CN111025288B - Security radar monitoring device and system - Google Patents
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- CN111025288B CN111025288B CN201911374455.9A CN201911374455A CN111025288B CN 111025288 B CN111025288 B CN 111025288B CN 201911374455 A CN201911374455 A CN 201911374455A CN 111025288 B CN111025288 B CN 111025288B
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/886—Radar or analogous systems specially adapted for specific applications for alarm systems
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
- G01S13/04—Systems determining presence of a target
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- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
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Abstract
The invention is suitable for the technical field of radar monitoring, and provides a security radar monitoring device and a system, wherein the device comprises: the data acquisition module acquires data in the area range of the security radar; the target identification module is used for filtering interference data in the data according to the data acquired by the data acquisition module to obtain target data; determining a corresponding radar working model according to the target data; the configuration module extracts parameter values based on the radar working model, the security radar is reinitialized according to the parameter values, and the data acquisition module carries out regional environment monitoring by adopting the reinitialized security radar, so that the security radar can work in an optimal working mode suitable for the current scene, the influence on the performance of the security radar due to weather and climate change can be reduced, the occurrence probability of false alarm and missed alarm of the security radar is reduced, and the monitored data is more accurate.
Description
Technical Field
The invention belongs to the technical field of radar monitoring, and particularly relates to a security radar monitoring device and system.
Background
With the continuous development of science and technology, electronic precaution technology is rapidly developed as an important development direction of security technology. The security radar is a new means which is raised in the security market. The security radar provides an early warning function by finding and identifying targets in an illegal intrusion alert range, and can provide all-time and all-weather security guarantee for the surrounding environment of important facility places.
When the current domestic commercial security radar is applied, the weather environment can influence the radar performance: storm can affect radar monitoring distance; the influence of the severe haze weather on the radar detection target caused by false-miss, the strong wind blowing trees caused by false-alarm and the like cause the reduction of the performance of the radar and the accuracy of monitoring data.
Disclosure of Invention
In view of this, embodiments of the present invention provide a security radar monitoring device and system, so as to solve the problem in the prior art that the weather environment causes the performance of a radar and the accuracy of monitoring data to be reduced.
A first aspect of an embodiment of the present invention provides a security radar monitoring apparatus, including: the system comprises a data acquisition module, a target identification module and a configuration module;
the data acquisition module is used for acquiring data in the area range of the security radar;
the target identification module is used for filtering interference data in the data according to the data acquired by the data acquisition module to obtain target data; determining a corresponding radar working model according to the target data;
the configuration module is used for extracting parameter values based on the radar working model, reinitializing the security radar according to the parameter values, and monitoring the regional environment by the data acquisition module through the reinitialized security radar.
In one embodiment, the object recognition module comprises: the system comprises a data purification unit and a Support Vector Machine (SVM) recognition unit;
the data purifying unit is used for processing the one-dimensional fast Fourier transform FFT data acquired by the data acquisition module to obtain data information of each CFAR point in each cluster in each frame of data; filtering data corresponding to the interference target in the data information of each CFAR point in each cluster in each frame of data according to the characteristic information of the target and the interference target to obtain target data;
and the SVM recognition unit is used for marking the data type of the target data and processing the target data marked with the data type to obtain a corresponding radar working model.
In one embodiment, the object recognition module further comprises: a decision-making unit is identified;
the identification decision unit is used for calculating to obtain decision weight of each frame according to continuous N frames of data of the placed radar, performing weighted summation calculation on each type of target in the continuous N frames of data based on the decision weight of each frame to obtain probability of each type of target, and determining that the placed radar is a target radar of a type of target corresponding to the maximum probability, wherein N is a positive integer greater than 0.
In an embodiment, the processing the one-dimensional FFT data acquired by the data acquisition module to obtain data information of each CFAR point in each cluster in each frame of data includes:
performing data analysis on the one-dimensional FFT data acquired by the data acquisition module to obtain analyzed data;
performing signal processing on the analyzed data to obtain two-dimensional FFT data;
and performing Constant False Alarm Rate (CFAR) detection and clustering processing on the two-dimensional FFT data to obtain data information of each CFAR point in each cluster in each frame of data.
In an embodiment, the filtering, according to the feature information of the target and the interfering target, data corresponding to the interfering target in the data information of each CFAR point in each cluster in each frame of data to obtain target data includes:
determining different characteristic information of a target and an interference target according to the characteristic information of the target and the interference target in a data acquisition scene;
and filtering data corresponding to the interference target corresponding to the different characteristic information in the data information of each CFAR point in each cluster in each frame of data according to the different characteristic information to obtain target data.
In an embodiment, the data type tagging of the target data includes:
and marking the data type of the target data according to the angle, the distance and the running speed of the target relative to the security radar.
In an embodiment, the processing the target data after the data type marking to obtain a corresponding radar working model includes:
performing primary feature extraction on the target data marked by the data types to obtain data information of each group of feature quantities;
performing characteristic selection on the data information of each group of characteristic quantities to obtain a radar working data model;
and obtaining a corresponding radar working model according to the radar working data model and each working data model stored in the model library.
In an embodiment, the performing feature selection on the data information of each group of feature quantities to obtain a radar working data model includes:
and eliminating coarse error data in the data information of each group of characteristic quantities, and performing normalization processing on each group of characteristic quantities with the coarse error data eliminated to obtain a radar working data model.
In an embodiment, the obtaining a corresponding radar working model according to the radar working data model and each working data model stored in a model library includes:
determining working data models corresponding to M types of targets according to the radar working data model and each working data model stored in a model library, and generating probability analysis of each working data model, wherein N is a positive integer greater than 0;
and determining a working data model corresponding to the type target with the maximum probability in probability analysis as a radar working model corresponding to the security radar.
A second aspect of an embodiment of the present invention provides a security radar monitoring system, including: the security radar monitoring device of any one of the above embodiments.
Compared with the prior art, the embodiment of the invention has the following beneficial effects: filtering interference data in the data according to the data acquired by the data acquisition module through a target identification module to obtain target data; determining a corresponding radar working model according to the target data; the configuration module is used for extracting parameter values based on the radar working model, reinitializing the security radar according to the parameter values, and monitoring the regional environment by the data acquisition module by adopting the reinitialized security radar, so that the security radar can work in an optimal working mode suitable for the current scene, the influence on the performance of the security radar due to the change of weather and climate can be reduced by identifying the environment, the probability of occurrence of false alarm and false missing alarm of the security radar is reduced, the monitored data is more accurate, and the problem that the weather environment causes the reduction of the performance of the radar and the accuracy of the monitored data in the prior art is solved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
FIG. 1 is a schematic diagram of a security radar monitoring apparatus provided in an embodiment of the present invention;
FIG. 2 is a schematic diagram of a target recognition module provided by an embodiment of the present invention;
FIG. 3 is a schematic flow chart of obtaining a radar working model according to an embodiment of the present invention;
FIG. 4 is an exemplary diagram of an object recognition module provided by another embodiment of the invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
In order to explain the technical means of the present invention, the following description will be given by way of specific examples.
Fig. 1 is a schematic view of a security radar monitoring device provided in an embodiment of the present invention, which is detailed as follows.
A security radar monitoring device may include: a data acquisition module 101, a target identification module 102 and a configuration module 103;
the data acquisition module 101 is used for acquiring data in a security radar area range;
the target identification module 102 is configured to filter interference data in the data according to the data acquired by the data acquisition module to obtain target data; determining a corresponding radar working model according to the target data;
the configuration module 103 is configured to extract a parameter value based on the radar working model, reinitialize the security radar according to the parameter value, and perform regional environment monitoring by using the reinitialized security radar by the data acquisition module 101.
Optionally, Fast Fourier Transform (FFT) may be performed on the data acquired by the data acquisition module 101 to obtain one-dimensional FFT data, and then the target identification module 102 further processes the one-dimensional FFT data.
Optionally, as shown in fig. 2, the object identifying module 102 includes: a data cleansing unit 1021, and a Support Vector Machine (SVM) recognition unit 1022.
The data purification unit 1021 is used for processing the one-dimensional FFT data acquired by the data acquisition module to obtain data information of each CFAR point in each cluster in each frame of data; filtering data corresponding to the interference target in the data information of each CFAR point in each cluster in each frame of data according to the characteristic information of the target and the interference target to obtain target data;
the SVM recognition unit 1022 is configured to perform data type tagging on the target data, and process the data type tagged target data to obtain a corresponding radar working model.
Optionally, the data purifying unit 1021 processes the one-dimensional fast fourier transform FFT data acquired by the data acquisition module by using data processing software. Data processing software is used in high-level technical computing languages and interactive environments for algorithm development, data visualization, data analysis, and numerical computation. In this embodiment, each cluster CFAR point data after the primary processing is obtained through data processing by data processing software. Wherein, the data processing process is as follows: and obtaining data information of each CFAR point in each cluster frame by frame through data analysis, signal processing and data processing processes of the original one-dimensional FFT data acquired by the security radar.
Optionally, the processing, by the data purifying unit 1021, of the one-dimensional FFT data acquired by the data acquisition module to obtain data information of each CFAR point in each cluster in each frame of data may include: the one-dimensional FFT data acquired by the data acquisition module is subjected to data analysis to obtain analyzed data, and it should be noted that the one-dimensional FFT data is data that includes a frame header but is not analyzed, so that the one-dimensional FFT data needs to be subjected to data analysis first.
Then, signal processing is carried out on the analyzed data to obtain two-dimensional FFT data; and then, performing Constant False Alarm Rate (CFAR) and clustering processing on the two-dimensional FFT data to obtain data information of each CFAR point in each cluster in each frame of data.
CFAR is a technique for determining whether a target signal exists by a radar system by discriminating a signal and noise output by a receiver under the condition of keeping a false alarm probability constant. Optionally, the constant false alarm detector first processes the input noise and then determines a threshold, compares the threshold with the input end signal, and determines that there is a target if the input end signal exceeds the threshold, or determines that there is no target if the input end signal exceeds the threshold. The signal is transmitted from the signal source, and is subjected to various interferences in the process of propagation, and the signal is processed after reaching the receiver and output to the detector, and then the detector makes a decision on the input signal according to a proper criterion. In the present embodiment, data of each CFAR point in each frame of data is obtained by processing two-dimensional FFT data with CFAR.
Optionally, the data purifying unit 1021 filters, according to the feature information of the target and the interference target, data corresponding to the interference target in the data information of each CFAR point in each cluster in each frame of data, to obtain target data, and may include:
determining different characteristic information of a target and an interference target according to the characteristic information of the target and the interference target in a data acquisition scene; and filtering data corresponding to interference targets corresponding to the different characteristic information in the data information of each CFAR point in each cluster in each frame of data according to the different characteristic information to obtain target data.
Optionally, in this embodiment, the target may be a three-class target: people, cars, and grass. The characteristic information of the target and the interference target can be angle, radial speed and other characteristic quantity information.
The interference data are intelligently removed through the data purification unit according to the acquired data scene, the target data are obtained, a good data base is provided for the next target identification, and the identification effect is indirectly improved.
The SVM recognition unit 1022 performs data type labeling on the target data, including: and marking the data type of the target data according to the angle, the distance and the running speed of the target relative to the security radar. Optionally, the target data is data processed by data processing software, and a clustering scatter diagram can be formed, which is more beneficial to data type marking. The data type flag may provide underlying data for subsequent feature analysis.
Optionally, as shown in fig. 3, the processing, by the SVM recognition unit 1022, of the target data after the data type marking to obtain a corresponding radar working model may include the following steps.
Step 301, performing preliminary feature extraction on the target data marked by the data type to obtain data information of each group of feature quantities.
Optionally, the target data after marking the data type includes seven groups of feature data, including distance, speed, angle, amplitude, signal-to-noise ratio, horizontal coordinate, and vertical coordinate of each CFAR point in each cluster of each frame of data, and then performing preliminary feature extraction on the target data after marking the data type may be to process the seven groups of feature data to obtain a mean value, a variance, a standard deviation, a polar deviation, and a maximum value of each group of feature quantity, that is, the data information of each group of feature quantity includes the mean value, the variance, the standard deviation, the polar deviation, and the maximum value of the feature quantity.
After the initial feature extraction, it is generally not possible to determine that the obtained data information of each group of feature quantities is a reliable classification scheme, and further feature selection is required to obtain a radar working data model through the feature selection.
And 302, performing feature selection on the data information of each group of feature quantities to obtain a radar working data model.
Optionally, the present step includes: and eliminating coarse error data in the data information of each group of characteristic quantities, and performing normalization processing on each group of characteristic quantities with the coarse error data eliminated to obtain a radar working data model.
Alternatively, coarse error data is referred to as coarse error for short, and refers to an error that is significantly beyond the expected error under the specified conditions, and thus is an error that significantly distorts the measurement result, so that the existence of coarse error data seriously affects the matching of the radar working data model, and therefore needs to be eliminated.
And 303, obtaining a corresponding radar working model according to the radar working data model and each working data model stored in the model library.
Optionally, this step may include: determining working data models corresponding to the M types of targets according to the radar working data model and each working data model stored in a model library, and generating probability analysis of each working data model; and determining a working data model corresponding to the type target with the maximum probability in probability analysis as a radar working model corresponding to the security radar.
Optionally, the model library stores work data models corresponding to a plurality of work scenes, for example, different work data models corresponding to a storm work scene, a haze weather work scene, or a snow work scene.
Optionally, the probability analysis is to output probabilities of identifying as some types of targets respectively. For example, a working data model corresponding to the type target in 3 is determined, then probability analysis is performed to obtain 3 probabilities, wherein the probabilities are A, B and C, the model with the highest probability is used as a finally determined radar working model, then a configuration module extracts parameter values based on the radar working model, the security radar is reinitialized according to the parameter values, and the data acquisition module adopts the reinitialized security radar to perform regional environment monitoring, so that the data monitored by the security radar is more matched with the current working scene. After the current working model is changed, the radar automatically extracts the relevant values in the database to reinitialize the radar, and the radar is ensured to work in the optimal working mode suitable for the current scene. The influence on the performance of the security radar due to the change of weather and climate can be reduced by identifying the environment, so that the probability of occurrence of false alarm and missing alarm of the security radar is reduced, and the monitored data is more accurate.
Optionally, as shown in fig. 4, the object identifying module 102 further includes: the decision unit 1023 is identified.
The identification decision unit 1023 is configured to calculate a decision weight of each frame according to the continuous N frames of data on which the radar is placed, perform weighted summation calculation on each type of target in the continuous N frames of data on the basis of the decision weight of each frame to obtain a probability of each type of target, and determine that the radar is a target radar on which the radar is placed and which is a category target corresponding to the maximum probability, where N is a positive integer greater than 0.
Optionally, the recognition decision unit 1023 may be embedded in the radar data track algorithm. For example, if N is 5, the target radar may be determined according to five consecutive frames of data. The decision weight is more reliant on the most recent frame number, i.e. the closer the frame number is to the recognition frame the greater the decision weight. For example, if the fifth frame is selected as the identification frame, the decision weight ratio of the first frame to the fifth frame may be 1:2:3:4: 5. And calculating each type of target in each frame in the continuous N frames of data to obtain the probability corresponding to the target of each type. The maximum probability is the target radar of the flight path, for example, the probability of the human being in the category of target is the maximum, then the human being in the category of target is the target radar of the flight path, and the flight path category can not be identified any more subsequently.
The identification decision unit can identify the target radar only by a few continuous frames of data of the radar instead of calculating each frame of data, so that the operation amount can be greatly reduced, and the identification effect is improved.
Optionally, after the target radar is determined, the configuration module extracts a parameter value according to a working model corresponding to the target radar, reinitializes the security radar according to the parameter value, and performs the regional environment monitoring by using the reinitialized security radar by the data acquisition module.
According to the security radar monitoring device, interference data can be eliminated through the data purification unit in the target identification module, target data are obtained, a good data base is provided for next target identification, and the identification effect is indirectly improved; the identification decision unit can identify the target radar only by continuous several frames of data, and does not calculate each frame of data, so that the operation amount can be greatly reduced, and the target identification effect is improved. The influence on the performance of the radar caused by the change of weather and climate can be reduced by identifying the environment, so that the probability of occurrence of false alarm and false alarm of the security radar is reduced. And after the current working model is changed, the security radar automatically extracts relevant parameter values in the model library to reinitialize the security radar, so that the security radar is ensured to work in the optimal working mode suitable for the current scene. The influence on the performance of the security radar due to the change of weather and climate can be reduced by identifying the environment, so that the probability of occurrence of false alarm and missing alarm of the security radar is reduced, and the monitored data is more accurate.
The embodiment of the invention also provides a security radar monitoring system which comprises the security radar monitoring device in any embodiment and has the beneficial effects brought by the security radar monitoring device in any embodiment.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein.
Claims (6)
1. The utility model provides a security protection radar monitoring devices which characterized in that includes: the system comprises a data acquisition module, a target identification module and a configuration module;
the data acquisition module is used for acquiring data in the area range of the security radar;
the target identification module is used for filtering interference data in the data according to the data acquired by the data acquisition module to obtain target data; determining a corresponding radar working model according to the target data; the target recognition module comprises a Support Vector Machine (SVM) recognition unit, and the SVM recognition unit is used for marking the data type of the target data and processing the target data marked by the data type to obtain a corresponding radar working model;
the processing of the target data marked by the data type to obtain the corresponding radar working model comprises the following steps: performing primary feature extraction on the target data marked by the data types to obtain data information of each group of feature quantities; performing characteristic selection on the data information of each group of characteristic quantities to obtain a radar working data model; obtaining a corresponding radar working model according to the radar working data model and each working data model stored in a model library;
the step of performing feature selection on the data information of each group of feature quantities to obtain a radar working data model comprises the following steps: removing coarse error data in the data information of each group of characteristic quantities, and performing normalization processing on each group of characteristic quantities with the coarse error data removed to obtain a radar working data model;
the obtaining of the corresponding radar working model according to the radar working data model and each working data model stored in the model base includes:
determining working data models corresponding to M types of targets according to the radar working data model and each working data model stored in a model library, and generating probability analysis of each working data model, wherein M is a positive integer greater than 0; determining a working data model corresponding to the type target with the highest probability in probability analysis as a radar working model corresponding to the security radar;
the object recognition module further comprises: a decision-making unit is identified;
the identification decision unit is used for calculating decision weight of each frame according to continuous N frames of data of the security radar, performing weighted summation calculation on each type of target in the continuous N frames of data based on the decision weight of each frame to obtain probability of each type of target, and determining that the security radar is a target radar of a type target corresponding to the maximum probability, wherein N is a positive integer greater than 0;
the configuration module is used for extracting parameter values based on the radar working model, reinitializing the security radar according to the parameter values, and monitoring the regional environment by the data acquisition module through the reinitialized security radar.
2. The security radar monitoring device of claim 1, wherein the target identification module further comprises: a data cleansing unit;
the data purification unit is used for processing the one-dimensional Fast Fourier Transform (FFT) data acquired by the data acquisition module to obtain data information of each CFAR point in each cluster in each frame of data; and filtering data corresponding to the interference target in the data information of each CFAR point in each cluster in each frame of data according to the characteristic information of the target and the interference target to obtain target data.
3. The security radar monitoring device of claim 2, wherein the processing of the one-dimensional FFT data collected by the data collection module to obtain data information for each CFAR point in each cluster in each frame of data comprises:
performing data analysis on the one-dimensional FFT data acquired by the data acquisition module to obtain analyzed data;
performing signal processing on the analyzed data to obtain two-dimensional FFT data;
and performing Constant False Alarm Rate (CFAR) detection and clustering processing on the two-dimensional FFT data to obtain data information of each CFAR point in each cluster in each frame of data.
4. The security radar monitoring device of claim 3, wherein the filtering, according to the characteristic information of the target and the interference target, data corresponding to the interference target in the data information of each CFAR point in each cluster in each frame of data to obtain target data comprises:
determining different characteristic information of a target and an interference target according to the characteristic information of the target and the interference target in a data acquisition scene;
and filtering data corresponding to the interference target corresponding to the different characteristic information in the data information of each CFAR point in each cluster in each frame of data according to the different characteristic information to obtain target data.
5. The security radar monitoring device as recited in claim 2 or claim 4, wherein said data type tagging the target data comprises:
and marking the data type of the target data according to the angle, the distance and the running speed of the target relative to the security radar.
6. A security radar monitoring system, comprising the security radar monitoring device of any one of claims 1 to 5.
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| CN108919247A (en) * | 2018-03-30 | 2018-11-30 | 中国科学院电子学研究所 | A kind of multiple target physical examination survey and localization method based on constant false alarm rate detection |
| CN109977885A (en) * | 2019-03-29 | 2019-07-05 | 成都纳雷科技有限公司 | A kind of people's vehicle automatic identifying method and device based on Doppler Feature |
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| CN110456321A (en) * | 2019-08-21 | 2019-11-15 | 森思泰克河北科技有限公司 | Filter out method, terminal device and the storage medium of radar wrong report |
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