WO2020097903A1 - 测角方法以及雷达设备 - Google Patents

测角方法以及雷达设备 Download PDF

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Publication number
WO2020097903A1
WO2020097903A1 PCT/CN2018/115839 CN2018115839W WO2020097903A1 WO 2020097903 A1 WO2020097903 A1 WO 2020097903A1 CN 2018115839 W CN2018115839 W CN 2018115839W WO 2020097903 A1 WO2020097903 A1 WO 2020097903A1
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WIPO (PCT)
Prior art keywords
antenna data
dimensional antenna
target point
distance
angle
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PCT/CN2018/115839
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English (en)
French (fr)
Inventor
周沐
曹毅
Original Assignee
华为技术有限公司
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Application filed by 华为技术有限公司 filed Critical 华为技术有限公司
Priority to PCT/CN2018/115839 priority Critical patent/WO2020097903A1/zh
Priority to CN201880096552.3A priority patent/CN112639512A/zh
Publication of WO2020097903A1 publication Critical patent/WO2020097903A1/zh

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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
    • G01S7/42—Diversity systems specially adapted for radar

Definitions

  • This application relates to radar technology, in particular to an angle measurement method and radar equipment.
  • Automated driving is an important function of future cars, and vehicle-mounted radar equipment is an important part of realizing automatic driving functions in future cars.
  • the vehicle-mounted radar device is used to detect the road surface in the current driving area of the target vehicle, for example, to detect obstacles in the area, or for adaptive cruise control.
  • the angle estimation value is calculated and obtained with high precision by increasing the number of antennas and performing the angle estimation with the super-resolution method. That is, the vehicle-mounted radar device can obtain N antenna data, and then calculate the angle estimation value according to the N antenna data and the super-resolution method, so that the calculated angle estimation value is more accurate.
  • the N antenna data obtained by the on-board radar device is N-dimensional antenna data
  • the super-resolution method is used for calculation, because the N-dimensional antenna data has more dimensions, its calculation is complicated Degree is relatively high.
  • the embodiments of the present application provide an angle measurement method and a radar device, which are applied to a radar system and used to reduce the complexity of measuring the first angle of a target point.
  • a first aspect of an embodiment of this application provides an angle measurement method, including:
  • the radar device can be used to obtain N-dimensional antenna data of the target point, where N is a positive integer greater than 2, and then the N-dimensional antenna data is reduced by the radar device to obtain K-dimensional antenna data, where K is less than A positive integer of N; and then calculate the first angle of the target point according to the K-dimensional antenna data, where the first angle is the angle of the target point relative to the radar device.
  • the N-dimensional antenna data of the target point is reduced to obtain K-dimensional antenna data, and then the first angle of the target point is calculated according to the K-dimensional antenna data, that is, by reducing the N-dimensional antenna data
  • the dimension of K-dimensional antenna data is less than that of N-dimensional antenna data, so the complexity of calculating the first angle is reduced.
  • the step of reducing the dimension of the N-dimensional antenna data to obtain the K-dimensional antenna data includes: first, splitting the N-dimensional antenna data into K sub-array antenna data; then, the K sub-arrays Dimension reduction of antenna data through beamforming to obtain K-dimensional antenna data.
  • a specific method for dimensionality reduction of N-dimensional antenna data is provided. In practical applications, the implementability and practicability of the solution are improved.
  • the method before performing dimension reduction on the N-dimensional antenna data to obtain K-dimensional antenna data, the method may further include: when the target point is located in a far-field area in the radiation area, the angle Fast Fourier transform (FFT) calculates the N-dimensional antenna data to obtain a second angle, where the second angle is the angle of the target point relative to the radar device, and the second angle The accuracy is lower than the accuracy of the first angle, and the radiation area may be an area covered by the radar device through the radar waveform.
  • FFT Fast Fourier transform
  • the second angle is the angle of the target point relative to the radar device, and the accuracy of the second angle is lower than the accuracy of the first angle; then, the antenna data of the K sub-arrays is reduced by beamforming to obtain the dimension
  • the step of K-dimensional antenna data may include: first, determine the beamforming vector corresponding to each of the K subarrays, the beamforming vector including the weight corresponding to the target antenna in the second angular direction, the target antenna is The receiving antenna corresponding to the antenna data included in each sub-array, and the number of receiving antennas corresponding to the target antenna is greater than or equal to 1; then, the antenna data of each sub-array in the K sub-arrays is multiplied by each sub-array The corresponding beamforming vector obtains the K-dimensional antenna data.
  • the target point when the target point is located in the far-field area of the radiation area, a specific process of reducing the dimensionality of the antenna data of K sub-arrays by beamforming is provided, and then the target is calculated according to the dimensionality-reduced data The first angle of the point, which improves the anti-interference performance and anti-clutter performance, thereby improving the accuracy of the first angle. In practical applications, the achievability and integrity of the solution have been improved.
  • the method before performing dimension reduction on the N-dimensional antenna data to obtain K-dimensional antenna data, the method may further include: first, when the target point is located in a near-field area in the radiation area, The N-dimensional antenna data can be calculated by beamforming to obtain the angle and distance between each receiving antenna in the radar device and the target point.
  • the radiation area is the area covered by the radar device through the radar waveform;
  • the antenna data of the K sub-arrays is reduced in dimension through beamforming, and the steps of obtaining K-dimensional antenna data may include: first, the weight of each receiving antenna may be determined according to the angle and distance between each receiving antenna data and the target point; then The beamforming vector corresponding to each of the K subarrays is determined according to the weight of each receiving antenna data.
  • the beamforming vector may include the weight corresponding to the angle and distance of the target antenna relative to the target point. Is the receiving antenna corresponding to the antenna data included in each sub-array, and the number of receiving antennas corresponding to the target antenna is greater than or equal to 1.
  • a specific process of dimensionality reduction by beamforming is provided, and then the first angle of the target point is calculated according to the data after dimensionality reduction, which improves The anti-jamming performance and anti-clutter performance are improved, thereby improving the accuracy of the first angle.
  • the achievability and integrity of the solution have been improved.
  • M N-K + 1
  • the M K-dimensional antenna data is dimensionally reduced through spatial smoothing to obtain K-dimensional antenna data.
  • another specific method for dimensionality reduction of N-dimensional antenna data is provided. In practical applications, the diversity and practicality of the scheme are improved.
  • the M K-dimensional antenna data is dimensionally reduced by spatial smoothing
  • obtaining K-dimensional antenna data may include: first, determining the K-dimensional antenna data of each of the M K-dimensional antenna data Column vector, and determine the conjugate to rank of the column vector of each K-dimensional antenna row data; then, multiply the column vector of each K-dimensional antenna data by the conjugate of the corresponding column vector of each K-dimensional antenna data The rank is converted to obtain the K-dimensional antenna data.
  • a specific dimensionality reduction process for reducing the dimensionality of the antenna data of K sub-arrays through spatial smoothing is provided. In practical applications, the achievability and integrity of the solution are improved.
  • the method before acquiring N-dimensional antenna data, may further include: first, acquiring the speeds and distances of multiple candidate points detected by the radar device, and then according to the speeds of the multiple candidate points And distance, using constant false alarm rate detection algorithm to determine the target point.
  • a specific method for determining the target point is provided, and in practical applications, the integrity and practicability of the solution are improved.
  • using the radar device to obtain the N-dimensional antenna data of the target point may include: first, using the radar device to obtain the N-dimensional antenna data associated with the first speed and the first distance, the first speed is The speed of the target point relative to the radar device, the first distance is the distance between the target point and the radar device; then, the echo intensity threshold can be determined according to the N-dimensional antenna data associated with the first speed and the first distance ; Then according to the echo intensity threshold and the echo signal of at least one candidate point closer to the target point, a second distance and a second velocity are obtained by calculation, each candidate point closer to the target point and the radar The distance between the devices is the second distance, and the difference between the second distance and the first distance is within a preset range; the N-dimensional antenna data associated with the first speed and the first distance and the second speed The N-dimensional antenna data associated with the second distance is used as the N-dimensional antenna data of the target point.
  • calculating the first angle of the target point according to the K-dimensional antenna data may include: calculating the first angle of the target point according to the K-dimensional antenna data and the super-resolution method.
  • a specific method for calculating the first angle of the target point is provided, which improves the practicality of the solution.
  • a second aspect of an embodiment of the present application provides a radar device that has a function to realize the behavior of the radar device of the first aspect described above.
  • This function may be implemented by hardware, or may be implemented by hardware executing corresponding software.
  • the hardware or software includes one or more modules corresponding to the above functions.
  • a third aspect of the embodiments of the present application provides a radar device.
  • the radar device includes: a processor, a memory, an input-output device, and a bus; the processor, memory, and input-output device are respectively connected to the bus, and the memory stores There are computer instructions; when the processor executes the computer instructions in the memory, the memory stores computer instructions; when the processor executes the computer instructions in the memory, it is used to implement any one of the implementation manners of the first aspect .
  • a fourth aspect of the embodiments of the present application provides a computer program product including instructions, which is characterized in that, when it is run on a computer, the computer is caused to execute any implementation manner as in the first aspect.
  • a fifth aspect of the embodiments of the present application provides a computer-readable storage medium, which includes instructions, which when executed on a computer, causes the computer to execute any implementation manner as in the first aspect.
  • the N-dimensional antenna data of the target point is obtained by using a radar device, where N is a positive integer greater than 2; then, the N-dimensional antenna data is dimension-reduced to obtain K-dimensional antenna data, K Is a positive integer less than N; then the first angle of the target point is calculated according to the K-dimensional antenna data, and the first angle is the angle of the target point relative to the radar device.
  • the N-dimensional antenna data of the target point is reduced to obtain K-dimensional antenna data, and then the first angle of the target point is calculated according to the K-dimensional antenna data, that is, by performing the N-dimensional antenna data Dimensionality reduction is used to obtain K-dimensional antenna data after dimensionality reduction. Since the dimension of K-dimensional antenna data is smaller than that of N-dimensional antenna data, the complexity of calculating the first angle is reduced.
  • FIG. 1A is a schematic diagram of an application scenario framework in an embodiment of this application.
  • FIG. 1B is a schematic diagram of a scenario of the angle measuring method in the embodiment of the present application.
  • FIG. 2 is a schematic diagram of an embodiment of an angle measuring method in an embodiment of the present application.
  • FIG. 3 is a schematic diagram of another scenario of the angle measuring method in the embodiment of the present application.
  • FIG. 4 is a schematic diagram of another scenario of the angle measuring method in the embodiment of the present application.
  • FIG. 5 is a schematic structural diagram of a radar device in an embodiment of this application.
  • FIG. 6 is another schematic structural diagram of the radar device in the embodiment of the present application.
  • Embodiments of the present application provide an angle measurement method and a radar device, which are applied to a radar system and used to reduce the complexity of measuring the first angle of a target point.
  • FIG. 1A is a schematic diagram of an application scenario provided by an embodiment of the present application.
  • a radar system is installed in a vehicle.
  • the radar system includes a vehicle-mounted radar device.
  • a transmitting antenna in the vehicle-mounted radar device transmits a radar waveform, and then N receiving antennas in the vehicle-mounted radar device receive a target point for the transmitting antenna.
  • the radar echo waveform returned by the transmitted radar waveform.
  • the target point can be an obstacle or pedestrian within the measurement range of the vehicle-mounted radar device, and the first angle can reduce the dimensionality of the radar device according to the N-dimensional antenna data received by the N receiving antennas.
  • the N-dimensional antenna data may include the phase and amplitude of the radar echo waveform received by the receiving antenna , Frequency, etc.
  • the above radar system may be an orthogonal frequency division multiplexing (OFDM) radar system, a frequency modulated continuous wave (FMCW) radar system or a phase modulated continuous wave (PMCW) radar system , This application does not limit the specific radar system.
  • OFDM orthogonal frequency division multiplexing
  • FMCW frequency modulated continuous wave
  • PMCW phase modulated continuous wave
  • multiple vehicle-mounted radar devices may be installed in the vehicle as needed, and the plurality of vehicle-mounted radar devices are respectively used for short-range measurement, middle-range measurement, or long-range measurement.
  • multiple on-vehicle radar devices may be installed in the vehicle.
  • the multiple on-vehicle radar devices include those used for short-range measurement, those used for mid-range measurement, and those used for long-distance measurement. At least one of them. It should be known that the aforementioned short-range measurement, middle-range measurement or long-range measurement are based on the vehicle as a reference point.
  • the distance measured by the farthest distance from the vehicle during the short-distance measurement is less than the distance measured from the vehicle during the middle-distance measurement, and the distance measured from the vehicle at the farthest distance during the middle-distance measurement Less than the distance measured from the farthest distance to the vehicle during long-distance measurement.
  • the calculation of the angle between the target point and the vehicle-mounted radar device is mainly performed by the super-resolution method.
  • the super-resolution method is used to calculate the angle of the target point relative to the on-board radar equipment.
  • the correlation matrix is mainly calculated based on the N-dimensional antenna data, and then the product of the noise space is calculated by singular value decomposition according to the correlation matrix, and then the noise
  • the product of the space and the array response coefficient of the N-dimensional antenna data are used to calculate the angle of the target point relative to the vehicle-mounted radar device; wherein the complexity of the process of singular value decomposition through the correlation matrix is related to the dimension of the correlation matrix, the correlation matrix
  • the more dimensions of, the higher the complexity of the calculation, and the correlation matrix has the same dimensions as the N-dimensional antenna data, so the larger the N, the higher the complexity of the calculation.
  • the vehicle-mounted radar device acquires the N-dimensional antenna data of the target point through N antennas, and then uses the super-resolution method to calculate according to the N-dimensional antenna data to obtain the angle of the target point relative to the vehicle-mounted radar device.
  • N is a positive integer greater than 2
  • the complexity of the calculation method is related to the number of antennas N, that is, the larger the N, the higher the calculation complexity.
  • FIG. 1A is only to illustrate an application scenario of the present application.
  • the angle measurement method of the present application is also applicable to other types of radar systems, for example, ultrasonic radar systems, imaging
  • This application is not limited to radar systems or foreign object radar systems for airport runway inspection.
  • the radar device may be a vehicle-mounted radar device, a security device, or a runway foreign object detection device, etc., which is not limited in this application.
  • an embodiment of the present application proposes an angle measurement method for reducing the complexity of measuring the angle of the target point relative to the radar device.
  • the radar device is first used to obtain N-dimensional antenna data of the target point, where N is a positive integer greater than 2; then, the N-dimensional antenna data is dimension-reduced to obtain K-dimensional antenna data, where K is less than A positive integer of N; then the first angle of the target point is calculated according to the K-dimensional antenna data, and the first angle is the angle of the target point relative to the radar device.
  • the N-dimensional antenna data is reduced in dimension to obtain K-dimensional antenna data, and then the first angle of the target point is calculated according to the K-dimensional antenna data, that is, the N-dimensional antenna data is reduced in dimension
  • the K-dimensional antenna data after dimensionality reduction is acquired. Since the dimension of the K-dimensional antenna data is smaller than that of the N-dimensional antenna data, the complexity of calculating the first angle is reduced.
  • the radar device reduces the dimension of the N-dimensional antenna data to obtain the K-dimensional antenna data.
  • the radar device can reduce the dimension of the N-dimensional antenna data through beamforming.
  • the specific process is to reduce the N-dimensional antenna data.
  • the data is split into antenna data of K sub-arrays, and then the beamforming vector corresponding to each sub-array is determined, and then the antenna data of each sub-array is multiplied by the corresponding beam-forming vector to obtain K-dimensional antenna data; Dimension reduction of N-dimensional antenna data in a spatially smooth manner.
  • M N-K + 1
  • the conjugate of the vector is converted to rank to obtain K-dimensional antenna data; the specific application does not limit this.
  • only the radar device performs dimension reduction on the N-dimensional antenna data through beamforming to obtain K-dimensional antenna data as an example for description.
  • the radar device may preferably calculate the N-dimensional antenna data by angle fast Fourier transform FFT to obtain the second angle of the target point, the The second angle is the angle of the target point relative to the radar device, and the accuracy of the second angle is lower than the accuracy of the first angle, that is, the second angle is a roughly calculated angle of the target point relative to the radar device;
  • the number of N-dimensional antennas is reduced in the second angular direction by beamforming.
  • the target point When the target point is located in the far-field area, first determine the second angle of the target point, and then perform dimensionality reduction by beamforming in the direction of the second angle, and then calculate the first angle of the target point according to the data after dimensionality reduction
  • the anti-jamming performance and anti-clutter performance are improved, thereby improving the accuracy of the first angle.
  • the N-dimensional antenna data can be calculated by beamforming to obtain the angle and distance of each receiving antenna of the radar device; then the radar device determines the angle of each receiving antenna And the weight of each receiving antenna corresponding to the distance, and according to the weight of each receiving antenna, determine the beamforming vector corresponding to each of the K subarrays, the beamforming vector includes the target antenna relative to the target point
  • the weight corresponding to the angle and distance of, the target antenna is the receiving antenna corresponding to the antenna data included in each sub-array, and the number of receiving antennas corresponding to the target antenna is greater than or equal to 1, then each of the K sub-arrays
  • the antenna data of the array is multiplied by the beamforming vector corresponding to each subarray to obtain K-dimensional antenna data; the following is an example to illustrate: suppose subarray 1 of K subarrays includes receiving antenna 1, receiving antenna 2, and receiving antenna 3 Antenna data, then it can be determined that the beamforming vector includes the first weight corresponding to the
  • the target point When the target point is located in the near-field area, first determine the angle and distance of each receiving antenna relative to the target point, and then perform dimensionality reduction by beamforming in the direction of the angle and distance of each receiving antenna, and then calculate based on the data after dimensionality reduction The first angle of the target point, which improves the anti-interference performance and anti-clutter performance, thereby improving the accuracy of the first angle.
  • only the target point is located in the far field area of the radiation area as an example for description.
  • the radar device calculating the first angle of the target point according to the K-dimensional antenna data may be the radar device calculating the first angle according to the K-dimensional antenna data and the super-resolution method.
  • the radar device can obtain the speed and distance of multiple candidate points from the radar echoes received by the N receiving antennas, and then use the constant false alarm rate detection algorithm according to the speed and distance of the multiple candidate points Determine the target point, where the speed and distance of the multiple candidate points can be calculated by the radar device according to the phase difference and time difference between the radar echo and the transmitting radar waveform of the transmitting antenna.
  • Distance it should be noted that while determining the target point, the speed of the target point relative to the radar device and the distance of the target point relative to the radar device are also determined. In this application, the speed of the target point relative to the radar device is determined. For the first speed, the distance between the first target point and the radar device is the first distance.
  • the radar device may acquire the N-dimensional antenna data associated with each group of speeds and distances as shown in FIG. 1B by sampling the radar echo received by the receiving antenna and the Doppler effect.
  • Each receiving antenna will have a corresponding set of antenna data, then there are N receiving antennas, that is, each group of speed and distance is associated with N-dimensional antenna data; then in this application, the speed of the target point relative to the radar device is the first For the speed, the distance of the target point relative to the radar device is the first distance, then it can be seen from FIG. 1B that the N-dimensional antenna data of the target point may include the N-dimensional antenna data associated with the first speed and the first distance.
  • the radar device may determine the radar echo intensity of the target point according to the N-dimensional antenna data associated with the first speed and the first distance, and then set an echo intensity threshold according to the radar echo intensity, and then determine The echo signal strength of other candidate points closer to the target point is greater than the echo intensity threshold, then the radar device calculates the second velocity and the second distance of the other candidate points relative to the radar device;
  • the echo signal refers to a radar echo that reflects the radar emission waveform from other candidate points, and the difference between the second distance and the first distance of the target point is within a preset range; then, the second speed and the second distance
  • the associated N-dimensional antenna data is also used as the N-dimensional antenna data of the target point.
  • only the N-dimensional antenna data of the target point includes the N-dimensional antenna data associated with the first velocity and the first distance as an example for description.
  • An embodiment of the angle measurement method in the embodiment of the present application includes:
  • the radar device acquires the speed and distance of multiple candidate points.
  • the radar equipment includes one or more transmitting channels and receiving channels, N tx transmitting antennas transmit radar waveforms through a preset pattern, N rx receiving antennas simultaneously receive radar echoes, and receive through N rx receiving antennas The data obtained can be used to measure the second angle of the target point.
  • the radar device determines the target point according to the speed and distance of the multiple candidate points and the constant false alarm rate detection algorithm.
  • the radar device may determine that the echo intensity of the unit corresponding to the first speed and the first distance is greater than a preset value according to the constant false alarm rate detection algorithm and the speed and distance of multiple candidate points, then the radar device It can be determined that there is a target point in the unit corresponding to the first speed and the first distance, and then it can also be determined that the speed of the target point relative to the radar device is the first speed, and the distance of the target point relative to the radar device is the first distance.
  • the radar device acquires N-dimensional antenna data associated with the first speed and the first distance.
  • the radar device determines the first distance and the first speed corresponding to the target point, then the N-dimensional antenna data associated with the first distance and the first speed may be obtained from the N-dimensional antenna data acquired by the N antennas.
  • Each dimension of the N-dimensional antenna data includes antenna data associated with the first velocity and the first distance, that is, as shown in FIG. 1B, the N-dimensional antenna data may include N dimensions associated with the first velocity and the first distance Antenna data, that is, antenna data received by the receiving antenna 1 to the receiving antenna N and associated with the first speed and the first distance.
  • the radar device determines that the target point is located in the far-field area in the radiation area.
  • the radar device 2D 2 / ⁇ value is determined according to the formula d f d f wherein, D is the maximum distance between the receiving antennas in the radar device, the radar wave [lambda] is the wavelength of the emitted radar device; then, the radar device determines the target Whether the first distance of the point relative to the radar device is greater than the d f , if so, the radar device can determine that the target point is located in the far-field area of the radiation area; secondly, if not, the radar device can determine that the target point is located The near-field area in the radiation area.
  • the radar device performs angle FFT calculation on the N-dimensional antenna data to obtain a second angle.
  • the radar device can calculate the N-dimensional antenna data by angle FFT to obtain a second angle; wherein, the second angle is the angle of the target point relative to the radar device, and the first The accuracy of the second angle is lower than the accuracy of the first angle, and it can be understood that the second angle is a roughly calculated angle of the target point relative to the radar device.
  • the radar device splits the N-dimensional antenna data into K sub-array antenna data.
  • the radar device After acquiring the N-dimensional antenna data, the radar device splits the N-dimensional antenna data into K sub-array antenna data, where K is a positive integer less than N, the dimension of each sub-array antenna data is not limited, and each sub-array antenna The dimensions of the data can be the same or different. As shown in FIG. 3, the radar device divides the N-dimensional antenna data into antenna data of K sub-arrays, and then reduces the dimension of the K sub-arrays by beamforming in the direction of the second angle, which can improve the reception of radar echo signals and The signal-to-interference-noise ratio of the interference signal improves the accuracy of the first angle of measurement.
  • the radar device determines the beamforming vector corresponding to each of the K subarrays.
  • the radar device obtains the antenna data of K sub-arrays, and then determines the beamforming vector corresponding to each of the K sub-arrays, where the beamforming vector includes the weight corresponding to the target antenna in the second angular direction, the
  • the target antenna can be understood as the receiving antenna corresponding to the antenna data of each of the K sub-arrays, and the number of receiving antennas corresponding to the target antenna is greater than or equal to 1.
  • the following example illustrates the radar device to determine the beam of each sub-array The specific process of forming the vector: As shown in FIG.
  • the radar device can determine that the subarray 1 includes The antenna corresponding to the antenna data includes receiving antenna 1, receiving antenna 2 and receiving antenna 3, and then the radar device can determine the first weight of receiving antenna 1 in the direction of the second angle of the target point, the receiving antenna 2 is The second weight in the second angular direction of the target point and the third weight in the second angular direction of the receiving antenna 3, then the beamforming vector corresponding to the subarray 1 is (first Weights, second weights, third weights); the calculation process of beamforming vectors corresponding to other sub-arrays is similar to the beamforming vectors of sub-array 1, which will not be repeated here.
  • the radar device multiplies the antenna data of each sub-array of the K sub-arrays by the beamforming vector corresponding to each corresponding sub-array to obtain K-dimensional antenna data.
  • the radar device multiplies the antenna data of each of the K sub-arrays by the beamforming vector corresponding to each corresponding sub-array to obtain K-dimensional antenna data; the specific process is: multiplying the antenna data of the receiving antenna 1 by the first The first scalar weight is obtained by the beamforming weight, and the second scalar weight is obtained by multiplying the antenna data of the receiving antenna 2 by the second beamforming weight, and the third beamforming weight is obtained by multiplying the antenna data of the receiving antenna 3 by the third beamforming weight. Scalar, and then add the first scalar, the second scalar and the third scalar to obtain the first dimensional scalar in the K-dimensional antenna data.
  • FIG. 4 is a schematic diagram of the dimensionality reduction of the N-dimensional antenna data by the vehicle-mounted radar device.
  • the vehicle-mounted radar device is Reduce the dimension of the N-dimensional antenna data in the direction to obtain the K-dimensional antenna data; assuming that the second angle is the direction of beam 2 or the direction of beam 3, then the vehicle-mounted device aligns N in the direction of beam 2 or beam 3 Dimensional antenna data is dimension reduced to obtain K-dimensional antenna data.
  • the radar device calculates the first angle of the target point according to the K-dimensional antenna data and the super-resolution method.
  • the radar device can calculate the first angle of the target point according to the K-dimensional antenna data and the super-resolution method, where the super-resolution method can be a root-MUSIC (root multiple signal classification) method, or It is a minimum variance distortion-free response method or a rotation-invariant signal parameter estimation method, etc., which is not specifically limited in this application.
  • the super-resolution method can be a root-MUSIC (root multiple signal classification) method, or It is a minimum variance distortion-free response method or a rotation-invariant signal parameter estimation method, etc., which is not specifically limited in this application.
  • the existing calculation method has high complexity and is greatly affected by clutter and interference waves, which makes the accuracy of its calculation angle lower, and the calculation of this application
  • the complexity of the method is low, and the dimensionality reduction through beamforming can improve the anti-interference performance and anti-clutter performance. Therefore, the first angle calculated based on the data after dimensionality reduction is more accurate.
  • K 2
  • the K-dimensional antenna data is a two-dimensional matrix, and its matrix rank is close to 1.
  • the radar device is used to obtain N-dimensional antenna data of the target point, where N is a positive integer greater than 2; then, the N-dimensional antenna data is dimension-reduced to obtain K-dimensional antenna data, and K is less than N A positive integer; then the first angle of the target point is calculated according to the K-dimensional antenna data, and the first angle is the angle of the target point relative to the radar device.
  • the N-dimensional antenna data of the target point is reduced to obtain K-dimensional antenna data, and then the first angle of the target point is calculated according to the K-dimensional antenna data, that is, by performing the N-dimensional antenna data
  • Dimensionality reduction is used to obtain K-dimensional antenna data after dimensionality reduction. Since the dimension of K-dimensional antenna data is smaller than that of N-dimensional antenna data, the complexity of calculating the first angle is reduced.
  • An embodiment of the radar device in the embodiment of the present application includes:
  • the transceiver module 501 is used to obtain N-dimensional antenna data of the target point, where N is a positive integer greater than 2;
  • the processing module 502 is used to reduce the dimension of the N-dimensional antenna data to obtain K-dimensional antenna data, where K is a positive integer less than N; the first angle of the target point is calculated according to the K-dimensional antenna data, and the first angle is The angle of the target point relative to the radar device.
  • processing module 502 is specifically used to:
  • the antenna data of the K sub-arrays is reduced by beamforming to obtain K-dimensional antenna data.
  • processing module 502 is also used to:
  • the N-dimensional antenna data is calculated by angle fast Fourier transform FFT to obtain a second angle, which is the target point relative to the radar
  • the angle of the device, and the accuracy of the second angle is lower than the accuracy of the first angle
  • the radiation area is the area covered by the radar device through the radar waveform.
  • the processing module 502 is specifically used for:
  • the beamforming vector corresponding to each of the K subarrays the beamforming vector including the weight corresponding to the target antenna in the second angular direction, the target antenna corresponding to the reception of the antenna data included in each subarray Antenna, and the number of receiving antennas corresponding to the target antenna is greater than or equal to 1;
  • processing module 502 is also used to:
  • the N-dimensional antenna data is calculated by beamforming to obtain the angle and distance between each receiving antenna in the radar device and the target point.
  • the radiation area is The area covered by the radar equipment radiation through the radar waveform;
  • the processing module 502 is specifically used for:
  • the beamforming vector corresponding to each of the K subarrays is determined according to the weight of each receiving antenna.
  • the beamforming vector includes the weight corresponding to the angle and distance of the target antenna relative to the target point.
  • the target antenna is the The receiving antenna corresponding to the antenna data included in each sub-array, and the number of receiving antennas corresponding to the target antenna is greater than or equal to 1;
  • processing module 502 is specifically used to:
  • the M K-dimensional antenna data is dimensionally reduced through spatial smoothing to obtain K-dimensional antenna data.
  • processing module 502 is specifically used to:
  • the column vector of each K-dimensional antenna data is multiplied by the conjugate transposed rank of the corresponding column vector of each K-dimensional antenna data to obtain K-dimensional antenna data.
  • the transceiver module 501 is also used to:
  • the processing module 502 is also used to:
  • a constant false alarm rate detection algorithm is adopted to determine the target point.
  • the transceiver module 501 is specifically used to:
  • the first speed is the speed of the target point relative to the radar device
  • the first distance is the distance between the target point and the radar device
  • the echo intensity threshold is determined according to the N-dimensional antenna data associated with the first velocity and the first distance; then according to the echo intensity threshold and the echo signal of at least one candidate point closer to the target point, Two speeds and a second distance, the distance between each candidate point closer to the target point and the radar device is a second distance, and the difference between the second distance and the first distance is within a preset distance range;
  • the N-dimensional antenna data associated with the first speed and the first distance and the N-dimensional antenna data associated with the second speed and the second distance are used as the N-dimensional antenna data of the target point.
  • processing module 502 is specifically used to:
  • the first angle of the target point is calculated according to the K-dimensional antenna data and the super-resolution method.
  • the present application also provides a radar device 600. Please refer to FIG. 6.
  • An embodiment of the radar device in the embodiment of the present application includes:
  • the processor 601, the memory 602, and the input-output device 603 are respectively connected to the bus 604, and the memory stores computer instructions.
  • the processing module 502 in the foregoing embodiment may specifically be the processor 601 in this embodiment, so the specific implementation of the processor 601 will not be described in detail.
  • the transceiver module 501 in the foregoing embodiment may specifically be the input-output device 603 in this embodiment, so the specific implementation of the input-output device 603 will not be described in detail.
  • the disclosed system, device, and method may be implemented in other ways.
  • the device embodiments described above are only schematic.
  • the division of the units is only a division of logical functions.
  • there may be other divisions for example, multiple units or components may be combined or Can be integrated into another system, or some features can be ignored, or not implemented.
  • the displayed or discussed mutual coupling or direct coupling or communication connection may be indirect coupling or communication connection through some interfaces, devices or units, and may be in electrical, mechanical or other forms.
  • the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
  • each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units may be integrated into one unit.
  • the above integrated unit may be implemented in the form of hardware or software functional unit.
  • the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it may be stored in a computer-readable storage medium.
  • the technical solution of the present application essentially or part of the contribution to the existing technology or all or part of the technical solution can be embodied in the form of a software product, the computer software product is stored in a storage medium , Including several instructions to enable a computer device (which may be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in the embodiments of the present application.
  • the aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk and other media that can store program code .

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  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
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Abstract

一种测角方法以及雷达设备，应用于雷达系统，用于降低测量目标点的第一角度的复杂度。方法包括：利用雷达设备获取目标点的N维天线数据，N为大于2的正整数；将N维天线数据进行降维，得到K维天线数据，K为小于N的正整数；根据K维天线数据计算目标点的第一角度，第一角度为目标点相对于雷达设备的角度。

Description

测角方法以及雷达设备 技术领域
本申请涉及雷达技术，尤其一种测角方法以及雷达设备。
背景技术
自动驾驶为未来汽车的重要功能，而车载雷达设备，是未来汽车中实现自动驾驶功能中的一个重要组成部分。车载雷达设备用于检测目标车辆当前驾驶区域内的路面情况，例如检测该区域内的障碍物，或者用于自适应巡航控制等。
目前，为了提高车载雷达测量目标物体相对于目标车辆的角度估计值的精度，通过增加天线数量并配合超分辨率方法进行角度估计来计算并得到高精度的角度估计值。即车载雷达设备可以获取N根天线数据，然后根据该N根天线数据和超分辨率方法来计算得到角度估计值，这样计算得到的角度估计值更为精准。
但是，由于天线数量较多时，即车载雷达设备获取得到的N根天线数据为N维天线数据，那么使用超分辨率方法进行计算时，由于N维天线数据的维度较多，导致其计算的复杂度比较高。
发明内容
本申请实施例提供了一种测角方法以及雷达设备，应用于雷达系统，用于降低测量目标点的第一角度的复杂度。
本申请实施例的第一方面提供一种测角方法，包括：
在雷达系统中，可以利用雷达设备获取目标点的N维天线数据，该N为大于2的正整数；然后通过雷达设备将该N维天线数据进行降维，得到K维天线数据，K为小于N的正整数；再根据该K维天线数据计算该目标点的第一角度，其中，该第一角度为目标点相对于该雷达设备的角度。在本实施例中，将目标点的N维天线数据进行降维，得到K维天线数据，然后再根据该K维天线数据计算该目标点的第一角度，即通过将N维天线数据进行降维来获取降维后的K维天线数据，由于K维天线数据的维度相对于N维天线数据的维度较少，因此，降低了计算第一角度的复杂度。
一种可能的实现方式中，将N维天线数据进行降维，得到K维天线数据的步骤包括：首先，将该N维天线数据拆分为K个子阵的天线数据；然后，将K个子阵的天线数据通过波束成型进行降维，得到K维天线数据。在该可能的实现方式中，提供了一种具体的对N维天线数据进行降维的方法，在实际应用中，提升了方案可实现性和实用性。
另一种可能的实现方式中，将N维天线数据进行降维，得到K维天线数据之前，该方法还可以包括：当该目标点位于辐射区域中的远场区域的情况下，可以通过角度快速傅里叶变换(fast fourier transform,FFT)对该N维天线数据进行计算，得到第二角度，其中，该第二角度为该目标点相对于该雷达设备的角度，且该第二角度的精度低于该第一角度的精度，该辐射区域可以为该雷达设备通过雷达波形进行辐射所覆盖的区域。该第二角 度为该目标点相对于该雷达设备的角度，且该第二角度的精度低于该第一角度的精度；然后，将该K个子阵的天线数据通过波束成型进行降维，得到K维天线数据的步骤可以包括：首先，确定K个子阵中的每个子阵所对应的波束成型向量，该波束成型向量包括目标天线在该第二角度方向所对应的权值，该目标天线为该每个子阵所包括的天线数据对应的接收天线，且该目标天线对应的接收天线的数量大于或者等于1；然后，将该K个子阵中的每个子阵的天线数据乘以该每个子阵所对应的波束成型向量，得到该K维天线数据。在该可能的实现方式中，当目标点位于该辐射区域中的远场区域时，提供了将K个子阵的天线数据通过波束成型进行降维的具体过程，再根据降维后的数据计算目标点的第一角度，这样提高了抗干扰性能和抗杂波性能，从而提高第一角度的精度。在实际应用中，提升了方案的可实现性和完整性。
另一种可能的实现方式中，将该N维天线数据进行降维，得到K维天线数据之前，该方法还可以包括：首先，当该目标点位于辐射区域中的近场区域的情况下，可以通过波束成型对该N维天线数据进行计算，得到该雷达设备中的每根接收天线与该目标点的角度和距离，该辐射区域为该雷达设备通过雷达波形进行辐射所覆盖的区域；将K个子阵的天线数据通过波束成型进行降维，得到K维天线数据的步骤可以包括：首先，可以根据每根接收天线数据与该目标点的角度和距离确定每根接收天线的权值；然后根据每根接收天线数据的权值确定K个子阵中的每个子阵所对应的波束成型向量，该波束成型向量可以包括目标天线相对于目标点的角度和距离所对应的权值，该目标天线为该每个子阵所包括的天线数据对应的接收天线，且该目标天线对应的接收天线的数量大于或者等于1。在该可能的实现方式中，当目标点位于辐射区域的近场区域中时，提供了通过波束成型进行降维的具体过程，再根据降维后的数据计算目标点的第一角度，这样提高了抗干扰性能和抗杂波性能，从而提高第一角度的精度。在实际应用中，提升了方案的可实现性和完整性。
另一种可能的实现方式中，将该N维天线数据进行降维，得到K维天线数据的步骤可以包括：首先，将N维天线数据拆分为M个K维天线数据，其中，M＝N-K+1；然后将该M个K维天线数据通过空间平滑进行降维，得到K维天线数据。在该可能的实现方式中，提供了另一种具体的对N维天线数据进行降维的方法，在实际应用中，提升了方案多样性和实用性。
另一种可能的实现方式中，将该M个K维天线数据通过空间平滑进行降维，得到K维天线数据可以包括：首先，确定该M个K维天线数据中每个K维天线数据的列向量，以及确定该每个K维天线行数据的列向量的共轭转秩；然后，将每个K维天线数据的列向量乘以对应的每个K维天线数据的列向量的共轭转秩，得到该K维天线数据。在该可能的实现方式中，提供了将K个子阵的天线数据通过空间平滑进行降维的具体降维过程，在实际应用中，提升了方案的可实现性和完整性。
另一种可能的实现方式中，在获取N维天线数据之前，该方法还可以包括：首先，获取该雷达设备检测到的多个候选点的速度和距离，然后根据该多个候选点的速度和距离，采用恒虚警率检测算法，确定该目标点。在该可能的实现方式中，提供了一种具体的确定目标点的方法，在实际应用中，提升了方案的完整性和实用性。
另一种可能的实现方式中，利用雷达设备获取目标点的N维天线数据可以包括：首先，利用该雷达设备获取与第一速度和第一距离关联的N维天线数据，该第一速度为该目标点相对于该雷达设备的速度，该第一距离为该目标点与该雷达设备的距离；然后，根据与该第一速度和第一距离关联的N维天线数据可以确定回波强度阈值；再根据该回波强度阈值和距离该目标点较近的至少一个候选点的回波信号，通过计算得到第二距离和第二速度，距离该目标点较近的每一候选点与该雷达设备之间的距离为第二距离，该第二距离与该第一距离的差值在预设范围内；将与该第一速度和第一距离关联的N维天线数据以及与该第二速度和第二距离关联的N维天线数据作为该目标点的N维天线数据。在该可能的实现方式中，提供了一种雷达设备获取N维天线数据的获取方式以及该N维天线数据具体所包括的数据，通过目标点的速度和距离来获取对应的N维天线数据，在实际应用中，提升了方案的可实现性。
另一种可能的实现方式中，根据K维天线数据计算该目标点的第一角度可以包括：根据该K维天线数据以及超分辨率方法计算该目标点的第一角度。在该可能的实现方式中，提供了一种具体的计算目标点的第一角度的方法，提升了方案的实用性。
本申请实施例第二方面提供了一种雷达设备，该雷达具有实现上述第一方面雷达设备行为的功能，该功能可以通过硬件实现，也可以通过硬件执行相应的软件实现。该硬件或软件包括一个或多个与上述功能对应的模块。
本申请实施例中第三方面提供了一种雷达设备，该雷达设备包括：处理器、存储器、输入输出设备以及总线；该处理器、存储器、输入输出设备分别与该总线相连，该存储器中存储有计算机指令；该处理器在执行该存储器中的计算机指令时，该存储器中存储有计算机指令；该处理器在执行该存储器中的计算机指令时，用于实现如第一方面任意一种实现方式。
本申请实施例第四方面提供了一种包括指令的计算机程序产品，其特征在于，当其在计算机上运行时，使得该计算机执行如第一方面中的任一种的实现方式。
本申请实施例第五方面提供了一种计算机可读存储介质，其特征在于，包括指令，当该指令在计算机上运行时，使得计算机执行如第一方面中任一种实现方式。
本申请实施例提供的技术方案中，利用雷达设备获取目标点的N维天线数据，该N为大于2的正整数；然后，将该N维天线数据进行降维，得到K维天线数据，K为小于N的正整数；然后根据该K维天线数据计算该目标点的第一角度，该第一角度为该目标点相对于该雷达设备的角度。通过本申请的技术方案，将目标点的N维天线数据进行降维，得到K维天线数据，然后再根据该K维天线数据计算该目标点的第一角度，即通过将N维天线数据进行降维来获取降维后的K维天线数据，由于K维天线数据的维度相对于N维天线数据的维度较少，因此降低了计算第一角度的复杂度。
附图说明
图1A为本申请实施例中一个应用场景框架示意图；
图1B为本申请实施例中的测角方法的一个场景示意图；
图2为本申请实施例中的测角方法的一个实施例示意图；
图3为本申请实施例中的测角方法的另一个场景示意图；
图4为本申请实施例中的测角方法的另一个场景示意图；
图5为本申请实施例中的雷达设备的一个结构示意图；
图6为本申请实施例中的雷达设备的另一个结构示意图。
具体实施方式
下面将结合本申请实施例中的附图，对本申请实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域技术人员所获得的所有其他实施例，都属于本发明保护的范围。
本申请的说明书和权利要求书及上述附图中的术语“第一”、“第二”、“第三”、“第四”等(如果存在)是用于区别类似的对象，而不必用于描述特定的顺序或先后次序。应该理解这样使用的数据在适当情况下可以互换，以便这里描述的实施例能够以除了在这里图示或描述的内容以外的顺序实施。此外，术语“包括”和“具有”以及他们的任何变形，意图在于覆盖不排他的包含，例如，包含了一系列步骤或单元的过程、方法、系统、产品或设备不必限于清楚地列出的那些步骤或单元，而是可包括没有清楚地列出的或对于这些过程、方法、产品或设备固有的其它步骤或单元。
本申请实施例提供一种测角方法以及雷达设备，应用于雷达系统，用于降低测量目标点的第一角度的复杂度。
请参阅图1A，图1A为本申请实施例提供的一种应用场景示意图。在图1中，车辆中安装有雷达系统，该雷达系统包括车载雷达设备，该车载雷达设备中的发射天线发射雷达波形，然后在车载雷达设备中的N根接收天线接收目标点针对该发射天线发射的雷达波形返回的雷达回波波形。值得注意的是，该目标点可以为位于该车载雷达设备的测量范围内的障碍物或者行人等，该第一角度可以为雷达设备根据N根接收天线接收到的N维天线数据进行降维，得到K维天线数据，然后根据降维后的K维天线数据计算得到目标点相对于车载雷达设备的角度，其中，该N维天线数据可以包括接收天线接收到的雷达回波波形的相位、幅度、频率等。
上述雷达系统可以为正交频分复用(orthogonal frequency division multiplexing，OFDM)雷达系统、调频连续波(frequency modulated continuous wave，FMCW)雷达系统或者调相连续波(phase modulated continuous wave，PMCW)雷达系统，本申请不对具体的雷达系统做限定。
在实际应用中，可以根据需要在该车辆中安装多个车载雷达设备，该多个车载雷达设备分别用于进行近距测量、中距测量或长距测量。或者说，可以在该车辆中安装多个车载雷达设备，该多个车载雷达设备中包括用于进行近距测量的，用于进行中距测量的，以及用于进行远距测量的车载雷达设备中的至少一种。应当知道的是，前述的近距测量、中距测量或长距测量均是以该车辆为参考点的。因此，近距测量时测量到的最远端与该车辆的距离小于中距测量时测量到的最远端与该车辆的距离，且中距测量时测量到的最远端与该 车辆的距离小于长距测量时测量到的最远端与该车辆的距离。
目前，对于目标点与车载雷达设备的角度的计算主要是通过超分辨率方法来进行计算。通过超分辨率方法来计算目标点相对于车载雷达设备的角度，主要是根据N维天线数据进行计算得到其相关矩阵，然后根据相关矩阵通过奇异值分解计算得到噪声空间的积，再通过该噪声空间的积以及该N维天线数据的阵列响应系数来计算该目标点相对于该车载雷达设备的角度；其中，通过相关矩阵进行奇异值分解的过程的复杂度与相关矩阵的维度相关，相关矩阵的维度越多，计算的复杂度越高，而相关矩阵与N维天线数据的维度相同，所以N越大，计算的复杂度越高。因此，车载雷达设备通过N根天线获取该目标点的N维天线数据，然后根据该N维天线数据，使用超分辨率方法进行计算，得到该目标点相对于车载雷达设备的角度。其中，上述N为大于2的正整数，但是，该计算方法的复杂度与天线数量N有关系，即N越大则计算的复杂度越高。
其中，需要说明的是，图1A的示例仅仅是为了说明本申请的一种应用场景，在实际应用中，本申请的测角方法同样适用于其他类型的雷达系统，例如，超声波雷达系统、成像雷达系统或者机场跑道检查异物雷达系统等，本申请不对此做限定。而本申请中雷达设备可以为车载雷达设备、安防设备或者跑道异物检测设备等，本申请也不对此做限定。
有鉴于此，本申请实施例提出了一种测角方法，用于降低测量目标点相对于雷达设备的角度的复杂度。本申请的技术方案中，首先利用雷达设备获取目标点的N维天线数据，该N为大于2的正整数；然后，将该N维天线数据进行降维，得到K维天线数据，K为小于N的正整数；然后根据该K维天线数据计算该目标点的第一角度，该第一角度为该目标点相对于该雷达设备的角度。通过本申请的技术方案，将N维天线数据进行降维，得到K维天线数据，然后再根据该K维天线数据计算该目标点的第一角度，即通过将N维天线数据进行降维来获取降维后的K维天线数据，由于K维天线数据的维度相对于N维天线数据的维度较少，因此降低了计算第一角度的复杂度。
本申请实施例中，雷达设备将N维天线数据进行降维，得到K维天线数据，具体可以是雷达设备通过波束成型的方式对N维天线数据进行降维，其具体过程是将N维天线数据拆分为K个子阵的天线数据，然后确定每个子阵所对应的波束成型向量，再将每个子阵的天线数据乘以对应的波束成型向量，得到K维天线数据；其次，还可以通过空间平滑的方式对N维天线数据进行降维，具体过程是将N维天线数据拆分为M个K维天线数之后，其中，M＝N-K+1，然后确定该M个K维天线数据中每个K维天线数据的列向量以及每个K维天线数据的列向量的共轭转秩；然后将每个K维天线数据的列向量乘以对应的每个K维天线数据的列向量的共轭转秩，得到K维天线数据；具体本申请对此不做限定。在后续的实施例中，仅以雷达设备对该N维天线数据通过波束成型进行降维，得到K维天线数据为例进行说明。
本申请实施例中，雷达设备在将N维天线数据通过波束成型进行降维之前，雷达设备可以先确定目标点位于辐射区域中的远场区域还是近场区域，其中，该辐射区域为该雷达设备通过雷达波形进行辐射所覆盖的区域；其中，雷达设备确定目标点的位置的具体过程为：根据公式d f＝2D 2/λ，其中，D为雷达设备中两根接收天线之间的最大距离，λ为雷 达设备发射的雷达波形的波长，那么当目标点相对于雷达设备的第一距离大于该d f时，则雷达设备可以确定该目标点位于辐射区域中的远场区域；当目标点相对于雷达设备的第一距离小于该d f时，那么此时雷达设备可以确定该目标点位于辐射区域中的近场区域。
本申请实施例中，当目标点位于辐射区域的远场区域时，那么雷达设备可以优选通过角度快速傅里叶变换FFT对该N维天线数据进行计算，得到该目标点的第二角度，该第二角度为该目标点相对该雷达设备的角度，且该第二角度的精度低于该第一角度的精度，即第二角度为一个粗略计算得到的目标点相对于雷达设备的角度；再将N维天线数通过波束成型在该第二角度方向上进行降维。当目标点位于远场区域时，先确定目标点的第二角度，然后在该第二角度的方向通过波束成型进行降维，再根据降维后的数据计算目标点的第一角度，这样提高了抗干扰性能和抗杂波性能，从而提高第一角度的精度。
当目标点位于辐射区域的近场区域时，那么可以通过波束成型对N维天线数据进行计算，得到该雷达设备的每根接收天线的角度和距离；然后雷达设备确定该每根接收天线的角度和距离所对应的每根接收天线的权值，并根据该每根接收天线的权值确定K个子阵中的每个子阵所对应的波束成型向量，该波束成型向量包括目标天线相对于目标点的角度和距离所对应的权值，该目标天线为该每个子阵所包括的天线数据对应的接收天线，且该目标天线对应的接收天线的数量大于等于1，再将K个子阵中每个子阵的天线数据乘以该每个子阵所对应的波束成型向量，得到K维天线数据；下面通过举例进行说明：假设K个子阵中的子阵1包括接收天线1、接收天线2和接收天线3的天线数据，那么可以确定波束成型向量包括该接收天线1相对于目标点的角度和距离所对应的第一权值、接收天线2相对于目标点的角度和距离所对应的第二权值和接收天线3相对于目标点的角度和距离所对应的第三权值；然后将该子阵1的接收天线1的接收到的天线数据乘以该第一权值，将子阵1的接收天线2接收到的天线数据乘以该第二权值，将子阵1的接收天线3接收到的天线数乘以该第三权值，并将结果相加得到该K维天线数据的第一维的标量；针对K维天线数据的其他维度的标量的计算与第一维的标量计算过程类似，具体此处不再赘述。当目标点位于近场区域时，先确定每根接收天线相对目标点的角度和距离，然后在每根接收天线的角度和距离的方向通过波束成型进行降维，再根据降维后的数据计算目标点的第一角度，这样提高了抗干扰性能和抗杂波性能，从而提高第一角度的精度。在后续的实施例中，仅以该目标点位于辐射区域的远场区域为例进行说明。
本申请实施例中，雷达设备根据该K维天线数据计算该目标点的第一角度可以是雷达设备根据该K维天线数据以及该超分辨率方法计算该第一角度。
本申请实施例中，雷达设备可以从N根接收天线接收到的雷达回波中获取多个候选点的速度和距离，然后根据该多个候选点的速度和距离，采用恒虚警率检测算法确定该目标点，其中，该多个候选点的速度和距离具体可以雷达设备根据雷达回波与发射天线的发射雷达波形的相位差、时间差等计算得到的多个候选点与雷达设备的速度和距离，需要说明的是，在确定目标点的同时，此时目标点相对于雷达设备的速度和该目标点相对于雷达设备的距离也就确定了，本申请以目标点相对于雷达设备的速度为第一速度，第一目标点相对于雷达设备的距离为第一距离为例进行说明。
本申请实施例中，雷达设备可以通过采样接收天线接收到的雷达回波以及多普勒效应获取如图1B所示的每组速度和距离关联的N维天线数据，每组速度和距离在每根接收天线都会有对应一组天线数据，那么接收天线有N根，即每组速度和距离都关联有N维天线数据；那么在本申请中，该目标点相对于雷达设备的速度为第一速度，该目标点相对于雷达设备的距离为第一距离，那么由图1B可知，该目标点的N维天线数据可以包括该第一速度和第一距离关联的N维天线数据。可选的，该雷达设备该可以根据该第一速度和第一距离关联的N维天线数据确定该目标点的雷达回波强度，然后根据该雷达回波强度设置回波强度阈值，然后根据确定离该目标点较近的其他候选点的回波信号的强度大于该回波强度阈值，那么雷达设备计算其他候选点的相对于雷达设备的第二速度和第二距离；其中，其他候选点的回波信号是指其他候选点反射该雷达发射波形的雷达回波，并且该第二距离与目标点的第一距离的差值在预设范围内；然后，将该第二速度和第二距离关联的N维天线数据也作为该目标点的N维天线数据。在后续的实施例中，仅以该目标点的N维天线数据包括该第一速度和第一距离关联的N维天线数据为例进行说明。
下面从雷达设备的角度，对本申请实施例中测角方法进行介绍，请参阅图2，本申请实施例中的测角方法的一个实施例包括：
201、雷达设备获取多个候选点的速度和距离。
在雷达系统，雷达设备包括一个或者多个发射通道和接收通道，N tx个发射天线通过预置的模式发送雷达波形，N rx个接收天线同时接收雷达回波，而通过N rx个接收天线接收到的数据可以用于测量目标点的第二角度。其中，预置的模式可以是N tx个发射天线轮流切换发射相同的雷达波形，每次一个发射天线工作，即本申请N维天线数据可以理解为N个接收天线接收到的天线数据，N为实体接收天线的数量N rx；该预置的模式也可以是N tx个发射天线使用不同码字同时发射雷达波形；然后N rx个接收天线接收N tx次相同的雷达波形，则N维天线数据可以理解为N＝N txN rx个接收通道到的数据，具体雷达设备的接收N维天线数据的方式本申请不做限定。然后雷达设备可以从N根接收天线接收到的雷达回波中计算多个候选点的速度和距离，该多个候选点可以理解为在雷达设备通过雷达波形进行辐射的辐射区域内出现的物体，例如，障碍物或者行人等。
202、雷达设备根据该多个候选点的速度和距离、以及恒虚警率检测算法确定目标点。
如图1B所示，雷达设备可以根据恒虚警率检测算法以及多个候选点的速度和距离来确定第一速度和第一距离所对应的单元的回波强度大于预设值，那么雷达设备可以确定该第一速度和第一距离所对应的单元中存在目标点，然后此时也可以确定该目标点相对于雷达设备速度为第一速度，该目标点相对于雷达设备的距离为第一距离。
203、雷达设备获取第一速度和第一距离关联的N维天线数据。
雷达设备确定了该目标点所对应的第一距离和第一速度，那么可以从N根天线获取到的N维天线数据中获取与第一距离和第一速度关联的N维天线数据。该N维天线数据中的每维天线数据包括与第一速度和第一距离关联的天线数据，即如图1B所示，N维天线数据可以包括与第一速度和第一距离关联的N维天线数据，即接收天线1至接收天线N接收到的与第一速度和第一距离关联的天线数据。
204、雷达设备确定该目标点位于辐射区域中的远场区域。
雷达设备根据公式d f＝2D 2/λ确定d f的值，其中，D为雷达设备中接收天线之间的最大距离，λ为雷达设备发射的雷达波形的波长；然后，雷达设备判断该目标点相对于雷达设备的第一距离是否大于该d f，若是，则雷达设备可以确定该目标点位于该辐射区域中的远场区域；其次，若不大于，则雷达设备可以确定该目标点位于辐射区域中的近场区域。
205、雷达设备对该N维天线数据进行角度FFT计算，得到第二角度。
雷达设备确定目标点位于远场区域之后，那么雷达设备可以通过角度FFT对N维天线数据进行计算，得到第二角度；其中，该第二角度为目标点相对于雷达设备的角度，且该第二角度的精度低于该第一角度的精度，可以理解为该第二角度为一个粗略计算得到的目标点相对于雷达设备的角度。
206、雷达设备将N维天线数据拆分为K个子阵的天线数据。
雷达设备获取到N维天线数据之后，将该N维天线数据拆分为K个子阵的天线数据，其中K为小于N的正整数，每个子阵天线数据的维度不限定，且每个子阵天线数据的维度可以相同，也可以不相同。如图3所示，雷达设备将该N维天线数据划分为K个子阵的天线数据，然后将K个子阵在第二角度的方向上通过波束成型进行降维，可以提高接收雷达回波信号与干扰信号的信干噪比，从而提高测量的第一角度的精度。
207、雷达设备确定K个子阵中的每个子阵所对应的波束成型向量。
雷达设备得到K个子阵的天线数据，然后确定K个子阵中的每个子阵所对应的波束成型向量，其中，该波束成型向量包括目标天线在该第二角度方向上所对应的权值，该目标天线可以理解为K个子阵中的每个子阵的天线数据所对应的接收天线，并且该目标天线所对应的接收天线的数量大于或者等于1.下面通过举例说明雷达设备确定每个子阵的波束成型向量的具体过程：如图3所示，假设子阵1天线数据包括接收天线1的天线数据、接收天线2的天线数据和接收天线3的天线数据，那么雷达设备可以确定子阵1所包括的天线数据所对应的天线包括接收天线1、接收天线2和接收天线3，然后该雷达设备可以确定接收天线1在该目标点的第二角度的方向的第一权值、该接收天线2在该目标点的第二角度方向上的第二权值以及接收天线3在该目标点的第二角度方向上的第三权值，那么该子阵1所对应的波束成型向量即为(第一权值，第二权值，第三权值)；针对其他子阵所对应的波束成型向量的计算过程与子阵1的波束成型向量类似，具体此处不再赘述。
208、雷达设备将K个子阵中的每个子阵的天线数据乘以对应的每个子阵所对应的波束成型向量，得到K维天线数据。
雷达设备将K个子阵中的每个子阵的天线数据乘以对应的每个子阵所对应的波束成型向量，得到K维天线数据；具体过程为：将接收天线1的天线数据乘以该第一波束成型权值得到第一标量，以及将接收天线2的天线数据乘以该第二波束成型权值得到第二标量，将接收天线3的天线数据乘以该第三波束成型权值得到第三标量，然后再将该第一标量、第二标量和第三标量相加得到K维天线数据中的第一维的标量。另外，K维天线数据中其他维的标量也与前述说明子阵1通过计算得到第一维的标量的过程类似，具体不再赘述。上述波束成型降维具体可以参阅图4所示，图4为车载雷达设备对N维天线数据进行降维 的一个示意图，假设第二角度为波束1的方向，那么车载雷达设备在该波束1的方向上对N维天线数据进行降维，得到K维天线数据；假设第二角度为波束2的方向或者波束3的方向，那么车载设备在该波束2的方向上或者波束3的方向上对N维天线数据进行降维，得到K维天线数据。
209、雷达设备根据该K维天线数据和超分辨率方法计算该目标点的第一角度。
雷达设备可以根据该K维天线数据和超分辨率方法计算该目标点的第一角度，其中，超分辨率方法可以为求根多目标分类方法(root-MUSIC，root multiple signal classification),还可以是最小方差无失真响应方法或者旋转不变信号参数估计方法等，具体本申请不做限定。例如，假设N＝192，即192根接收天线，如果直接使用超分辨率方法进行计算，那么其计算的复杂度量级为Nlog(N)+P(N/2) 3＝1456+26542080，其中，P为复杂度系数，假设P＝30；而通过本申请的方法进行计算，其计算的复杂度量级为Nlog 2(N)+T(KN/2+P*K^2)＝1456+5440其中，N为N维天线数据的维度，T为目标点的数量，K为降维后的K维天线数据的维度，P＝30,T＝20,K＝2。因此，由上述对计算的复杂量级的计算结果可知，现有的计算方式复杂度高，且受杂波和干扰波的影响大，使得其计算的角度的精度较低，而本申请的计算方式的复杂度较低，并且通过波束成型降维的方式能够提高抗干扰性能和抗杂波的性能，因此，再根据降维后的数据计算得到的第一角度的更为精准。其次，当K＝2时，此时该K维天线数据为二维矩阵，其矩阵秩接近1，在进行超分辨率方法计算时，不需要使用矩阵秩分解和秩的估计，其计算的复杂度可大大降低。
本申请实施例中，利用雷达设备获取目标点的N维天线数据，该N为大于2的正整数；然后，将该N维天线数据进行降维，得到K维天线数据，K为小于N的正整数；然后根据该K维天线数据计算该目标点的第一角度，该第一角度为该目标点相对于该雷达设备的角度。通过本申请的技术方案，将目标点的N维天线数据进行降维，得到K维天线数据，然后再根据该K维天线数据计算该目标点的第一角度，即通过将N维天线数据进行降维来获取降维后的K维天线数据，由于K维天线数据的维度相对于N维天线数据的维度较少，因此降低了计算第一角度的复杂度。
上面对本申请实施例中的测角方法进行了描述，下面对本申请实施例中的雷达设备进行描述，请参阅图5，本申请实施例中雷达设备的一个实施例包括：
收发模块501，用于获取目标点的N维天线数据，N为大于2的正整数；
处理模块502，用于将该N维天线数据进行降维，得到K维天线数据，K为小于N的正整数；根据该K维天线数据计算该目标点的第一角度，该第一角度为该目标点相对于该雷达设备的角度。
一种可能的实现方式中，该处理模块502具体用于：
将该N维天线数据拆分为K个子阵的天线数据；
将该K个子阵的天线数据通过波束成型进行降维，得到K维天线数据。
另一种可能的实现方式中，该处理模块502还用于：
在该目标点位于辐射区域中的远场区域的情况下，通过角度快速傅里叶变换FFT对该 N维天线数据进行计算，得到第二角度，该第二角度为该目标点相对于该雷达设备的角度，且该第二角度的精度低于该第一角度的精度，该辐射区域为该雷达设备通过雷达波形进行辐射所覆盖的区域。
该处理模块502具体用于：
确定K个子阵中的每个子阵所对应的波束成型向量，该波束成型向量包括目标天线在该第二角度方向所对应的权值，该目标天线为每个子阵所包括的天线数据对应的接收天线，且该目标天线对应的接收天线的数量大于或者等于1；
将该K个子阵中的每个子阵的天线数据乘以每个子阵所对应的波束成型向量，得到K维天线数据。
另一种可能的实现方式中，该处理模块502还用于：
在该目标点位于辐射区域中的近场区域的情况下，通过波束成型对该N维天线数据进行计算，得到雷达设备中的每根接收天线与该目标点的角度和距离，该辐射区域为该雷达设备通过雷达波形进行辐射所覆盖的区域；
该处理模块502具体用于：
根据该每根接收天线与该目标点的角度和距离确定每根接收天线的权值；
根据每根接收天线的权值确定K个子阵中的每个子阵所对应的波束成型向量，该波束成型向量包括目标天线相对于目标点的角度和距离所对应的权值，该目标天线为该每个子阵所包括的天线数据对应的接收天线，且该目标天线对应的接收天线的数量大于或者等于1；
将该K个子阵中的每个子阵的天线数据乘以该每个子阵所对应的波束成型向量，得到K维天线数据。
另一种可能的实现方式中，该处理模块502具体用于：
将该N维天线数据拆分为M个K维天线数据,其中，M＝N-K+1；
将该M个K维天线数据通过空间平滑进行降维，得到K维天线数据。
另一种可能的实现方式中，该处理模块502具体用于：
确定该M个K维天线数据中每个K维天线数据的列向量，以及确定每个K维天线数据的列向量的共轭转秩；
将该每个K维天线数据的列向量乘以对应的每个K维天线数据的列向量的共轭转秩，得到K维天线数据。
另一种可能的实现方式中，该收发模块501还用于：
获取该雷达设备检测到的多个候选点的速度和距离；
该处理模块502还用于：
根据该多个候选点的速度和距离，采用恒虚警率检测算法，确定该目标点。
另一种可能的实现方式中，该收发模块501具体用于：
利用雷达设备获取与该第一速度和第一距离关联的N维天线数据，该第一速度为该目标点相对于该雷达设备的速度，该第一距离为该目标点与该雷达设备的距离；
根据与该第一速度和第一距离关联的N维天线数据确定回波强度阈值；再根据该回波 强度阈值和距离该目标点较近的至少一个候选点的回波信号，通过计算得到第二速度和第二距离，距离该目标点较近的每一候选点与该雷达设备之间的距离为第二距离，该第二距离与该第一距离的差值在预设距离范围内；
将与该第一速度和第一距离关联的N维天线数据以及与该第二速度和第二距离关联的N维天线数据作为该目标点的N维天线数据。
另一种可能的实现方式中，该处理模块502具体用于：
根据该K维天线数据以及超分辨率方法计算该目标点的第一角度。
本申请还提供一种雷达设备600，请参阅图6，本申请实施例中雷达设备一个实施例包括：
处理器601、存储器602、输入输出设备603以及总线604；
一种可能的实现方式中，该处理器601、存储器602、输入输出设备603分别与总线604相连，该存储器中存储有计算机指令。
前述实施例中的处理模块502具体可以是本实施例中的处理器601，因此该处理器601的具体实现不再赘述。前述实施例中的收发模块501则具体可以是本实施例中的输入输出设备603，因此该输入输出设备603的具体实现不再赘述。
所属领域的技术人员可以清楚地了解到，为描述的方便和简洁，上述描述的系统，装置和单元的具体工作过程，可以参考前述方法实施例中的对应过程，在此不再赘述。
在本申请所提供的几个实施例中，应该理解到，所揭露的系统，装置和方法，可以通过其它的方式实现。例如，以上所描述的装置实施例仅仅是示意性的，例如，所述单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个单元或组件可以结合或者可以集成到另一个系统，或一些特征可以忽略，或不执行。另一点，所显示或讨论的相互之间的耦合或直接耦合或通信连接可以是通过一些接口，装置或单元的间接耦合或通信连接，可以是电性，机械或其它的形式。
所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本实施例方案的目的。
另外，在本申请各个实施例中的各功能单元可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中。上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能单元的形式实现。
所述集成的单元如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本申请的技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案的全部或部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质中，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)执行本申请各个实施例所述方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(ROM，Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、磁碟或者光盘等各种可以存储程序代码的介质。
以上所述，以上实施例仅用以说明本申请的技术方案，而非对其限制；尽管参照前述实施例对本申请进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本申请各实施例技术方案的精神和范围。

Claims (20)

  1. 一种测角方法，其特征在于，所述方法应用于雷达系统，所述方法包括：
    利用雷达设备获取目标点的N维天线数据，N为大于2的正整数；
    将所述N维天线数据进行降维，得到K维天线数据，K为小于N的正整数；
    根据所述K维天线数据计算所述目标点的第一角度，所述第一角度为所述目标点相对于所述雷达设备的角度。
  2. 根据权利要求1所述的方法，其特征在于，所述将所述N维天线数据进行降维，得到K维天线数据的步骤包括：
    将所述N维天线数据拆分为K个子阵的天线数据；
    将所述K个子阵的天线数据通过波束成型进行降维，得到K维天线数据。
  3. 根据权利要求1或2所述的方法，其特征在于，所述将N维天线数据进行降维，得到K维天线数据之前，所述方法还包括：
    在所述目标点位于辐射区域中的远场区域的情况下，通过角度快速傅里叶变换FFT对所述N维天线数据进行计算，得到第二角度，所述第二角度为所述目标点相对于所述雷达设备的角度，且所述第二角度的精度低于所述第一角度的精度，所述辐射区域为所述雷达设备通过雷达波形进行辐射所覆盖的区域；
    所述将所述K个子阵的天线数据通过波束成型进行降维，得到K维天线数据的步骤包括：
    确定K个子阵中的每个子阵所对应的波束成型向量；
    将所述K个子阵中的每个子阵的天线数据乘以每个子阵所对应的波束成型向量，得到所述K维天线数据。
  4. 根据权利要求1或2所述的方法，其特征在于，所述将N维天线数据进行降维，得到K维天线数据之前，所述方法还包括：
    在所述目标点位于辐射区域中的近场区域的情况下，通过波束成型对所述N维天线数据进行计算，得到所述雷达设备中的每根接收天线与所述目标点的角度和距离，所述辐射区域为所述雷达设备通过雷达波形进行辐射所覆盖的区域；
    所述将所述K个子阵的天线数据通过波束成型进行降维，得到K维天线数据的步骤包括：
    根据所述每根接收天线与所述目标点的角度和距离确定每根接收天线的权值；
    根据每根接收天线的权值确定K个子阵中的每个子阵所对应的波束成型向量；
    将所述K个子阵中的每个子阵的天线数据乘以每个子阵所对应的波束成型向量，得到所述K维天线数据。
  5. 根据权利要求1所述的方法，其特征在于，所述将所述N维天线数据进行降维，得到K维天线数据的步骤包括：
    将所述N维天线数据拆分为M个K维天线数据,其中，M＝N-K+1；
    将所述M个K维天线数据通过空间平滑进行降维，得到K维天线数据。
  6. 根据权利要求5所述的方法，其特征在于，所述将所述M个K维天线数据通过空间 平滑进行降维，得到K维天线数据包括：
    确定所述M个K维天线数据中每个K维天线数据的列向量，以及确定每个K维天线数据的列向量的共轭转秩；
    将所述每个K维天线数据的列向量乘以对应的每个K维天线数据的列向量的共轭转秩，得到所述K维天线数据。
  7. 根据权利要求1至6中的任一项所述的方法，其特征在于，所述获取N维天线数据之前，所述方法还包括：
    获取所述雷达设备检测到的多个候选点的速度和距离；
    根据所述多个候选点的速度和距离，采用恒虚警率检测算法，确定所述目标点。
  8. 根据权利要求1至7中的任一项所述的方法，其特征在于，所述利用雷达设备获取目标点的N维天线数据，包括：
    利用所述雷达设备获取与第一速度和第一距离关联的N维天线数据，所述第一速度为所述目标点相对于所述雷达设备的速度，所述第一距离为所述目标点与所述雷达设备的距离；
    根据与所述第一速度和第一距离关联的N维天线数据确定回波强度阈值；
    根据所述回波强度阈值和距离所述目标点较近的至少一个候选点的回波信号，通过计算得到第二距离和第二速度，距离所述目标点较近的每一候选点与所述雷达设备之间的距离为第二距离，所述第二距离与所述第一距离的差值在预设距离范围内；
    将与所述第一速度和第一距离关联的N维天线数据以及与所述第二速度和第二距离关联的N维天线数据作为所述目标点的N维天线数据。
  9. 根据权利要求1至8中的任一项所述的方法，其特征在于，所述根据所述K维天线数据计算所述目标点的第一角度包括：
    根据所述K维天线数据以及超分辨率方法计算所述目标点的第一角度。
  10. 一种雷达设备，其特征在于，所述雷达设备包括：
    收发模块，获取目标点的N维天线数据，N为大于2的正整数；
    处理模块，将所述N维天线数据进行降维，得到K维天线数据，K为小于N的正整数；根据所述K维天线数据计算所述目标点的第一角度，所述第一角度为所述目标点相对于所述雷达设备的角度。
  11. 根据权利要求10所述的雷达设备，其特征在于，所述处理模块具体用于：
    将所述N维天线数据拆分为K个子阵的天线数据；
    将所述K个子阵的天线数据通过波束成型进行降维，得到K维天线数据。
  12. 根据权利要求10或11所述的雷达设备，其特征在于，所述处理模块还用于：
    在所述目标点位于辐射区域中的远场区域的情况下，通过角度快速傅里叶变换FFT对所述N维天线数据进行计算，得到第二角度，所述第二角度为所述目标点相对于所述雷达设备的角度，且所述第二角度的精度低于所述第一角度的精度，所述辐射区域为所述雷达设备通过雷达波形进行辐射所覆盖的区域；
    所述处理模块具体用于：
    确定K个子阵中的每个子阵所对应的波束成型向量；
    将所述K个子阵中的每个子阵的天线数据乘以每个子阵所对应的波束成型向量，得到所述K维天线数据。
  13. 根据权利要求10或11所述的雷达设备，其特征在于，所述处理模块还用于：
    在所述目标点位于辐射区域中的近场区域的情况下，通过波束成型对所述N维天线数据进行计算，得到所述雷达设备中的每根接收天线与所述目标点的角度和距离，所述辐射区域为所述雷达设备通过雷达波形进行辐射所覆盖的区域；
    所述处理模块具体用于：
    根据所述每根接收天线与所述目标点的角度和距离确定每根接收天线的权值；
    根据所述每根接收天线的权值确定K个子阵中的每个子阵所对应的波束成型向量；
    将所述K个子阵中的每个子阵的天线数据乘以每个子阵所对应的波束成型向量，得到所述K维天线数据。
  14. 根据权利要求10所述的雷达设备，其特征在于，所述处理模块具体用于：
    将所述N维天线数据拆分为M个K维天线数据,其中，M＝N-K+1；
    将所述M个K维天线数据通过空间平滑进行降维，得到K维天线数据。
  15. 根据权利要求14所述的雷达设备，其特征在于，所述处理模块具体用于：
    确定所述M个K维天线数据中每个K维天线数据的列向量，以及确定每个K维天线数据的列向量的共轭转秩；
    将所述每个K维天线数据的列向量乘以对应的每个K维天线数据的列向量的共轭转秩，得到所述K维天线数据。
  16. 根据权利要求10至15中的任一项所述的雷达设备，其特征在于，所述收发模块还用于：
    获取所述雷达设备检测到的多个候选点的速度和距离；
    根据所述多个候选点的速度和距离，采用恒虚警率检测算法，确定所述目标点。
  17. 根据权利要求10至16中的任一项所述的雷达设备，其特征在于，所述利用雷达设备获取目标点的N维天线数据，包括：
    利用所述雷达设备获取与第一速度和第一距离关联的N维天线数据，所述第一速度为所述目标点相对于所述雷达设备的速度，所述第一距离为所述目标点与所述雷达设备的距离；
    根据与所述第一速度和所述第一距离关联的N维天线数据确定回波强度阈值；
    根据所述回波强度阈值和距离所述目标点较近的至少一个候选点的回波信号，通过计算得到第二距离和第二速度，距离所述目标点较近的每一候选点与所述雷达设备之间的距离为第二距离，所述第二距离与所述第一距离的差值在预设距离范围内；
    将与所述第一速度和第一距离关联的N维天线数据以及与所述第二速度和第二距离关联的N维天线数据作为所述目标点的N维天线数据。
  18. 根据权利要求10至17中的任一项所述的雷达设备，其特征在于，所述处理模块具体用于：
    根据所述K维天线数据以及超分辨率方法计算所述目标点的第一角度。
  19. 一种包含指令的计算机程序产品，其特征在于，当其在计算机上运行时，使得所述计算机执行如权利要求1至9中任一项所述的方法。
  20. 一种计算机可读存储介质，其特征在于，包括指令，当所述指令在计算机上运行时，使得计算机执行如权利要求1至9中任一项所述的方法。
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