Precise radar low-delay detection method and device, storage medium and electronic device

Abstract

This invention provides a precision radar low-latency detection method, apparatus, storage medium, and electronic device. The method includes: grouping target signals received by the receiving antenna of a target device into multiple target signal groups, wherein the target signals are signals emitted by the transmitting antenna of the target device and reflected by a target object; performing the following operations on each target signal group to obtain the position of the target object corresponding to each target signal group: performing a Fourier transform on each signal in the target signal group in the target dimension to obtain a two-dimensional range Doppler map; performing digital beamforming on the two-dimensional range Doppler map to obtain a three-dimensional range Doppler azimuth map; and determining the position of the target object based on the three-dimensional range Doppler azimuth map. This invention solves the problem of poor real-time performance in determining the position of objects in related technologies, thereby improving the real-time performance of object position determination.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of communications, and more specifically, to a precision radar low-latency detection method, apparatus, storage medium, and electronic device. Background Technology

[0002] Millimeter-wave radar has a wide range of applications, including in the automotive industry to ensure active vehicle safety, and in industrial environments such as perimeter security radar, traffic queuing and flow statistics radar, vehicle speed measurement radar, and holographic intersection sensing radar. These applications place high demands on the millimeter-wave radar's ability to perceive its surroundings. Several key parameters affecting the perception capability of millimeter-wave radar are its ranging range, wide-area measurement capability, azimuth measurement capability, and height measurement capability. Therefore, a radar that can perform wide-area measurement with a large coverage area, while also possessing excellent azimuth and height measurement capabilities, can better perceive the surrounding dynamic and static environment, further improving the safety performance of autonomous driving and the accuracy of queuing / flow events.

[0003] In related technologies, electronic scanning is typically performed using digital or analog beamforming. This allows for a very wide field of view and long-range measurements. Sparse arrays can also be used to achieve large-aperture measurements, improving radar angle measurement resolution. However, this method suffers from long scan times and low frame rates, making it difficult to meet real-time measurement requirements. Furthermore, sparse arrays can increase sidelobe levels, leading to a higher false alarm rate. While electronic scanning using digital or analog beamforming has the drawbacks of long scan times and low frame rates, making it difficult to meet real-time measurement requirements, mechanical scanning suffers from long stability and short lifespan. Additionally, achieving high-resolution Doppler measurements requires long measurement times, making it difficult to meet real-time measurement requirements.

[0004] This indicates that the relevant technologies suffer from poor real-time performance in determining the location of objects.

[0005] There is currently no effective solution to the aforementioned problems in the relevant technologies. Summary of the Invention

[0006] This invention provides a precision radar low-latency detection method, apparatus, storage medium, and electronic device to at least solve the problem of poor real-time performance in determining the location of objects in related technologies.

[0007] According to an embodiment of the present invention, a precision radar low-latency detection method is provided, comprising: grouping target signals received by the receiving antenna of a target device into multiple target signal groups, wherein the target signals are signals emitted by the transmitting antenna of the target device and formed by reflection from a target object; performing the following operations for each target signal group to obtain the position of the target object corresponding to each target signal group: performing a Fourier transform on each signal included in the target signal group in the target dimension to obtain a two-dimensional range Doppler map; performing digital beamforming on the two-dimensional range Doppler map to obtain a three-dimensional range Doppler azimuth map; and determining the position of the target object based on the three-dimensional range Doppler azimuth map.

[0008] According to another embodiment of the present invention, a precision radar low-latency detection device is provided, comprising: a grouping module for grouping target signals received by the receiving antenna of a target device into multiple target signal groups, wherein the target signals are signals emitted by the transmitting antenna of the target device and formed by reflection from a target object; and a determination module for performing the following operations for each target signal group to obtain the position of the target object corresponding to each target signal group: performing a Fourier transform on each signal included in the target signal group in the target dimension to obtain a two-dimensional range Doppler map; performing digital beamforming on the two-dimensional range Doppler map to obtain a three-dimensional range Doppler azimuth map; and determining the position of the target object based on the three-dimensional range Doppler azimuth map.

[0009] According to yet another embodiment of the present invention, a computer-readable storage medium is also provided, wherein a computer program is stored therein, wherein the computer program is configured to perform the steps in any of the above method embodiments when executed.

[0010] According to yet another embodiment of the present invention, an electronic device is also provided, including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the steps in any of the above method embodiments.

[0011] This invention groups the target signals received by the receiving antenna of the target device into multiple target signal groups. For each target signal group, the following operations are performed to obtain the position of the target object corresponding to each group: Fourier transform is performed on each signal in the target dimension to obtain a two-dimensional range Doppler image; digital beamforming is performed on the two-dimensional range Doppler image to obtain a three-dimensional range Doppler azimuth image; and the position of the target object is determined based on the three-dimensional range Doppler azimuth image. Since a two-dimensional range Doppler image can be obtained through Fourier transform of the target dimension, and a three-dimensional range Doppler azimuth image can be obtained through digital beamforming, wide-area long-distance measurement and real-time scanning are achieved. Therefore, the problem of poor real-time performance in determining the position of objects in related technologies can be solved, thus improving the real-time performance of object position determination. Attached Figure Description

[0012] Figure 1 This is a hardware structure block diagram of a mobile terminal for a precision radar low-latency detection method according to an embodiment of the present invention.

[0013] Figure 2 This is a flowchart of a precision radar low-latency detection method according to an embodiment of the present invention;

[0014] Figure 3 This is a schematic diagram of the antenna layout structure of the target device according to an embodiment of the present invention;

[0015] Figure 4 This is a flowchart of a precision radar low-latency detection method according to a specific embodiment of the present invention;

[0016] Figure 5 This is a structural block diagram of a precision radar low-latency detection device according to an embodiment of the present invention. Detailed Implementation

[0017] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings and examples.

[0018] It should be noted that the terms "first," "second," etc., in the specification, claims, and drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0019] The methods and embodiments provided in this application can be executed on a mobile terminal, computer terminal, or similar computing device. Taking running on a mobile terminal as an example, Figure 1 This is a hardware structure block diagram of a mobile terminal for a precision radar low-latency detection method according to an embodiment of the present invention. Figure 1 As shown, a mobile terminal may include one or more ( Figure 1Only one is shown in the diagram. A processor 102 (which may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data are also shown. The mobile terminal may further include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the mobile terminal described above. For example, the mobile terminal may also include components that are more... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0020] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the precision radar low-latency detection method in this embodiment of the invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, thereby implementing the above-described method. The memory 104 may include high-speed random access memory and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0021] The transmission device 106 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the mobile terminal's communication provider. In one example, the transmission device 106 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 106 may be a Radio Frequency (RF) module, used for wireless communication with the Internet.

[0022] This embodiment provides a low-latency detection method for precision radar. Figure 2 This is a flowchart of a precision radar low-latency detection method according to an embodiment of the present invention, such as... Figure 2 As shown, the process includes the following steps:

[0023] Step S202: The target signal received by the receiving antenna of the target device is grouped to obtain multiple groups of target signals, wherein the target signal is a signal transmitted by the transmitting antenna of the target device and formed by reflection from the target object;

[0024] Step S204: Perform the following operations for each group of target signals to obtain the position of the target object corresponding to each group of target signals: perform a Fourier transform on each signal in the target signal group in the target dimension to obtain a two-dimensional range Doppler map; perform digital beamforming on the two-dimensional range Doppler map to obtain a three-dimensional range Doppler azimuth map; determine the position of the target object based on the three-dimensional range Doppler azimuth map.

[0025] In the above embodiments, the target device may include radar equipment, such as vehicle-mounted radar equipment. The target device may include a receiving antenna and a transmitting antenna. The transmitting antenna transmits a signal, which is reflected when passing through a target object. The receiving antenna receives the reflected signal, i.e., the target signal. The target object may be a vehicle, a person, an obstacle, or other similar object. A schematic diagram of the antenna layout structure of the target device can be found in the appendix. Figure 3 .

[0026] In the above embodiments, each receiving antenna can receive multiple target signals, which can be grouped into multiple target signal groups. For each target signal group, a Fourier transform of the target dimension can be performed on each signal in the target signal group to obtain a two-dimensional range Doppler map, such as an R-V Map. The target dimension can be a slow-time dimension. When performing the Fourier transform, factors such as main lobe width, SNR attenuation, and side lobe level can be considered to determine the windowing function and perform windowing processing on the signal.

[0027] For example, after splitting the Doppler sequence of each receiving antenna into 12 groups, a slow-dimensional FFT is performed on the Doppler dimension of each group, resulting in 12 RV Maps. Each receiving antenna has 12 RV Maps. Based on the angle corresponding to the selected beamforming, the RV Map corresponding to that angle is determined for each receiving antenna. Then, the selected RV Maps of each receiving antenna are combined to perform digital beamforming, forming the final RVA 3D cube, i.e., a three-dimensional range-Doppler azimuth map. After obtaining the three-dimensional range-Doppler azimuth map, the azimuth angle of the target object and the elevation angle of the target device can be determined based on the three-dimensional range-Doppler azimuth map, thereby determining the position of the target object.

[0028] The entity performing the above steps can be the target device, a background processor, etc., but is not limited to these.

[0029] This invention groups the target signals received by the receiving antenna of the target device into multiple target signal groups. For each target signal group, the following operations are performed to obtain the position of the target object corresponding to each group: Fourier transform is performed on each signal in the target dimension to obtain a two-dimensional range Doppler image; digital beamforming is performed on the two-dimensional range Doppler image to obtain a three-dimensional range Doppler azimuth image; and the position of the target object is determined based on the three-dimensional range Doppler azimuth image. Since a two-dimensional range Doppler image can be obtained through Fourier transform of the target dimension, and a three-dimensional range Doppler azimuth image can be obtained through digital beamforming, wide-area long-distance measurement and real-time scanning are achieved. Therefore, the problem of poor real-time performance in determining the position of objects in related technologies can be solved, thus improving the real-time performance of object position determination.

[0030] In an exemplary embodiment, grouping the target signal received by the receiving antenna of the target device into multiple target signal groups includes: target sampling of the target signal to obtain a first signal; demodulation of the first signal to obtain a second signal; determining parameter information of the second signal; determining a first windowing function based on the parameter information; windowing the second signal using the first windowing function to obtain a third signal; performing a range-dimensional Fourier transform on the third signal to obtain a fourth signal; and grouping the fourth signal to obtain multiple target signal groups. In this embodiment, when grouping the target signal, target sampling can be performed on the target signal to obtain the first signal. For example, performing analog-to-digital conversion on the target signal yields ADC data. The ADC data is then demodulated to obtain the second signal. During demodulation, a demodulation function can be determined first, and the first signal can be demodulated using the demodulation function to obtain the second signal. A first windowing function is then determined based on the parameter information of the second signal, and the second signal is windowed using the first windowing function. The parameter information may include the main lobe width, SNR loss, side lobe level, etc. If the main lobe width, SNR loss, and side lobe level are considered comprehensively, a suitable window function can be determined. Then, a windowed range-dimensional Fourier transform can be performed on each chirp of each receiving antenna to obtain the fourth signal. The fourth signal can then be grouped to obtain multiple target signal groups.

[0031] In an exemplary embodiment, demodulating the first signal to obtain a second signal includes: determining a random delay index for each chirp included in the first signal, and the number of samples for the target sampling of each chirp; determining the ratio of the random delay index to the number of samples; determining a first product of a first constant, a first imaginary number, a second constant, and a distance dimension index; determining a second product of the ratio and the first product; determining a demodulation function with a base of the natural constant and an exponent of the negative of the second product; and determining the product of the demodulation function and the first signal as the second signal. In this embodiment, the transmitting end performs random delay phase coding, and the receiving end performs signal demodulation. The demodulation formula is exp(-j*2*pi*RangeIdx*k / N), where RangeIdx is the distance dimension index, k is the random delay index of each chirp in the sampling interval, and N is the number of ADC samples per chirp. J is the first imaginary number, the first constant is 2, and the second constant is pi. At this time, the signal emitted by its own radar can be focused in the Doppler dimension, while the signals emitted by other interfering radars will spread out in the Doppler dimension, causing the noise floor to rise.

[0032] In an exemplary embodiment, grouping the fourth signal to obtain multiple groups of the target signal includes: determining an additional phase for each chirp included in the fourth signal, wherein the additional phase is an additional phase added when the transmitting antenna transmits the signal; and grouping each chirp included in the fourth signal based on the additional phase to obtain multiple groups of the target signal. In this embodiment, when performing digital beamforming at the transmitting end, the additional phase for each transmitting antenna is 2*pi*M(n)*lamba / lamba*sin(angle), where lambba represents the wavelength, M can be (0,4,8,12,16,20,24,28,32,36,40,44), n is the transmitting antenna index, which can be determined according to the number of transmitting antennas. For example, when there are 12 transmitting antennas, the transmitting antenna index n is from 1 to 12, and angle is the angle to which the beamforming points. Changing the phase of each chirp to the above form, we get 2*pi*M(n)*(m / N). Therefore, the sine of the angle pointed to by each chirp is m / N. Since the spacing between the transmitting antennas is greater than half the wavelength, when all transmitting antennas radiate electromagnetic waves simultaneously, grating lobes will be formed in space. At this time, when n = 0, 1, 2, 3, 4, 5, 2*pi*K(n)*((m+12*l) / N) = 2*pi*K(n)*((m) / N) + 2*pi*n*l, where m is any integer from 0 to 11, and l is an integer continuously varying from 0 to 63. exp(j*2*pi*n*l) = 1, so when the chirp sequence of each receiving antenna is rearranged into 12 groups according to m+12*l, each group corresponds to a different scanning angle, and the length of each group is 64.For example, given the original sequence (0,1,2,3,4…766,767), arranging it into 12 groups, the first group is (0,12,24,…), corresponding to the sine values ​​of angles (0,1 / 4,1 / 2,3 / 4,1,-3 / 4,-1 / 2,-1 / 4); the second group is (1,13,25,…), corresponding to the sine values ​​of angles (1 / 48,13 / 48,25 / 48,37 / 48,-47 / 48,-35 / 48,-23 / 48,-11 / 48); the third group is (2,14,26,…), corresponding to the sine values ​​of angles… The sine value is calculated using (2+12*l) / N. The fourth group is (3, 15, 27, ...), and the sine value of the corresponding angle is calculated using (3+12*l) / N. The fifth group is (4, 16, 28, ...), and the sine value of the corresponding angle is calculated using (4+12*l) / N. The sixth group is (5, 17, 29, ...), and the sine value of the corresponding angle is calculated using (5+12*l) / N. The seventh group is (6, 18, 30, ...), and the sine value of the corresponding angle is calculated using (6+12*l) / N. The eighth group is (7, 19, 31, ...), and the sine value of the corresponding angle is... The sine value is calculated with reference to (7+12*l) / N. The ninth group is (8, 20, 32, ...), and the sine value of the corresponding angle is calculated with reference to (8+12*l) / N. The tenth group is (9, 21, 33, ...), and the sine value of the corresponding angle is calculated with reference to (9+12*l) / N. The eleventh group is (10, 22, 34, ...), and the sine value of the corresponding angle is calculated with reference to (10+12*l) / N. The twelfth group is (11, 23, 35, ...), and the sine value of the corresponding angle is calculated with reference to (11+12*l) / N. When n = 6, 7, 8, 9, 10... At time 11, 2*pi*(K(n))*((m+12*l) / N)=2*pi*(24+4*(n-6))*((m+12*l) / N)=2*pi*24*m / N+2*pi*24*l / 4+2*pi*4*(n-6)*m / N+2*pi*(n-6)*l, where 2*pi*24*m / N=pi*m represents the additional phase in the pitch direction, 2*pi*24*l / 4 is a quantity that varies linearly with l, and 2*pi*4*(n-6)*m / N represents the angle of the combined azimuth direction. Therefore, the treatment is the same as for n=0,1,2,3,4,5.

[0033] In the above embodiment, the additional phase can be the phase added to each signal by the transmitting antenna when transmitting the signal. The transmitting antenna simultaneously transmits a linear frequency modulated continuous wave (Chirp), with a starting frequency denoted as f, a bandwidth of B, and a Chirp period of T. The values ​​of f, B, and T are set reasonably according to actual conditions. Random delay is used to reduce interference from radars with carrier frequencies close to each other. The additional phase ф for each Chirp is as follows: ф=2*pi*K / N*m, where N can be a constant 32, 48, or other values. The following example uses N=48. m is the Chirp index, with values ​​from 1 to 768 in increments of 1. K is a quantity related to the transmitting antenna index. For example, K corresponding to transmitting antenna indices 1-12 can be (0,4,8,12,16,20,24,28,32,36,40,44).

[0034] In an exemplary embodiment, performing digital beamforming on the two-dimensional range Doppler map to obtain a three-dimensional range Doppler azimuth map includes: determining a target angle for performing the digital beamforming; determining a first weight of the receiving antenna based on the target angle; determining a target two-dimensional range Doppler map corresponding to the target angle included in the two-dimensional range Doppler map; and determining the product of the first weight and the target two-dimensional range Doppler map as the three-dimensional range Doppler azimuth map. In this embodiment, each receiving antenna has 12 RV Maps. Based on the angle corresponding to the selected beamforming, the R-V Map corresponding to that angle is determined in each receiving antenna, and the first weight of the receiving antenna is determined based on the target angle. The product of the first weight and the target two-dimensional range Doppler map is then determined as the three-dimensional range Doppler azimuth map. For example, when performing beamforming on four horizontally distributed receiving antennas r1-r4, the weights are weight = (1, exp(-j*2*pi*2*sin(angle))), exp(-j*2*pi*24*sin(angle))), exp(-j*2*pi*26*sin(angle))), where sin(angle) is the sine value of all angles corresponding to the above 12 groups. Then, based on the selected angle sine value, the RV Map of the four receiving antennas is determined, denoted as S = [RVMap1(i,j), RVMap2(i,j), RVMap3(i,j), RVMap4(i,j)], which respectively correspond to the RV-Map of the four receiving antennas and all point to the element in the i-th row and j-th column of the RV-Map. Multiplying S and weight accordingly yields the beamforming result. After performing the above operation on all elements in the RV-Map, the RV-Angle Map corresponding to the angle sine value is obtained, which has the same size as the RV-Map. Then, the RV-Angle Map obtained from all angle sine values ​​is... The maps are pieced together to form the RV-A3D cube.

[0035] In an exemplary embodiment, determining the position of the target object based on the three-dimensional range-Doppler azimuth map includes: determining the target azimuth angle of the target object and the target pitch angle of the target device based on the three-dimensional range-Doppler azimuth map; and determining the position of the target object based on the target azimuth angle and the target pitch angle. In this embodiment, when determining the position of the target object, the target azimuth angle of the target object and the target pitch angle of the target device can be determined according to the three-dimensional range-Doppler azimuth map, and the position of the target object can be determined based on the target azimuth angle and the target pitch angle.

[0036] In the above embodiments, when determining the antenna layout of the target device, the discrete set of the sine values ​​of the elevation angle can be determined as A, such as {-13 / 27,…,-3 / 27,-2 / 27,-1 / 27,0,1 / 27,1 / 13.5,3 / 27…13 / 27}. As above, digital beamforming weights are generated for each elevation angle. Since the heights of the r5 and r6 receiving antennas are different, and the heights of the r9 and r10 receiving antennas are different, the beamforming weights in the elevation direction can be generated using the heights of the four receiving antennas r1, r5, r9, and r13 as one set, and the heights of r1, r6, r10, and r13 as another set. Then, these sets are combined to solve for the target elevation angle.

[0037] In an exemplary embodiment, determining the target azimuth angle of the target object based on the three-dimensional range-Doppler azimuth map includes: determining the angle corresponding to the peak point in the azimuth power spectrum included in the three-dimensional range-Doppler azimuth map; determining a second weight corresponding to each angle; determining a third product of the second weight and the target value; and determining the angle corresponding to the maximum value included in the third product as the target azimuth angle. In this embodiment, the azimuth direction of the RVA 3D cube obtained by the four receiving antennas r1-r4 is ambiguous. For example, when the target object is located at zero degrees, peak points will be formed at 0 degrees, -30 degrees, and 30 degrees on the azimuth power spectrum, making it impossible to determine the true position of the real target. Furthermore, the virtual equivalent array formed by r1-r4 and t1-t12 is a uniform array, so its sidelobe level is relatively low. The RVA 3D cube formed by the four receiving antennas r13-r16 can be used to further enhance the azimuth angle. The cube performs orientation deblurring, with weights added to r13-r16 as (exp(-j*2*pi*0.25*sin(angle))), exp(-j*2*pi*1.75*sin(angle))), exp(-j*2*pi*24.25*sin(angle))), exp(-j*2*pi*25.75*sin(angle)))). The weights corresponding to the three angles (0, -30, 30) are (1, 1, 1, 1), (exp(j* The values ​​of pi / 4), exp(j*7*pi / 4), exp(j*pi*24.25)), exp(j*pi*25.75))), and (exp(-j*pi / 4)), exp(-j*7*pi / 4)), exp(-j*pi*24.25)), and exp(-j*pi*25.75))) are all different. Therefore, the weights corresponding to different angles are multiplied by the same value to obtain different power values. This allows us to distinguish the three angles and determine the angle with the largest power value as the target azimuth angle, thereby achieving angle deambiguity.

[0038] In an exemplary embodiment, after determining the target azimuth angle of the target object and the target pitch angle of the target device based on the three-dimensional range Doppler azimuth map, the method further includes: determining the target Doppler velocity of the target object; determining a first matched filter based on the target Doppler velocity, the target azimuth angle, and the target pitch angle; determining the target peak value of the velocity dimension included in the two-dimensional range Doppler map; matching the first matched filter with the target peak value to obtain a matching result; determining the amplitude of the matching result; determining the target index corresponding to the largest amplitude included in the amplitude; and determining the velocity of the target object based on the target index. In this embodiment, because the Doppler dimension is resampled, and undersampled, the unambiguous Doppler range is reduced. Compared to the original unambiguous Doppler range, the resampled unambiguous Doppler range is reduced by a factor of 12. Therefore, it is necessary to expand the unambiguous Doppler velocity. The 2D ADC data from each receiving antenna can be processed using a 2D-FFT to directly obtain the RV Map without undersampling. A first matched filter is constructed using this information after obtaining the undersampled Doppler velocity (Doppler), azimuth angle (Azi), and elevation angle (Ele) of the target point. There are 12 sets of matched filters. This is because in the RV Map of each receiving antenna, the real target will generate 12 peaks (P1, P2, P3, P4, P5, P6, P7, P8, P9, P10, P11, P12) along the Doppler dimension at approximately the same distance, corresponding to transmitting antennas t1 to t12. Therefore, there are 12 possible assumptions: P1 can be any one of t1 to t12. Knowing which antenna P1 corresponds to allows us to determine the corresponding antennas for P2 to P12. Knowing the antenna sequence, we can then construct the corresponding first matched filter using the obtained azimuth and elevation angles. Each set of first matched filters is matched and filtered with 12 complex values ​​(P1, P2, P3, P4, P5, P6, P7, P8, P9, P10, P11, P12), and the amplitude of the result is taken. Therefore, a total of 12 amplitudes are obtained. The maximum value among the amplitudes is taken, and the index corresponding to this value is set as Index. The speed of the target object is determined based on the target index.

[0039] In an exemplary embodiment, determining the velocity of the target object based on the target index includes: determining a fourth product of the target index and the Doppler unambiguous period, wherein the Doppler unambiguous period is a predetermined period; and determining the velocity of the target object as the sum of the target Doppler velocity and the fourth product. In this embodiment, the final Doppler value, and the velocity of the target object, can be expressed as UnAmbDoppler = Doppler + Indxe * UnAmbPeriod. UnAmbPeriod is the undersampled Doppler unambiguous period.

[0040] In an exemplary embodiment, after performing a target-dimensional Fourier transform on each signal included in the target signal group to obtain a two-dimensional range Doppler map, the method further includes: if it is determined that the distance between the target object and the target device is less than a predetermined distance, gridding the imaging region included in the two-dimensional range Doppler map to obtain a plurality of target grids; determining a first time delay from each target grid to the transmitting antenna and a second time delay from each target grid to the receiving antenna; determining a second matched filter based on the first time delay and the second time delay; determining the average radial distance from the target grid to the transmitting antenna and the receiving antenna based on the first time delay and the second time delay; compensating for the deviation distance based on the average radial distance to obtain the target distance; determining the response value corresponding to the target distance in the two-dimensional range Doppler map; determining the reflection value of the target object by multiplying the response value and the second matched filter; and determining the target imaging region of the target object based on the reflection value. In this embodiment, within the radar's near-field range, d = 2*D*D / Lamba, where D is the maximum value of the receiving and transmitting apertures in the radar antenna layout, and Lamba is the wavelength. When D = 11 cm, d is approximately 6 m. Therefore, targets within a 6 m range cannot have their azimuth and elevation angles calculated using plane waves; instead, they can only be calculated using spherical waves. Therefore, a backplane imager (BP) is used to directly image the near-field static environment. The 2D-FFT of the two-dimensional ADC data from each receiving antenna can be performed to directly obtain the RV. The map, or two-dimensional range Doppler map, does not perform undersampling; the imaging region is gridded into a cube(x,y,z), and the size of each cube, i.e., the target grid, is determined by the application. The time delay from the cube to the nth transmit antenna (txn,tyn,tzn) is Δt1n = sqrt((x-txn)*(x-txn)+(y-tyn)*(y-tyn)+(z-tzn)*(z-tzn)) / c, where c is the speed of light; the time delay from the cubic lattice to the m-th receiving antenna (rxm, rym, rzm) is Δt2m = sqrt((x-rxm)*(x-rxm) + (y-rym)*(y-rym) + (z-rzm)*(z-rzm)) / c, where c is the speed of light; then, an ideal second matched filter exp(-j2*pi*f*(Δt1n+Δt2m)) is constructed for the transmitting and receiving antennas. The average radial distance r = 0.5*(Δt1n+Δt2m)*c from the lattice to the specified transmitting and receiving antennas is calculated. This distance is then used to compensate for additional offset distances from the feed network, microstrip lines, etc. The value corresponding to this point in the 2D-FFT is determined using this compensated distance and the receiving antenna ID and transmitting antenna ID, denoted as Dnm; the reflection value at this point is P(x,y,z) = ∑ n ∑m Dnm*exp(-j2*pi*f*(Δt1n+Δt2m)).

[0041] In an exemplary embodiment, after determining the target imaging region of the target object based on the reflection value, the method further includes: fusing the target imaging regions corresponding to each group of target signals to obtain a fused region; and updating the target imaging region based on the fused region. In this embodiment, each frame can image the static environment, and the imaged area of ​​each frame has the same part. Therefore, the near-range static environment can be fused. DST (Device-Based Stamping) can be used for fusion, reasonably setting the confidence levels of the three states (occupied, idle, and unknown) in the evidence theory, and then performing DST processing. However, when the radar is located near a moving vehicle, it is necessary to compensate for the displacement caused by the moving vehicle in the preceding and following frames. The displacement difference and heading angle deviation between the preceding and following frames can be calculated using information such as vehicle yaw rate, vehicle speed, lateral acceleration, and sideslip angle. Finally, displacement and heading compensation are performed, followed by DST fusion.

[0042] The low-delay detection method for precision radar is described below with reference to specific implementation methods:

[0043] Figure 4 This is a flowchart of a precision radar low-latency detection method according to a specific embodiment of the present invention, such as... Figure 4 As shown, the process includes:

[0044] Step S402, anti-interference processing: The transmitting end performs random delay phase coding, and the receiving end performs signal demodulation. The demodulation formula is exp(-j*2*pi*RangeIdx*k / N), where RangeIdx is the range dimension index, k is the random delay index of each chirp in the sampling interval, and N is the number of ADC samples for each chirp. At this time, the signal transmitted by the radar itself can be focused in the Doppler dimension, while the signals transmitted by other interfering radars will diverge in the Doppler dimension, causing an increase in the noise floor.

[0045] Step S404: Windowed 1D-FFT (One-Dimensional Fourier Transform) processing. Select a reasonable window function, taking into account the main lobe width, SNR loss, and side lobe level to determine the appropriate window function. Then, perform windowed distance-dimensional Fourier transform on each chirp of each receiving antenna.

[0046] Step S406, Doppler splitting

[0047] When performing digital beamforming at the transmitter, each transmitter antenna is given an additional phase of 2*pi*M(n)*lamba / lamba*sin(angle), where M is (0,4,8,12,16,20,24,28,32,36,40,44), n is the transmitter antenna index from 1 to 12, and angle is the angle to which the beamforming is directed. Changing the phase of each chirp to the above form, we get 2*pi*K(n)*(m / N). Therefore, the sine of the angle pointed to by each chirp is m / N. Since the spacing between the transmitting antennas is greater than half the wavelength, when all transmitting antennas radiate electromagnetic waves simultaneously, grating lobes will be formed in space. At this time, when n=0,1,2,3,4,5, 2*pi*K(n)*((m+12*l) / N)=2*pi*K(n)*((m) / N)+2*pi*n*l, where m is any integer from 0 to 11, and l is an integer continuously varying from 0 to 63. exp(j*2*pi*n*l)=1, so when the chirp sequence of each receiving antenna is rearranged into 12 groups according to m+12*l, each group corresponds to a different scanning angle, and the length of each group is 64.For example, given the original sequence (0,1,2,3,4…766,767), arranging it into 12 groups, the first group is (0,12,24,…), corresponding to the sine values ​​of angles (0,1 / 4,1 / 2,3 / 4,1,-3 / 4,-1 / 2,-1 / 4); the second group is (1,13,25,…), corresponding to the sine values ​​of angles (1 / 48,13 / 48,25 / 48,37 / 48,-47 / 48,-35 / 48,-23 / 48,-11 / 48); the third group is (2,14,26,…), corresponding to the sine values ​​of angles… The sine value is calculated using (2+12*l) / N. The fourth group is (3, 15, 27, ...), and the sine value of the corresponding angle is calculated using (3+12*l) / N. The fifth group is (4, 16, 28, ...), and the sine value of the corresponding angle is calculated using (4+12*l) / N. The sixth group is (5, 17, 29, ...), and the sine value of the corresponding angle is calculated using (5+12*l) / N. The seventh group is (6, 18, 30, ...), and the sine value of the corresponding angle is calculated using (6+12*l) / N. The eighth group is (7, 19, 31, ...), and the sine value of the corresponding angle is... The sine value is calculated with reference to (7+12*l) / N. The ninth group is (8, 20, 32, ...), and the sine value of the corresponding angle is calculated with reference to (8+12*l) / N. The tenth group is (9, 21, 33, ...), and the sine value of the corresponding angle is calculated with reference to (9+12*l) / N. The eleventh group is (10, 22, 34, ...), and the sine value of the corresponding angle is calculated with reference to (10+12*l) / N. The twelfth group is (11, 23, 35, ...), and the sine value of the corresponding angle is calculated with reference to (11+12*l) / N. When n = 6, 7, 8, 9, 10... At time 11, 2*pi*(K(n))*((m+12*l) / N)=2*pi*(24+4*(n-6))*((m+12*l) / N)=2*pi*24*m / N+2*pi*24*l / 4+2*pi*4*(n-6)*m / N+2*pi*(n-6)*l, where 2*pi*24*m / N=pi*m represents the additional phase in the pitch direction, 2*pi*24*l / 4 is a quantity that varies linearly with l, and 2*pi*4*(n-6)*m / N represents the angle of the combined azimuth direction. Therefore, the treatment is the same as for n=0,1,2,3,4,5.

[0048] Step S408, 2D-FFT: After splitting the Doppler sequence of each receiving antenna into 12 groups, perform slow-dimensional FFT on the Doppler dimension of each group, thus forming 12 RV Maps. The selection of the window function for the Doppler dimension should also take into account the main lobe width, SNR attenuation, and side lobe level.

[0049] Step S410, receiving antenna beamforming: each receiving antenna has 12 RV Maps. Based on the angle corresponding to the selected beamforming, determine the RV Map corresponding to that angle in each receiving antenna. Then, perform digital beamforming on the selected RV Maps of each receiving antenna to form the final RVA 3D cube. For example, when performing beamforming on four horizontally distributed receiving antennas r1-r4, the weights are weight = (1, exp(-j*2*pi*2*sin(angle))), exp(-j*2*pi*24*sin(angle))), exp(-j*2*pi*26*sin(angle))), where sin(angle) is the sine value of all angles corresponding to the above 12 groups. Then, based on the selected angle sine value, the RV Map of the four receiving antennas is determined, denoted as S = [RVMap1(i,j), RVMap2(i,j), RVMap3(i,j), RVMap4(i,j)], which respectively correspond to the RV-Map of the four receiving antennas and all point to the element in the i-th row and j-th column of the RV-Map. Multiplying S and weight accordingly yields the beamforming result. After performing the above operation on all elements in the RV-Map, the RV-AngleMap corresponding to the angle sine value is obtained, which has the same size as the RV-Map. Then, the RV-Angle Map obtained from all angle sine values ​​is... The maps are pieced together to form the RV-A3D cube.

[0050] Step S412, azimuth unambiguity resolution: In the RV-A3D cube obtained by the four receiving antennas r1-r4, the azimuth direction is ambiguous. For example, when the target is at zero degrees, the azimuth power spectrum will have peaks at 0 degrees, -30 degrees, and 30 degrees, making it impossible to determine the true location of the target. Furthermore, the virtual equivalent array formed by r1-r4 and t1-t12 is a uniform array, so its sidelobe level is relatively low. The RVA 3D cube formed by the four receiving antennas r13-r16... The cube performs orientation deblurring, with weights added in r13-r16 as follows: (exp(-j*2*pi*0.25*sin(angle))), exp(-j*2*pi*1.75*sin(angle))), exp(-j*2*pi*24.25*sin(angle))), exp(-j*2*pi*25.75*sin(angle)))). The weights corresponding to the three angles (0, -30, 30) are (1, 1, 1, 1) respectively. The values ​​of (exp(j*pi / 4)), (exp(j*7*pi / 4)), (exp(j*pi*24.25)), (exp(j*pi*25.75))), (exp(-j*pi / 4)), (exp(-j*7*pi / 4)), (exp(-j*pi*24.25)), (exp(-j*pi*25.75))) are all different. Therefore, the weights corresponding to different angles are multiplied by the same value to obtain different power values, which can distinguish the three angles and thus achieve angle defuzzification.

[0051] Step S414: Calculate the pitch angle. The discrete set of the sine values ​​of the pitch angle is A: {-13 / 27,…,-3 / 27,-2 / 27,-1 / 27,0,1 / 27,1 / 13.5,3 / 27…13 / 27}. As above, generate digital beamforming weights for each pitch angle. Since the heights of the r5 and r6 receiving antennas are different, and the heights of the r9 and r10 receiving antennas are different, the beamforming weights in the pitch direction are generated using the heights of the four receiving antennas r1, r5, r9, and r13 in one set, and the heights of r1, r6, r10, and r13 in another set. Then, combine these weights to solve for the pitch angle.

[0052] Step S416: Expand the unambiguous Doppler range. Due to the resampling of the Doppler dimension, and specifically undersampling, the unambiguous Doppler range becomes smaller. Compared to the original unambiguous Doppler range, the resampled unambiguous Doppler range is reduced by a factor of 12. Therefore, it is necessary to expand the Doppler unambiguous speed. The specific steps are as follows:

[0053] Perform 2D-FFT on the 2D ADC data of each receiving antenna to directly obtain the RV Map without undersampling;

[0054] To construct matched filters, after obtaining the undersampled Doppler velocity (Doppler), azimuth angle (Azi), and elevation angle (Ele) of the target point, this information is used to construct 12 sets of matched filters. This is because in the RV Map of each receiving antenna, the real target will generate 12 peaks (P1, P2, P3, P4, P5, P6, P7, P8, P9, P10, P11, P12) along the Doppler dimension at approximately the same distance, corresponding to transmitting antennas t1 to t12 respectively. Therefore, there are 12 assumptions that P1 is any one of t1 to t12. Once we know which antenna P1 is, we can obtain the corresponding antennas for P2 to P12. Knowing the antenna sequence, we can construct the corresponding matched filters using the obtained azimuth and elevation angles.

[0055] Each matched filter is applied to 12 complex values ​​(P1, P2, P3, P4, P5, P6, P7, P8, P9, P10, P11, P12), and the amplitude of the result is taken. Therefore, a total of 12 amplitudes are obtained. The maximum amplitude is taken, and its corresponding index is denoted as Index. The final Doppler value is then UnAmbDoppler = Doppler + Indexe * UnAmbPeriod. UnAmbPeriod is the unambiguous Doppler period after undersampling.

[0056] Step S418, Imaging BPA algorithm, in the radar near field range, d = 2*D*D / Lamba, where D is the maximum value of the receiving aperture and transmitting aperture in the radar antenna layout, and Lamba is the wavelength. When D = 11cm, d can be obtained as approximately 6m. Therefore, targets within a 6m range cannot have their azimuth and elevation angles solved using plane waves, but can only be solved using spherical waves. Therefore, BP is used to directly image the near-range static environment.

[0057] The specific steps are as follows:

[0058] Perform 2D-FFT on the 2D ADC data of each receiving antenna to directly obtain the RV Map without undersampling;

[0059] The imaging region is gridded into a cube(x,y,z), the size of which is determined by the application. The time delay from the cube to the nth transmitting antenna (txn,tyn,tzn) is Δt1n = sqrt((x-txn)*(x-txn)+(y-tyn)*(y-tyn)+(z-tzn)*(z-tzn)) / c, where c is the speed of light. The time delay from the cube to the mth receiving antenna (rxm,rym,rzm) is Δt2m = sqrt((x-rxm)*(x-rxm)+(y-rym)*(y-rym)+(z-rzm)*(z-rzm)) / c, where c is the speed of light. Then, an ideal matched filter exp(-j2*pi*f*(Δt1n+Δt2m)) is constructed for the transmitting and receiving antennas.

[0060] Calculate the average radial distance r = 0.5 * (Δt1n + Δt2m) * c from the grid to the specified transmit and receive antennas. Then, compensate for the additional offset distances from the feed network, microstrip lines, etc. using this compensated distance and the receive antenna ID and transmit antenna ID to determine the value corresponding to this point in the 2D-FFT, denoted as Dnm; the reflection value of this point is P(x,y,z) = ∑ n ∑ m Dnm*exp(-j2*pi*f*(Δ·1n+Δt2m)).

[0061] Step S420: Update the static grid state. Each frame can image the static environment, and the imaged areas of each frame have the same parts. Therefore, close-range static environments can be fused. This fusion can be done using DST (Device-Based Stereotype) fusion, with reasonable settings for the confidence levels of the occupied, idle, and unknown states in the evidence theory, followed by DST processing. However, when the radar is located near moving vehicles, it is necessary to compensate for the displacement caused by the moving vehicles in the preceding and following frames. This can be achieved by using information such as vehicle yaw rate, vehicle speed, lateral acceleration, and sideslip angle to calculate the displacement difference between preceding and following frames, as well as the heading angle deviation. Finally, displacement and heading compensation are performed, followed by DST fusion.

[0062] In the aforementioned embodiments, using DDMA (Doppler Frequency Division Multiplexing) technology and the grating lobe principle, the azimuth and elevation angles of the target are obtained through Doppler undersampling and beamforming. This eliminates the need to determine which extreme values ​​in the Doppler domain were generated by which transmitting antennas. The obtained azimuth and elevation angles are used to inversely expand the unambiguous Doppler range, reducing scanning time compared to phased arrays or mechanical scanning. The transmitting end uses digital beamforming technology to point each chirp in a different direction, while the receiving end directly generates a 3D cube map through Doppler undersampling and digital waveform technology, achieving wide-area long-distance measurement and real-time scanning. The target azimuth and elevation angle information is obtained through the 3D cube, and a corresponding matching function is constructed to expand the unambiguous Doppler value of the target.

[0063] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0064] This embodiment also provides a precision radar low-latency detection device for implementing the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0065] Figure 5 This is a structural block diagram of a precision radar low-latency detection device according to an embodiment of the present invention, such as... Figure 5 As shown, the device includes:

[0066] The grouping module 52 is used to group the target signal received by the receiving antenna of the target device into multiple groups of target signals, wherein the target signal is a signal transmitted by the transmitting antenna of the target device and formed by reflection from the target object;

[0067] The determination module 54 is configured to perform the following operations for each group of target signals to obtain the position of the target object corresponding to each group of target signals: performing a Fourier transform on each signal included in the target signal group in the target dimension to obtain a two-dimensional range Doppler map; performing digital beamforming on the two-dimensional range Doppler map to obtain a three-dimensional range Doppler azimuth map; and determining the position of the target object based on the three-dimensional range Doppler azimuth map.

[0068] In an exemplary embodiment, the grouping module 52 can group the target signal received by the receiving antenna of the target device into multiple target signal groups in the following manner: target sampling is performed on the target signal to obtain a first signal; the first signal is demodulated to obtain a second signal; parameter information of the second signal is determined; a first windowing function is determined based on the parameter information; the second signal is windowed using the first windowing function to obtain a third signal; a range-dimensional Fourier transform is performed on the third signal to obtain a fourth signal; and the fourth signal is grouped to obtain multiple target signal groups.

[0069] In an exemplary embodiment, the grouping module 52 can demodulate the first signal to obtain a second signal by: determining a random delay index for each chirp included in the first signal, and the number of samples for the target sampling of each chirp; determining the ratio of the random delay index to the number of samples; determining a first product of a first constant, a first imaginary number, a second constant, and a distance dimension index; determining a second product of the ratio and the first product; determining a demodulation function with a base of the natural constant and an exponent of the negative of the second product; and determining the product of the demodulation function and the first signal as the second signal.

[0070] In an exemplary embodiment, the grouping module 52 may group the fourth signal to obtain multiple groups of the target signal by: determining an additional phase of each chirp included in the fourth signal, wherein the additional phase is an additional phase added when the transmitting antenna transmits the signal; and grouping each chirp included in the fourth signal based on the additional phase to obtain multiple groups of the target signal.

[0071] In an exemplary embodiment, the determining module 54 can perform digital beamforming on the two-dimensional range Doppler map to obtain a three-dimensional range Doppler azimuth map by: determining a target angle for performing the digital beamforming; determining a first weight of the receiving antenna based on the target angle; determining a target two-dimensional range Doppler map included in the two-dimensional range Doppler map corresponding to the target angle; and determining the product of the first weight and the target two-dimensional range Doppler map as the three-dimensional range Doppler azimuth map.

[0072] In an exemplary embodiment, the determining module 54 can determine the position of the target object based on the three-dimensional range Doppler azimuth map by: determining the target azimuth angle of the target object and the target pitch angle of the target device based on the three-dimensional range Doppler azimuth map; and determining the position of the target object based on the target azimuth angle and the target pitch angle.

[0073] In an exemplary embodiment, the determining module 54 can determine the target azimuth angle of the target object based on the three-dimensional range Doppler azimuth map in the following manner: determining the angle corresponding to the peak point included in the azimuth power spectrum included in the three-dimensional range Doppler azimuth map; determining a second weight corresponding to each angle; determining a third product of the second weight and the target value; and determining the angle corresponding to the maximum value included in the third product as the target azimuth angle.

[0074] In an exemplary embodiment, the apparatus may be configured to: determine the target Doppler velocity of the target object after determining the target azimuth angle of the target object and the target pitch angle of the target device based on the three-dimensional range Doppler azimuth map; determine a first matched filter based on the target Doppler velocity, the target azimuth angle, and the target pitch angle; determine the target peak value of the velocity dimension included in the two-dimensional range Doppler map; match the first matched filter with the target peak value to obtain a matching result; determine the amplitude of the matching result; determine the target index corresponding to the largest amplitude included in the amplitude; and determine the velocity of the target object based on the target index.

[0075] In an exemplary embodiment, the apparatus can determine the velocity of the target object based on the target index by: determining a fourth product of the target index and the Doppler unambiguous period, wherein the Doppler unambiguous period is a predetermined period; and determining the sum of the target Doppler velocity and the fourth product as the velocity of the target object.

[0076] In an exemplary embodiment, the apparatus may be configured to, after performing a target-dimensional Fourier transform on each signal included in the target signal group to obtain a two-dimensional range Doppler image, and if it is determined that the distance between the target object and the target device is less than a predetermined distance, grid the imaging region included in the two-dimensional range Doppler image to obtain multiple target grids; determine a first time delay from each target grid to the transmitting antenna and a second time delay from each target grid to the receiving antenna; determine a second matched filter based on the first time delay and the second time delay; determine the average radial distance from the target grid to the transmitting antenna and the receiving antenna based on the first time delay and the second time delay; compensate for the deviation distance based on the average radial distance to obtain the target distance; determine the response value corresponding to the target distance in the two-dimensional range Doppler image; determine the reflection value of the target object by multiplying the response value and the second matched filter; and determine the target imaging region of the target object based on the reflection value.

[0077] In an exemplary embodiment, the apparatus may be used to, after determining the target imaging region of the target object based on the reflectance value, fuse the target imaging regions corresponding to each group of target signals to obtain a fused region; and update the target imaging region based on the fused region.

[0078] It should be noted that the above modules can be implemented by software or hardware. For the latter, they can be implemented in the following ways, but are not limited to: all the above modules are located in the same processor; or, the above modules are located in different processors in any combination.

[0079] Embodiments of the present invention also provide a computer-readable storage medium storing a computer program, wherein the computer program is configured to perform the steps in any of the above method embodiments when executed.

[0080] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard disk, magnetic disk, or optical disk.

[0081] Embodiments of the present invention also provide an electronic device including a memory and a processor, the memory storing a computer program and the processor being configured to run the computer program to perform the steps in any of the above method embodiments.

[0082] In one exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor and the input / output device is connected to the processor.

[0083] Specific examples in this embodiment can be found in the examples described in the above embodiments and exemplary implementations, and will not be repeated here.

[0084] It is obvious to those skilled in the art that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. They can be implemented using computer-executable program code, and thus can be stored in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those described herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0085] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A low-latency detection method for precision radar, characterized in that, include: The target signal received by the receiving antenna of the target device is grouped to obtain multiple groups of target signals, wherein the target signal is a signal transmitted by the transmitting antenna of the target device and formed by reflection from the target object; For each group of target signals, perform the following operations to obtain the position of the target object corresponding to each group of target signals: Perform a Fourier transform on each signal in the target signal group in the target dimension to obtain a two-dimensional range Doppler map; perform digital beamforming on the two-dimensional range Doppler map to obtain a three-dimensional range Doppler azimuth map; determine the position of the target object based on the three-dimensional range Doppler azimuth map; The method of grouping the target signal received by the receiving antenna of the target device to obtain multiple target signal groups includes: sampling the target signal to obtain a first signal; demodulating the first signal to obtain a second signal; determining the parameter information of the second signal; determining a first windowing function based on the parameter information; windowing the second signal using the first windowing function to obtain a third signal; performing a range-dimensional Fourier transform on the third signal to obtain a fourth signal; and grouping the fourth signal to obtain multiple target signal groups. The process of performing digital beamforming on the two-dimensional range Doppler image to obtain a three-dimensional range Doppler azimuth image includes: determining a target angle for performing the digital beamforming; determining a first weight of the receiving antenna based on the target angle; determining a target two-dimensional range Doppler image included in the two-dimensional range Doppler image that corresponds to the target angle; and concatenating the product of the first weight and the target two-dimensional range Doppler image to obtain the three-dimensional range Doppler azimuth image.

2. The method according to claim 1, characterized in that, Demodulating the first signal to obtain the second signal includes: Determine the random delay index of each Chirp included in the first signal, and the number of samples for the target sampling of each Chirp; Determine the ratio of the random delay index to the number of samples; Determine the first constant, the first imaginary number, the second constant, and the first product of the distance dimension index; Determine the second product of the ratio and the first product; Determine the demodulation function with the natural constant as the base and the negative of the second product as the exponent; The product of the demodulation function and the first signal is determined as the second signal.

3. The method according to claim 1, characterized in that, The fourth signal is grouped to obtain multiple groups of the target signal, including: Determine the additional phase of each Chirp included in the fourth signal, wherein the additional phase is the phase added when the transmitting antenna transmits the signal; Based on the additional phase, each Chirp included in the fourth signal is grouped to obtain multiple groups of the target signal.

4. The method according to claim 1, characterized in that, Determining the location of the target object based on the three-dimensional distance Doppler azimuth map includes: The target azimuth angle of the target object and the target pitch angle of the target device are determined based on the three-dimensional range Doppler azimuth map. The position of the target object is determined based on the target azimuth angle and the target elevation angle.

5. The method according to claim 4, characterized in that, Determining the target azimuth angle of the target object based on the three-dimensional range Doppler azimuth map includes: Determine the angles corresponding to the peak points in the azimuth power spectrum included in the three-dimensional range Doppler azimuth map; Determine the second weight corresponding to each angle; Determine the third product of the second weight and the target value; The angle corresponding to the maximum value included in the third product is determined as the target azimuth angle.

6. The method according to claim 4, characterized in that, After determining the target azimuth angle of the target object and the target pitch angle of the target device based on the three-dimensional range Doppler azimuth map, the method further includes: Determine the target Doppler velocity of the target object; The first matched filter is determined based on the target Doppler velocity, the target azimuth angle, and the target elevation angle. Determine the target peak value of the velocity dimension included in the two-dimensional distance Doppler image; The first matched filter is matched with the target peak value to obtain the matching result; Determine the magnitude of the matching result; Determine the target index corresponding to the largest amplitude included in the amplitude range; The speed of the target object is determined based on the target index.

7. The method according to claim 6, characterized in that, Determining the speed of the target object based on the target index includes: Determine the fourth product of the target index and the Doppler unambiguous period, wherein the Doppler unambiguous period is a predetermined period; The sum of the target Doppler velocity and the fourth product is determined as the velocity of the target object.

8. The method according to claim 1, characterized in that, After performing a target-dimensional Fourier transform on each signal included in the target signal group to obtain a two-dimensional distance Doppler map, the method further includes: If the distance between the target object and the target device is determined to be less than a predetermined distance, the imaging area included in the two-dimensional distance Doppler image is gridded to obtain multiple target grids; Determine a first time delay from each of the target grids to the transmitting antenna, and a second time delay from each of the target grids to the receiving antenna; The second matched filter is determined based on the first delay and the second delay; The average radial distance from the target grid to the transmitting antenna and the receiving antenna is determined based on the first delay and the second delay; The target distance is obtained based on the average radial distance compensation deviation distance; Determine the response value corresponding to the target distance in the two-dimensional distance Doppler image; The product of the response value and the second matched filter is determined as the reflection value of the target object; The target imaging area of ​​the target object is determined based on the reflectance value.

9. The method according to claim 8, characterized in that, After determining the target imaging region of the target object based on the reflectance value, the method further includes: The target imaging regions corresponding to each group of target signals are fused to obtain a fused region; The target imaging region is updated based on the fused region.

10. A precision radar low-latency detection device, characterized in that, include: The grouping module is used to group the target signal received by the receiving antenna of the target device into multiple groups of target signals, wherein the target signal is a signal transmitted by the transmitting antenna of the target device and formed by reflection from the target object; The determining module is configured to perform the following operations for each group of target signals to obtain the position of the target object corresponding to each group of target signals: Perform a Fourier transform on each signal in the target signal group in the target dimension to obtain a two-dimensional range Doppler map; perform digital beamforming on the two-dimensional range Doppler map to obtain a three-dimensional range Doppler azimuth map; determine the position of the target object based on the three-dimensional range Doppler azimuth map; The grouping module groups the target signal received by the receiving antenna of the target device into multiple target signal groups in the following manner: the target signal is sampled to obtain a first signal; the first signal is demodulated to obtain a second signal; parameter information of the second signal is determined; a first windowing function is determined based on the parameter information; the second signal is windowed using the first windowing function to obtain a third signal; a range-dimensional Fourier transform is performed on the third signal to obtain a fourth signal; and the fourth signal is grouped to obtain multiple target signal groups. The determining module performs digital beamforming on the two-dimensional range Doppler image to obtain a three-dimensional range Doppler azimuth map in the following manner: determining the target angle for performing the digital beamforming; determining the first weight of the receiving antenna based on the target angle; determining the target two-dimensional range Doppler image included in the two-dimensional range Doppler image corresponding to the target angle; and concatenating the product of the first weight and the target two-dimensional range Doppler image to obtain the three-dimensional range Doppler azimuth map.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein the computer program is configured to perform the method described in any one of claims 1 to 9 when executed.

12. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to run the computer program to perform the method as described in any one of claims 1 to 9.

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