WO2020052441A1 - Procédé de classification de cible et dispositif associé - Google Patents

Procédé de classification de cible et dispositif associé Download PDF

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Publication number
WO2020052441A1
WO2020052441A1 PCT/CN2019/103227 CN2019103227W WO2020052441A1 WO 2020052441 A1 WO2020052441 A1 WO 2020052441A1 CN 2019103227 W CN2019103227 W CN 2019103227W WO 2020052441 A1 WO2020052441 A1 WO 2020052441A1
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WIPO (PCT)
Prior art keywords
signal
target
energy
micro
motion
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PCT/CN2019/103227
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English (en)
Chinese (zh)
Inventor
王晓
张磊
陈熠
刘康
Original Assignee
深圳市道通智能航空技术有限公司
道通智能航空技术欧洲有限责任公司
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Publication of WO2020052441A1 publication Critical patent/WO2020052441A1/fr

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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
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
    • G01S7/411—Identification of targets based on measurements of radar reflectivity
    • 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
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section

Definitions

  • the present application relates to the technical field of target classification, and in particular, to a target classification method and related equipment.
  • Radar can be applied in many fields of industry, such as automotive electronics, drones, etc. Radar can achieve different functions in different fields. Such as radar can achieve ranging, angle measurement, speed measurement, altimetry and other functions.
  • radar can be divided into a variety of radars such as lidar, millimeter wave radar. The accuracy measured by each radar is different.
  • the embodiments of the present application provide a target classification method and related equipment, which can implement accurate classification of long-distance targets by using radar.
  • an embodiment of the present application provides a target classification method, including:
  • the related features of the target include micro-motion features
  • the classifying the target based on the related characteristics of the target includes:
  • the target is classified as a target in a micro-motion state or a target in a non-micro-motion state.
  • the micro-motion feature includes at least one of the following:
  • the range entropy feature is used to represent the uncertainty of the distance between the target and the radar
  • the noise energy ratio feature is used to represent a ratio of the energy of the noise signal to the energy of the Doppler signal in the Doppler signal;
  • the fretting energy ratio feature is used to represent the energy of the fretting signal and the energy of the Doppler signal in the Doppler signal, or the ratio of the fretting energy to the energy of the noise signal .
  • obtaining the relevant characteristics of the target based on the echo signal includes:
  • the echo signal Processing the echo signal to obtain a two-dimensional signal; wherein the two-dimensional signal includes a distance signal and a Doppler signal; the Doppler signal includes a micro motion signal and a noise signal;
  • obtaining the micro motion characteristics of the target based on the two-dimensional signal includes:
  • formula (1) is:
  • feature1 is used to represent the distance entropy feature
  • M is used to represent the number of frames of the acquired echo signal
  • k is used to represent the frame number of the acquired echo signal
  • c (k) is used to represent the echo of the kth frame
  • c (k) is determined according to formula (2);
  • formula (2) is:
  • N is used to indicate the number of frames included in the calculation window
  • n is used to indicate any frame in the calculation window
  • range (k) is used to indicate the distance value represented by the distance signal in the echo signal of the k-th frame
  • Range (n) is used to represent a distance value represented by a distance signal in an echo signal of the n-th frame in the calculation window; wherein N is an integer greater than or equal to 1.
  • obtaining the micro motion characteristics of the target based on the two-dimensional signal includes:
  • obtaining the micro motion characteristics of the target based on the two-dimensional signal includes:
  • Determining the target according to a ratio of the energy of the micro-motion signal to the energy of the Doppler signal, or the ratio of the energy of the micro-motion signal to the energy of the noise signal in the Doppler signal The characteristics of the fretting energy ratio.
  • the classifying the target according to related characteristics of the target includes:
  • the classification parameter formula is obtained by training any one of the following classifiers:
  • Support vector machine SVM
  • correlation vector machine RVM
  • KNN K nearest neighbor classification algorithm
  • the relevant characteristics of the target include RCS characteristics
  • the RCS feature is used to indicate a reflection degree of a target to the radar signal.
  • an embodiment of the present application provides a target classification device, including:
  • a transceiver module for transmitting a radar signal to detect a target in the environment; obtaining an echo signal of the target based on the radar signal feedback;
  • a processing module configured to obtain relevant characteristics of the target according to the echo signal; and classify the target according to the relevant characteristics of the target.
  • the related characteristics of the target include micro-motion characteristics
  • the processing module classifies the target according to the related characteristics of the target, including:
  • the target is classified as a target in a micro-motion state or a target in a non-micro-motion state.
  • the micro-motion feature includes at least one of the following:
  • the range entropy feature is used to represent the uncertainty of the distance between the target and the radar
  • the noise energy ratio feature is used to represent a ratio of the energy of the noise signal to the energy of the Doppler signal in the Doppler signal;
  • the fretting energy ratio feature is used to represent the energy of the fretting signal and the energy of the Doppler signal in the Doppler signal, or the ratio of the fretting energy to the energy of the noise signal .
  • the processing module obtains relevant characteristics of the target according to the echo signal, including:
  • the echo signal Processing the echo signal to obtain a two-dimensional signal; wherein the two-dimensional signal includes a distance signal and a Doppler signal; the Doppler signal includes a micro motion signal and a noise signal;
  • the processing module obtains the micro-motion characteristics of the target according to the two-dimensional signal, including:
  • formula (1) is:
  • feature1 is used to represent the distance entropy feature
  • M is used to represent the number of frames of the acquired echo signal
  • k is used to represent the frame number of the acquired echo signal
  • c (k) is used to represent the echo of the kth frame
  • c (k) is determined according to formula (2);
  • formula (2) is:
  • N is used to indicate the number of frames included in the calculation window
  • n is used to indicate any frame in the calculation window
  • range (k) is used to indicate the distance value represented by the distance signal in the echo signal of the k-th frame
  • Range (n) is used to represent a distance value represented by a distance signal in an echo signal of the n-th frame in the calculation window; wherein N is an integer greater than or equal to 1.
  • the processing module obtains the micro-motion characteristics of the target according to the two-dimensional signal, including:
  • the processing module obtains the micro-motion characteristics of the target according to the two-dimensional signal, including:
  • Determining the target according to a ratio of the energy of the micro-motion signal to the energy of the Doppler signal, or the ratio of the energy of the micro-motion signal to the energy of the noise signal in the Doppler signal The characteristics of the fretting energy ratio.
  • the processing module classifies the target according to related characteristics of the target, including:
  • the classification parameter formula is obtained by training any one of the following classifiers:
  • Support vector machine SVM
  • correlation vector machine RVM
  • KNN K nearest neighbor classification algorithm
  • the relevant characteristics of the target include RCS characteristics
  • the RCS feature is used to indicate a reflection degree of a target to the radar signal.
  • an embodiment of the present application provides a radar, including:
  • a processor connected to the transmitter and the receiver;
  • a memory connected to the processor
  • the transmitter is used for transmitting a radar signal to detect a target in the environment
  • the receiver is configured to acquire an echo signal of the target based on the radar signal feedback
  • the processor is configured to execute a computer program stored in the memory to implement the following steps:
  • the related features of the target include micro-motion features
  • the processor is configured to implement the classification of the target based on the related features of the target, and is specifically used to implement:
  • the target is classified as a target in a micro-motion state or a target in a non-micro-motion state.
  • the micro-motion feature includes at least one of the following:
  • the range entropy feature is used to represent the uncertainty of the distance between the target and the radar
  • the noise energy ratio feature is used to represent a ratio of the energy of the noise signal to the energy of the Doppler signal in the Doppler signal;
  • the fretting energy ratio feature is used to represent the energy of the fretting signal and the energy of the Doppler signal in the Doppler signal, or the ratio of the fretting energy to the energy of the noise signal .
  • the processor when the processor is configured to obtain relevant characteristics of the target according to the echo signal, the processor is specifically configured to implement:
  • the echo signal Processing the echo signal to obtain a two-dimensional signal; wherein the two-dimensional signal includes a distance signal and a Doppler signal; the Doppler signal includes a micro motion signal and a noise signal;
  • the processor is configured to obtain the micro-motion feature of the target according to the two-dimensional signal, and is specifically configured to implement:
  • formula (1) is:
  • feature1 is used to indicate the distance entropy feature
  • M is used to indicate the number of frames of the acquired echo signal
  • k is used to indicate the frame number of the acquired echo signal
  • c (k) is used to indicate the echo of the kth frame.
  • c (k) is determined according to formula (2);
  • formula (2) is:
  • N is used to indicate the number of frames included in the calculation window
  • n is used to indicate any frame in the calculation window
  • range (k) is used to indicate the distance value represented by the distance signal in the echo signal of the k-th frame
  • Range (n) is used to represent a distance value represented by a distance signal in an echo signal of the n-th frame in the calculation window; wherein N is an integer greater than or equal to 1.
  • the processor is configured to obtain the micro-motion feature of the target according to the two-dimensional signal, and is specifically configured to implement:
  • the processor is configured to obtain the micro-motion feature of the target according to the two-dimensional signal, and is specifically configured to implement:
  • Determining the target according to a ratio of the energy of the micro-motion signal to the energy of the Doppler signal, or the ratio of the energy of the micro-motion signal to the energy of the noise signal in the Doppler signal The characteristics of the fretting energy ratio.
  • the processor is configured to classify the target according to related characteristics of the target, and is specifically configured to implement:
  • the classification parameter formula is obtained by training any one of the following classifiers:
  • Support vector machine SVM
  • correlation vector machine RVM
  • KNN K nearest neighbor classification algorithm
  • the relevant characteristics of the target include RCS characteristics
  • the RCS feature is used to indicate a reflection degree of a target to the radar signal.
  • an embodiment of the present application provides a readable storage medium, characterized in that a computer program is stored in the readable storage medium, and when the computer program is executed by a processor, the first aspect may be implemented Either method.
  • an object in the environment can be detected and an echo signal of the target based on the radar signal feedback can be obtained.
  • relevant characteristics of the target can be obtained, and The related features of the target classify the target.
  • the above method can realize the classification of the target in the case of being far away from the target.
  • FIG. 1 is a schematic diagram of an application scenario according to an embodiment of the present application.
  • FIG. 2 is a schematic structural diagram of a radar according to an embodiment of the present application.
  • FIG. 3 is a schematic flowchart of a target classification method according to an embodiment of the present application.
  • FIG. 4 is a schematic flowchart of another target classification method according to an embodiment of the present application.
  • FIG. 5 is a schematic diagram of a module composition of a target classification device according to an embodiment of the present application.
  • the radar may be mounted on an aircraft, and the aircraft may be an Unmanned Aerial Vehicle (UAV) or other aircraft.
  • UAV Unmanned Aerial Vehicle
  • the radar 100 can be installed on the bottom of the aircraft 102, which can be used to detect the environmental situation of the landing site 104, and can use the landing site as a target to classify it. For example, classify landing sites as ground or water. Therefore, the aircraft can be notified of the classification result, so that the aircraft can adjust the landing place and avoid falling into the water. Can improve the intelligence of aircraft landing.
  • the radar can also achieve other functions, such as the aircraft performing altimetry on the air.
  • the radar can be installed on the vehicle to detect targets in the surrounding environment of the vehicle and classify the targets.
  • the radar can recognize the targets in the surrounding environment of the vehicle as roadblocks, railings, people, etc.
  • the following describes a radar provided by an embodiment of the present application to implement a target classification method.
  • FIG. 2 is a schematic structural diagram of a radar system according to an embodiment of the present application.
  • the radar system 200 may include a processor 201, a transmitter 203, a receiver 205, a bus 207, an interface 209, a memory 211, and the like.
  • the processor 201 is connected to the transmitter 203, the receiver 205, the memory 211, the interface 109, and the power supply system 207, respectively.
  • the power supply system 207 can also be connected to other modules outside the processor 201 according to design requirements.
  • each module may be connected to other modules except the processor 201 according to design requirements, which is not limited herein.
  • the transmitter 203 may be connected with a transmitting antenna, and the transmitting antenna may be an antenna array or other antenna forms applied to a radar, which is not limited herein.
  • the transmitter is used to transmit radar signals.
  • the transmitter can transmit lidar signals, millimeter-wave radar signals, and the like.
  • the transmitter can be used to transmit millimeter-wave radar signals at 77 GHz, 24 GHz, or other frequency bands, which is not limited herein.
  • the transmitter 203 may include a transmission control unit, which is used to control instruction interaction with the processor 201, and may also control the transmitter to transmit radar signals.
  • a transmission control unit which is used to control instruction interaction with the processor 201, and may also control the transmitter to transmit radar signals.
  • the receiver 205 may be connected with a receiving antenna.
  • the receiving antenna may be an antenna array or other antenna forms applied to a radar, which is not limited herein.
  • the receiver 205 is configured to receive an echo signal of the radar signal transmitted by the transmitter 203 after being reflected by the target.
  • the information carried in the echo signal can be used to reflect the characteristics, attributes, and motion characteristics of the target.
  • the receiver 205 may receive an echo signal through a single channel or multiple channels.
  • the receiver 205 may include a receiving control unit, which is used to control the implementation of the instruction interaction with the processor 201, and may also control the receiver to receive an echo signal and the like.
  • a receiving control unit which is used to control the implementation of the instruction interaction with the processor 201, and may also control the receiver to receive an echo signal and the like.
  • the receiver 205 may further include a processor for further processing the received echo signal, for example, processing the echo signal into a two-dimensional signal; or the receiver may send the echo signal to the processor Further processing is performed in the signal processor 2011 in 201, and the embodiment of the present application is not limited herein.
  • the transmitter 203 and the receiver 205 may be independent devices, or the transmitter 203 and the receiver 205 may be integrated into one device as a front end of the radar system 200.
  • the processor 201 may include a signal processor 2011 and a data processor 2013.
  • the signal processor 2011 is used to process the echo signal
  • the data processor 2013 is used to further process the processed echo signal to achieve classification of the target.
  • functions implemented by the signal processor 2011 and functions implemented by the data processor 2013 may be implemented by independent processors or jointly by the processors, which are not limited herein.
  • the processor may include a digital signal processor (Digital Signal Processing, DSP), a micro processor (Micro Processing Unit, MCU), an advanced reduced instruction set machine (Advanced RISC Machine, ARM), and the like.
  • DSP Digital Signal Processing
  • MCU Micro Processing Unit
  • ARM Advanced reduced instruction set machine
  • the processor may refer to a processor core or a processor chip.
  • the above processor or processors may be implemented by a hardware chip.
  • the hardware chip may be an application-specific integrated circuit (English: application-specific integrated circuit (abbreviation: ASIC)), a programmable logic device (English: programmable logic device (abbreviation: PLD)), or a combination thereof.
  • the PLD may be a complex programmable logic device (English: complex programmable device, abbreviation: CPLD), a field programmable logic gate array (English: field-programmable gate array, abbreviation: FPGA), general array logic (English: generic array) logic, abbreviation: GAL) or any combination thereof.
  • the transmitter 203, the receiver 205, and the processor 201 may be integrated into one hardware chip, or each may be implemented by an independent hardware chip, which is not limited herein.
  • the power system 207 may include a power source and a power management module.
  • the power supply can supply power to each module in the radar 200, and the power management module can be used to manage and control the power supply of each module.
  • the interface 209 is used to enable the radar system 200 to communicate with other equipment or devices.
  • the radar system 200 can transmit the target classification result to other devices or devices through the interface 209, so that other devices or devices can implement other functions based on the target classification results.
  • the radar system 200 when the radar system 200 is installed in an aircraft, the radar system 200 may be connected to a flight control system, a main control system, or other control systems in the aircraft through an interface 209.
  • the radar system 200 is connected to the main control system through the interface 209 as an example.
  • the radar system 200 can transmit the target classification result to the main control system through the interface 209.
  • the main control system can further determine whether the detected target is suitable for landing, or whether it needs to avoid the detected target. And can further control the aircraft to achieve the above functions.
  • the interface 209 may include a serial peripheral interface (SPI) 2091, a controller area network (CAN) 2093, a universal asynchronous transmission interface (Universal Receiver / Transmitter, UART) 2095 Wait.
  • SPI serial peripheral interface
  • CAN controller area network
  • UART universal asynchronous transmission interface
  • the interface 209 may also include other communication interfaces or input / output interfaces, which is not limited herein.
  • the memory 211 may include volatile memory (English: volatile memory), such as random access memory (English: random-access memory, abbreviation: RAM), such as static random access memory (English: static-access memory, abbreviation: SRAM), double data rate synchronous dynamic random access memory (English: Double Data Rate Synchronous Dynamic Random Access Memory, abbreviation: DDR SDRAM), etc .; the memory can also include non-volatile memory (English: non-volatile memory), For example, flash memory (English: flash memory), hard disk (English: hard disk drive (abbreviation: HDD)) or solid-state hard disk (English: solid-state drive (abbreviation: SSD)); the memory 211 may also include a combination of the above types of memory .
  • volatile memory such as random access memory (English: random-access memory, abbreviation: RAM), such as static random access memory (English: static-access memory, abbreviation: SRAM), double data rate synchronous dynamic random access memory (English: Double Data Rate Synchronous Dynamic Random Access Memory,
  • the memory may be an independent memory, or may be a memory inside a chip (such as a processor chip) or a module having a storage function.
  • the memory may store computer programs (such as application programs, functional modules), computer instructions, operating systems, data, databases, and the like.
  • the memory can be partitioned.
  • the radar system 200 may also include other components.
  • the radar system 200 may further include a classification trainer, etc., to implement online training of a classification model.
  • the other components included in the radar system 200 will not be repeated here.
  • FIG. 3 is a schematic flowchart of a target classification method according to an embodiment of the present application. As shown in FIG. 3, the method includes the following steps.
  • step S301 a radar signal is transmitted to detect a target in the environment.
  • Step S302 Acquire an echo signal of the target based on the radar signal feedback.
  • the radar system may transmit a radar signal to the environment through the above-mentioned transmitter and transmitting antenna, so as to detect a target in the environment, and may receive an echo signal of the target based on the radar signal reflected through the receiving antenna and the receiver, and This echo signal can be sent to a processor for further processing.
  • radar can detect targets at a long distance without being affected by ambient light.
  • Step S303 Obtain the relevant characteristics of the target according to the echo signal.
  • the radar can calculate the relevant characteristics of the target according to the echo signal.
  • the relevant characteristics of the target can be used to classify the target. That is, the relevant characteristics of the target can be used to reflect the motion state of the target, the electromagnetic characteristics of the target, and the target attributes. Furthermore, the target can be classified based on the relevant characteristics of the target.
  • the relevant characteristics of the target may include a micro-motion characteristic, and through the micro-motion characteristic, it can be determined whether the target is in a micro-motion state, and then the target can be classified into a target in a micro-motion state and a target in a non-micro-motion state.
  • the micro-motion can be understood as the movement of the target in addition to its own movement, or the movement of local components on the target, such as the rotation of the propeller of an aircraft, the swing of an arm back and forth when a person walks, and the like. If the target is in the micro-motion state, it indicates that the target has micro-motion.
  • the micro-motion state may include a wave state of the water surface; the non-micro-motion state may include a stationary state or a motion state of the target itself.
  • the radar when used in an aircraft to classify the landing point as a target, it can be understood that the water surface is non-rigid and easily affected by environmental factors such as wind. It is in a micro-motion state and rigid. The ground is at a static state, and the micro-movement characteristics of the obtained target can be used to classify the target to determine whether the currently detected landing point is the ground or the water surface, so that the aircraft can further determine whether the landing can be performed.
  • two targets that are in the micro-motion state can be classified based on their micro-motion characteristics.
  • radar when used in an aircraft to classify and recognize the ground in the desert, it can distinguish between quicksand and water surface based on the micromovement characteristics of quicksand and the micromotion characteristics of water surface.
  • the related features of the target may include at least one of the micro-movement feature of the target, the Radar Cross Section (RCS) feature (also referred to as the reflective surface feature of the target), and the like.
  • RCS Radar Cross Section
  • the RCS characteristics of the target can be used to reflect the target's reflection of the radar signal. Because the target reflects the radar signal differently according to its own attributes, the radar system can classify different targets based on the RCS characteristics.
  • the above two features can be combined to classify whether the target is in a micromotion state, and the classification accuracy can be improved by combining the features.
  • Step S304 classify the target according to the relevant characteristics of the target.
  • the target may be classified based on the related characteristics of the target based on the classifier or the trained classification model to obtain a classification result.
  • the classification result can be the classification attribute of the target.
  • the classification attributes of the target include ground attributes or water surface attributes.
  • the ground property of the target is used to characterize that the ground is rigid, that is, it moves without being affected by the environment, such as wind.
  • the target can be classified online or offline according to the relevant characteristics of the target.
  • achieving online classification of targets refers to inputting the relevant features of the targets into a radar-configured classifier, and the classifier outputs the classification attributes of the targets based on the obtained relevant features of the targets. Further, the correctness of the output results can also be fed back to the classifier, so that the classifier can adjust the classification algorithm. In this case, as the number of times the target is detected increases, the classifier output is more accurate.
  • to achieve offline classification of the target means to use multiple related features and classifiers to train a classification model and pre-store the classification model into the radar. After obtaining the relevant features of the target, the relevant features and classification model can be used to obtain Classification results, such as the classification properties of the target.
  • an object in the environment can be detected and an echo signal of the target based on the radar signal feedback can be obtained.
  • relevant characteristics of the target can be obtained, and The related features of the target classify the target.
  • the above method can realize the use of radar signals to classify targets when they are far away from the target.
  • FIG. 4 is a schematic flowchart of another target classification method according to an embodiment of the present application. As shown in FIG. 4, the method includes at least the following steps.
  • Step S401 transmitting a radar signal to detect a target in the environment, and obtaining an echo signal of the target based on the radar signal feedback.
  • step S402 the echo signal is processed to obtain a two-dimensional signal.
  • a 1-dimensional Fast Fourier Transform (1DFFT) may be performed on the echo signal to obtain the distance data between the radar and the target (also may be a distance signal).
  • a two-dimensional Fast Fourier Transform (2DFFT) may be performed on the echo signal to obtain a Doppler signal.
  • a two-dimensional signal including distance data and a Doppler signal can be obtained.
  • step S403 the relevant features of the target are obtained according to the two-dimensional signal.
  • the micro-motion characteristics of the target can be obtained according to the Doppler signal and / or the distance data in the two-dimensional signal.
  • the Doppler signal in the two-dimensional signal may include a micro-Doppler signal. That is, according to the Doppler signal, the micro-motion characteristics of the target related to the Doppler signal can be obtained.
  • the range entropy feature can be used to represent the uncertainty of the distance between the target and the radar.
  • the distance entropy characteristics are relatively large.
  • the distance entropy characteristics are relatively small. Differentiate whether the target is in a jog state.
  • the distance entropy feature (feature1) can be determined by formula (1).
  • formula (1) is:
  • feature1 is used to represent the distance entropy feature
  • M is used to represent the number of frames of the acquired echo signal
  • k is used to represent the frame number of the acquired echo signal
  • c (k) is used to represent the echo of the kth frame
  • the radar system can continuously transmit radar signals and can continuously receive multiple frames of echo signals.
  • the radar system can determine the distance from the target based on each frame of echo signals and its corresponding radar signal. It can be expressed as a distance signal or distance data.
  • the distance signal can be normalized, such as by formula (2), and the results of the M normalized processes can be calculated by entropy, such as by formula ( 1) Perform entropy calculation, and then multiple distance entropy features.
  • c (k) is determined according to formula (2).
  • formula (2) is:
  • N is used to indicate the number of frames included in the calculation window
  • n is used to indicate any frame in the calculation window
  • range (k) is used to indicate the distance value in the echo signal of the k-th frame
  • range (n) It is used to represent the distance value in the echo signal of the n-th frame in the calculation window
  • N is an integer greater than or equal to 1.
  • N can be understood as calculating the number of frames contained or contained in the window, or calculating the window length of the window, which is determined by the number of frames. Second, the characteristics of noise energy ratio.
  • the noise-to-energy ratio feature is used to represent a ratio of the energy of the noise signal to the energy of the Doppler signal in the Doppler signal.
  • the larger the noise energy ratio characteristic the smaller the proportion of the micromotion energy in the Doppler signal, and the less likely the target is in the micromotion state; accordingly, the smaller the noise energy ratio characteristic, the more the The greater the proportion of the fretting energy in the Doppler signal, the more likely the target is in the fretting state.
  • the micro motion signal in the Doppler signal may be determined first, and then the micro motion signal in the Doppler signal is removed using the CLEAN algorithm to obtain the noise signal.
  • the micro-motion signal can be represented by the harmonics of the Doppler signal in the frequency domain or the time domain.
  • the Doppler signal can be searched for the harmonic with the largest amplitude value to determine the micro-motion signal, and the CLEAN algorithm can be used.
  • the micro-motion signal represented by the harmonic is subtracted from the Doppler signal to obtain a residual signal.
  • the noise energy ratio can be determined by the ratio of the energy of the residual signal to the energy of the Doppler signal. If the CLEAN algorithm is performed q times on the Doppler signal in the above manner, where q is an integer greater than or equal to q, then q ratios can be obtained.
  • the noise energy ratio characteristic can be represented by a vector including these q ratios.
  • the target may be classified using the vector, or the target may be classified using one or more ratios in the vector.
  • q may be preset or determined based on the number of harmonics in the echo signal, which is not limited herein.
  • y is the energy signal represented by the subharmonic, which can be a time domain signal or a frequency domain signal
  • A represents the amplitude in the Doppler signal
  • ⁇ represents the phase
  • j represents the time
  • f c represents the Doppler Frequency
  • M is the cumulative number of pulses.
  • the energy ratio characteristic value can be determined by the following formula (4):
  • R i represents the eigenvalue of the energy ratio based on the CLEAN algorithm
  • 1 ⁇ i ⁇ q L represents the spectrum length of the Doppler signal
  • S r (n) represents the Doppler signal.
  • S r (n) represents the noise signal in the Doppler signal
  • S i (n) represents the i-th time according to the Doppler signal (also called the original signal) and The residual signal obtained by subtracting the harmonic signals.
  • feature2 is the energy ratio feature.
  • the fretting energy ratio feature is used to represent a ratio of the energy of the fretting signal to the energy of the Doppler signal in the Doppler signal, or the energy of the fretting signal to the energy of the noise signal.
  • the larger the characteristic of the micro motion energy ratio the greater the proportion of the micro motion signal in the Doppler signal, and the more likely the target is in the micro motion state.
  • feature3 represents the characteristics of the fretting energy ratio
  • the interval [f1, F1] represents the frequency band of the fretting signal energy.
  • the interval may be preset, and the determination of the interval may be related to the application scenario involved. For example, the preset interval is different in different application scenarios.
  • the interval [f2, F2] indicates the frequency band of the noise signal or the frequency band of the Doppler signal. Similarly, the interval may be preset or determined based on the interval [f1, F1].
  • the interval [f2, F2] represents the frequency band of the noise signal
  • the interval [f2, F2] can represent all or part of the frequency band of the noise signal
  • f is the interval [f1, F1] or the interval [f2, F2]
  • P (f) is the amplitude corresponding to f frequency.
  • the RCS feature can also be obtained based on the two-dimensional signal.
  • the manner of obtaining is not limited in the examples of the present application.
  • Step S404 Determine a classification parameter formula corresponding to the relevant feature of the target.
  • the different combinations of the related features correspond to different classification parameter formulas.
  • classification parameter formula in the embodiment of the present application can be understood as a classification model.
  • the corresponding relationship between the combination mode of the relevant features of the target and the classification parameter formula may be pre-stored in the radar system, as pre-stored in the memory 211 of the radar system shown in FIG. 2.
  • the classification parameter formula corresponding to the related feature can be obtained according to the pre-stored correspondence.
  • Step S405 classify the target according to the classification parameter formula and related characteristics of the target.
  • one or more related features of the target may be used as input values of the classification parameter formula, and then the parameter values of the classification parameters may be calculated. Further, the classification interval in which the parameter value of the classification parameter falls can be determined, and if it falls within a certain classification interval, a target classification corresponding to the classification interval can be determined. The correspondence between the classification interval and the target classification may be obtained through pre-training, which is pre-stored in the radar. Alternatively, one or more related features of the target can be used as the input value of the classification parameter formula, and the classification result of the target can be directly obtained, and then the target classification can be determined.
  • the target classification corresponding to the combination may be determined according to a combination of the calculated related features of the target.
  • the combination of related features may include at least two related features, and the combination of related features may further improve the accuracy of classifying the target.
  • the above-mentioned method for determining the classification attribute of the target may be implemented by a classifier in the radar, or by other devices in the radar, such as by a processor in the radar executing a corresponding program.
  • the corresponding relationship, or the classification parameter formula may be obtained by the classifier through a training algorithm.
  • the classifier includes multiple training algorithms, and the above-mentioned correspondence relationship or classification parameter formula can be obtained based on one or more training algorithms in the classifier.
  • the training correspondence or classification parameter formula can be pre-stored in the radar.
  • the radar detects the target in real time and calculates the relevant characteristics of the target, it can be combined with the pre-stored correspondence or according to the classifier or other device in the radar.
  • Classification parameter formula to further obtain classification results.
  • the classifier in the radar can directly obtain the classification result according to the relevant features of the target and the training algorithm.
  • the classifier may include at least one of the following:
  • SVM Support Vector Machine
  • RVM Relative Vector Machine
  • KNN K-nearest neighbor classification algorithm
  • neural network etc.
  • the radar can also output the classification result of the target to other devices, so that other devices can perform further processing according to the classification attributes of the target.
  • the embodiment of the present application only uses the classification attribute of the target as the ground attribute and the water surface attribute as an example for description.
  • the recognition and classification of the target in other application scenarios can also be implemented. Be limited.
  • the radar is installed on the bottom of the aircraft and can be used to assist the aircraft in autonomous landing.
  • the radar when the aircraft is in a scene that needs to land, the radar is triggered to emit radar signals.
  • the radar transmits radar signals toward the landing point or the ground.
  • the landing point or the ground After receiving the radar signal, the landing point or the ground reflects and reflects the radar signal.
  • the signal is an echo signal.
  • the radar After receiving the echo signal, the radar can obtain the relevant characteristics of the target based on the echo signal.
  • the echo signal can be processed into a two-dimensional signal based on the above algorithm, and the target can be calculated based on the two-dimensional signal. Micro-movement features, RCS features, etc.
  • At least one of a range entropy feature, an energy ratio feature, a fretting energy ratio feature, an RCS feature, and the like can be obtained, and then the landing ground can be classified according to the related features of the calculated target. Landing points are classified as either ground or water.
  • the radar can transmit the classification result to the aircraft's flight control system, and the flight control system can determine whether to make a vertical landing based on the classification result. For example, when the classification result is on the ground, the flight control system may control the power unit of the aircraft for vertical landing. When the classification result is on the water, the flight control system stops the landing plan, or plans a new landing path.
  • FIG. 5 is a schematic diagram of a module composition of a target classification device according to an embodiment of the present application.
  • the target classification apparatus 500 may include a transceiver module 501 and a processing module 503.
  • the transceiver module 501 is configured to transmit a radar signal to detect a target in the environment, and obtain an echo signal of the target based on the radar signal feedback;
  • a processing module 503 is configured to obtain relevant characteristics of the target according to the echo signal; and classify the target according to the relevant characteristics of the target.
  • the related features of the target include micro-motion features
  • the processing module 503 classifies the target based on the related characteristics of the target, including:
  • the target is classified as a target in a micro-motion state or a target in a non-micro-motion state.
  • the micro-motion feature includes at least one of the following:
  • the range entropy feature is used to represent the uncertainty of the distance between the target and the radar
  • the noise energy ratio feature is used to represent a ratio of the energy of the noise signal to the energy of the Doppler signal in the Doppler signal;
  • the fretting energy ratio feature is used to represent the energy of the fretting signal and the energy of the Doppler signal in the Doppler signal, or the ratio of the fretting energy to the energy of the noise signal .
  • the processing module 503 obtains the relevant characteristics of the target according to the echo signal, including:
  • the echo signal Processing the echo signal to obtain a two-dimensional signal; wherein the two-dimensional signal includes a distance signal and a Doppler signal; the Doppler signal includes a micro motion signal and a noise signal;
  • the processing module 503 obtains the micro motion characteristics of the target according to the two-dimensional signal, including:
  • formula (1) is:
  • feature1 is used to indicate the distance entropy feature
  • M is used to indicate the number of frames of the acquired echo signal
  • k is used to indicate the frame number of the acquired echo signal
  • c (k) is used to indicate the echo of the kth frame.
  • c (k) is determined according to formula (2);
  • formula (2) is:
  • N is used to indicate the number of frames included in the calculation window
  • n is used to indicate any frame in the calculation window
  • range (k) is used to indicate the distance value represented by the distance signal in the echo signal of the k-th frame
  • Range (n) is used to represent a distance value represented by a distance signal in an echo signal of the n-th frame in the calculation window; wherein N is an integer greater than or equal to 1.
  • the processing module 503 obtains the micro motion characteristics of the target according to the two-dimensional signal, including:
  • the processing module 503 obtains the micro motion characteristics of the target according to the two-dimensional signal, including:
  • Determining the target according to a ratio of the energy of the micro-motion signal to the energy of the Doppler signal, or the ratio of the energy of the micro-motion signal to the energy of the noise signal in the Doppler signal The characteristics of the fretting energy ratio.
  • the processing module 503 classifies the target according to related characteristics of the target, including:
  • the classification parameter formula is obtained by training any one of the following classifiers:
  • Support vector machine SVM
  • correlation vector machine RVM
  • KNN K nearest neighbor classification algorithm
  • the relevant characteristics of the target include RCS characteristics
  • the RCS feature is used to indicate a reflection degree of a target to the radar signal.
  • target classification device may also include other functional modules, which is not limited herein.
  • the above functional modules may be implemented by software, hardware or a combination thereof.
  • the above-mentioned functional module may be implemented by a computer program, or the transmitting-receiving module in the above-mentioned functional module may be implemented by a transmitter or a receiver shown in FIG. 2, and the processing module in the above-mentioned functional module may be processed by the processing shown in FIG.
  • the processor or the computer program implemented by the processor is not limited herein.
  • the processor in the radar may be included in the processor, or included in the transmitter or receiver, or included in other devices, where at least one processor may For performing any one of the methods in the above embodiments.
  • an embodiment of the present application further provides a readable storage medium, characterized in that a computer program is stored in the readable storage medium, and when the computer program is executed by a processor, the first aspect may be implemented Either method.
  • an object in the environment can be detected and an echo signal of the target based on the radar signal feedback can be obtained.
  • relevant characteristics of the target can be obtained, and The related features of the target classify the target.
  • the above method can realize the classification of the target in the case of being far away from the target.

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  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Radar Systems Or Details Thereof (AREA)

Abstract

L'invention concerne un procédé de classification de cible et un dispositif associé. Le procédé consiste à : émettre un signal radar dans un environnement où un radar est situé, de façon à détecter une cible dans l'environnement (S301), et acquérir un signal d'écho renvoyé par la cible sur la base du signal radar (S302) ; obtenir une caractéristique associée de la cible en fonction du signal d'écho (S303) ; et classifier la cible en fonction de la caractéristique associée de la cible (S304). La présente invention est apte à réaliser une classification précise d'une cible distante à l'aide d'un radar.
PCT/CN2019/103227 2018-09-14 2019-08-29 Procédé de classification de cible et dispositif associé WO2020052441A1 (fr)

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