external historical data access module is used for providing information such as historical data in a historical marine environment database and prediction output (such as meteorological model output) of other models to the whole system, inputting the historical data and actual measurement data into a marine mode and further forecasting the temperature and salinity distribution of the sea;
the signal processing subsystem takes a data acquisition board card and a signal processor as hardware platforms and is responsible for carrying out data acquisition, management and algorithm realization of the system, managing the acquired real-time environment parameter data and the historical data, realizing the forecast of ocean thermohaline field distribution through analysis, realizing the statistical calculation and algorithm of sound field uncertainty in dynamic ocean environment by comparing the forecast and actual measurement data, carrying out detection efficiency evaluation and proposing an optimization suggestion of system parameters;
the signal processing subsystem comprises a data service module, a sound field forecasting module, a system modeling module and a performance evaluation module, wherein,
the data service module comprises a data acquisition card and a hardware interface unit, acquires marine environment information detected by each sensor, stores the marine environment information according to a data format same as historical data, and provides the organized real-time monitoring data and historical access data to the sound field forecasting module to provide data support for the realization of a subsequent algorithm;
the sound field forecasting module is used for forecasting the distribution of the ocean thermal-salt field through real-time and historical data analysis, further obtaining the distribution of the ocean sound field and sound propagation loss estimation through sound field modeling, and meanwhile, the forecasting and actual measurement data can be compared to realize statistical calculation of sound field uncertainty in the dynamic ocean environment;
the system modeling module is responsible for carrying out acoustic performance modeling on the underwater fusion detection system combining the dynamic state and the static state, and comprises the steps of calculating the space gain of an acoustic array, calculating the gain of an active and passive detection signal processing algorithm, designing a detection algorithm and a detection threshold value and the like;
and the efficiency evaluation module integrates the calculation results of the sound field forecasting module and the system modeling module, estimates and forecasts the detection distance and range of the fusion detection system in real time, evaluates the detection efficiency of the system and further provides an optimization suggestion of system parameters.
And the display control subsystem is a human-computer interaction part of the whole system and realizes the visual display of system parameter configuration and output results.
In this embodiment, the display control subsystem includes: the parameter configuration module is used for configuring acoustic parameters of the system and realizing function selection at least comprising typical targets and real-time/forecast; and the output display module is used for providing a sound field distribution prediction graph and sound field uncertainty representation, fusing the prediction result of the system detection distance, and comparing and analyzing according to the system requirement and the efficiency evaluation result to provide a reasonable parameter optimization suggestion.
According to the technical scheme, the underwater sound detection efficiency evaluation system with multi-source acoustic big data fusion provided by the embodiment of the invention utilizes the marine environment historical data to establish a marine sound field forecasting method and a dynamic marine environment uncertainty characterization method which are adaptive to a refined region coupling marine mode-sound field model, and establish a marine sound field forecasting model and a statistical model of sound field change characteristics; measuring marine environment data in real time by using sensors such as a sound velocimeter, a temperature and depth instrument, a noise monitoring hydrophone and the like, and analyzing and forecasting the sound field environment of a test sea area in real time on a built model; establishing a digital simulation model by using a functional model provided by the multi-source acoustic information fusion detection system, and simulating the functional performance of the multi-source acoustic information fusion detection system; under the support of actually measured and predicted ocean sound field information and typical target acoustic characteristic fusion detection models, a fusion detection efficiency evaluation method is researched to evaluate the real-time detection capability of the multi-source acoustic fusion detection system and can predict the detection capability in advance.
While the present invention has been described in detail with reference to the preferred embodiments, it should be understood that the above description should not be taken as limiting the invention. Various modifications and alterations to this invention will become apparent to those skilled in the art upon reading the foregoing description. Accordingly, the scope of the invention should be limited only by the attached claims.
Claims (7)
1. A multi-source acoustic big data fusion underwater acoustic detection efficiency evaluation system is characterized by comprising:
the measurement sensing subsystem is used for monitoring and collecting marine environment parameters at least including temperature, salinity, depth and environmental noise of a test sea area in real time and providing real-time environment parameter data;
the external historical data access module is used for accessing historical data in the historical marine environment database and prediction output information of other models at least comprising meteorological models, inputting the historical data and measured data into a marine mode and further forecasting the temperature and salt field distribution of the sea;
the signal processing subsystem is used for managing the acquired real-time environment parameter data and the historical data, realizing the forecast of the ocean thermal-salt field distribution through analysis, realizing the statistic calculation and algorithm of the sound field uncertainty in the dynamic ocean environment by comparing the forecast with the actually measured data, carrying out detection efficiency evaluation and providing the optimization suggestion of system parameters;
and the display control subsystem is used for performing man-machine interaction and realizing system parameter configuration and output result display.
2. The multi-source acoustic big data fusion underwater acoustic detection efficiency evaluation system according to claim 1,
the measurement and sensing subsystem at least comprises a thermohaline depth gauge, environmental noise monitoring and other hydrological monitoring equipment for monitoring marine environmental parameters at least comprising water flux, heat flux and wind stress.
3. The multi-source acoustic big data fused underwater acoustic detection performance evaluation system according to claim 2,
the signal processing subsystem comprises a data service module, a data acquisition card and a hardware interface unit, and is used for acquiring the real-time environment parameter data, storing the real-time environment parameter data in the same data format as the historical data, and providing the organized real-time monitoring data and the historical access data to a sound field forecasting module so as to provide data support for the implementation of a subsequent algorithm.
4. The multi-source acoustic big data fusion underwater acoustic detection efficiency evaluation system according to claim 3,
the signal processing subsystem further comprises a sound field forecasting module which is used for forecasting the distribution of the ocean thermal-salt field by analyzing the real-time environment parameter data and the historical data, obtaining the distribution of the ocean sound field and the estimation of sound propagation loss by sound field modeling, and comparing the forecast data with the actually measured data to realize the statistical calculation of the sound field uncertainty in the dynamic ocean environment.
5. The multi-source acoustic big data fusion underwater acoustic detection efficiency evaluation system according to claim 4,
the signal processing subsystem also comprises a system modeling module which is used for carrying out acoustic performance modeling on the underwater fusion detection system with dynamic combination and static combination, and carrying out design at least comprising sound array space gain calculation, active and passive detection signal processing algorithm gain calculation, detection algorithm and detection threshold.
6. The multi-source acoustic big data fusion underwater acoustic detection efficiency evaluation system according to claim 5,
the signal processing subsystem also comprises an efficiency evaluation module which is used for integrating the calculation results of the sound field forecasting module and the system modeling module, estimating and forecasting the detection distance and range of the fusion detection system in real time, and carrying out detection efficiency evaluation so as to provide an optimization suggestion of system parameters.
7. The multi-source acoustic big data fused underwater acoustic detection performance evaluation system according to claim 6, wherein said display control subsystem comprises:
the parameter configuration module is used for configuring the acoustic parameters of the system and realizing function selection at least comprising typical targets and real-time/forecast;
and the output display module is used for providing a sound field distribution prediction graph and sound field uncertainty representation, fusing the prediction result of the detection distance of the system, and providing a reasonable parameter optimization suggestion according to the comparison analysis of the system requirement and the efficiency evaluation result.
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