A method, apparatus, and vehicle for data processing

Abstract

A data processing method, device and vehicle, the method can comprise: sending a transmission signal by a detection device, and receiving a echo signal reflected by a target; determining an unambiguous speed measurement interval according to a real-time speed of the detection device and a maximum unambiguous speed; determining a relative speed of the target with respect to the detection device according to the echo signal and the real-time speed of the detection device, so that the relative speed is within the unambiguous speed measurement interval. By acquiring the real-time speed of the detection device, the unambiguous speed measurement interval of the detection device is dynamically updated, the situation of speed ambiguity of the detection device detecting the target is reduced, the hardware complexity of the detection device does not need to be increased, the time-frequency resources of the detection device are saved, and the overall performance of the detection device is improved.

Description

Technical Field

[0001] This application relates to the field of intelligent driving technology, and more specifically, to a data processing method, apparatus, and vehicle. Background Technology

[0002] With societal development, intelligent transportation equipment, smart home devices, robots, and other intelligent terminals are gradually entering people's daily lives. Especially in the field of autonomous driving, sensor technology is crucial, and radar detection technology is a vital component of sensor technology. Frequency-modulated continuous wave (FMCW) radar is a commonly used radar system in automotive radar. To improve angular resolution, automotive radar typically employs a multiple-input multiple-output (MIMO) antenna architecture. To accurately detect the speed of a target relative to the vehicle radar, the maximum unambiguous speed of the radar needs to be increased accordingly; however, this leads to a deterioration in other radar performance indicators. Therefore, how to reduce target speed ambiguity in radar detection without increasing the maximum unambiguous speed of the radar, and without causing deterioration in other performance indicators, has become a pressing problem to be solved in the development of autonomous driving technology. Summary of the Invention

[0003] This application provides a data processing method, apparatus, and vehicle that can reduce the ambiguity in the detection speed of the target by the detection device without causing the deterioration of other indicators of the detection device, thereby improving the overall performance of the detection device. Moreover, it does not introduce additional phase between channels, ensuring the accuracy of the detected target angle, and does not increase the hardware complexity of the detection device.

[0004] In a first aspect, a data processing method is provided, comprising: transmitting a transmission signal via a detection device and receiving an echo signal reflected by a target; and processing the data according to the real-time velocity V of the detection device. c With the maximum unambiguous velocity V of the detection device max Determine the unambiguous speed measurement range; based on the echo signal and real-time speed V c The relative velocity V of the target with respect to the detection device is determined so that the relative velocity V is within the unambiguous velocity measurement range.

[0005] For example, the detection device can be a radar. Specifically, it can be an FMCW radar. More specifically, it can be a time division multiplexing-multiple input multiple output (TDM-MIMO) FMCW radar, a frequency division multiplexing-multiple input multiple output (FDM-MIMO) FMCW radar, or a code division multiplexing-multiple input multiple output (CDM-MIMO) FMCW radar.

[0006] For example, the transmission signal sent by the detection device can be a pulse signal.

[0007] For example, the real-time speed and the maximum unambiguous speed of the detection device can be obtained from external devices or stored locally.

[0008] Based on the above technical solution, the unambiguous velocity measurement range of the detection device can be dynamically updated through the real-time speed of the detection device, reducing the ambiguity of the target velocity detected by the detection device, without expanding the maximum unambiguous speed of the detection device, effectively saving the time and frequency resources of the detection device, without causing the deterioration of other indicators of the detection device, improving the overall performance of the detection device, without introducing additional phase between channels, ensuring the accuracy of the detected target angle, and without increasing the hardware complexity of the detection device.

[0009] In conjunction with the first aspect, in some implementations of the first aspect, the unambiguous velocity measurement range determined based on the real-time velocity of the detection device is:

[0010] [-V max -V c V max -V c ]

[0011] It should be understood that the above technical solutions are applicable to TDM-MIMO FMCW radar, FDM-MIMO FMCW radar, and CDM-MIMO FMCW radar.

[0012] Based on the above technical solution, the length of the unambiguous velocity measurement range of the detection device is 2V. max In existing technologies, the method of directly expanding the maximum unambiguous velocity of the detection device determines an unambiguous velocity measurement interval length greater than 2V. maxThis leads to a deterioration in other performance indicators of the detection device. This solution effectively conserves the time and frequency resources of the detection device, does not cause a deterioration in other performance indicators, improves the overall performance of the detection device, does not introduce additional phase between channels, ensures the accuracy of the detected target angle, and does not increase the hardware complexity of the detection device.

[0013] In conjunction with the first aspect, in some implementations of the first aspect, because the TDM-MIMO FMCW radar uses a round-robin method to transmit multi-channel pulse signals, and the dynamically updated unambiguous velocity measurement range is not symmetrical about the origin 0, the velocity unambiguity de-ambiguation scheme for the TDM-MIMO FMCW radar needs to include the following data preprocessing steps in order for the TDM-MIMO FMCW radar to accurately calculate the relative velocity of the target:

[0014] The first data is determined, which is the data obtained after the echo signal received by the detection device is subjected to a distance-dimensional fast Fourier transform; based on the real-time speed V of the detection device... c and maximum unambiguous speed V max Determine the first window function T(ε); based on the first data and the first window function T(ε), determine the relative velocity V.

[0015] For example, the virtual antenna channel synthesized by the MIMO radar transceiver antenna can be more than one; therefore, the channel for receiving echo signals can also be more than one. Thus, the aforementioned first data can be multidimensional data obtained by performing a range-dimensional fast Fourier transform on the echo signal received by the detection device.

[0016] Based on the above technical solution, the signal spectrum of the echo signal velocity dimension can be made symmetrical with respect to the real-time vehicle speed, so that the subsequent speed calculation process is not affected by the dynamic adjustment of the unambiguous speed measurement interval, and the calculated speed is within the unambiguous speed measurement interval.

[0017] In conjunction with the first aspect, in some implementations of the first aspect, the first window function can be determined according to the following formulas (1)-(4):

[0018]

[0019] ε=1-V c / V max (2)

[0020]

[0021] β(ε)=[1,exp(jπε),...,exp(jπε(L-1))] (4)

[0022] Where ε represents the adjustment factor, α(ε) represents the first motion compensation factor, β(ε) represents the second motion compensation factor, and N t The number of transmitting antennas in the detection device is represented by L, and the number of pulse signals transmitted by the transmitting antennas within a preset time period is represented by T. w This represents the second window function between pulse signals. N represents r A 1-dimensional vector of all ones, where ⊙ represents the dot product. It represents the Kronecker product.

[0023] It should be understood that the second window function T w It is the window function added between chirps during the fast Fourier transform of the velocity dimension when the unambiguous velocity measurement interval is symmetrical about the origin 0. There are many forms of the second window function, among which the Hamming window is a commonly used one, specifically represented by the following formula (5):

[0024]

[0025] Where N represents the length of the Hamming window.

[0026] Another commonly used second window function is the Hanning window, which is specifically represented by the following formula (6):

[0027]

[0028] Where N represents the length of the Hanning window.

[0029] In conjunction with the first aspect, some implementations of the first aspect also include the following velocity deambiguation process for TDM-MIMO FMCW radar:

[0030] The second data is determined, which is the first data after adding a first window function and then performing a fast Fourier transform in the velocity dimension. Based on this second data, the Doppler channel index N of the velocity element length δV and the relative velocity V of the detection device is determined. doppler Based on the adjustment factor ε and the maximum unambiguous velocity V max Determine the compensation factor ΔV; based on the velocity element length δV and the Doppler channel index N. doppler And the compensation factor ΔV, determine the relative velocity V.

[0031] For example, the first data mentioned above can be multidimensional data obtained by performing a distance-dimensional fast Fourier transform on the echo signal received by the detection device. Therefore, the second data mentioned above can be multidimensional data obtained by performing a velocity-dimensional fast Fourier transform on the first data after adding a first window function T(ε).

[0032] Based on the above technical solution, by adding a compensation factor, when the TDM-MIMO FMCW radar adopts the method of dynamically updating the unambiguous velocity measurement range proposed in this application, it can accurately calculate the relative velocity of the target based on the second data determined after the above data preprocessing and the compensation factor, thereby reducing the occurrence of velocity ambiguity in target detection by the radar. Since the angle-dimensional Fourier transform of the FMCW radar is processed based on the result data after the velocity-dimensional Fourier transform, when there is no velocity ambiguity in the measured target, it can effectively improve the accuracy of the angle-dimensional Fourier transform data processing results in 3D-FFT processing.

[0033] In conjunction with the first aspect, in some implementations of the first aspect, the compensation factor ΔV is determined according to the following formula (7):

[0034] ΔV=-εV max (7)

[0035] In conjunction with the first aspect, in some implementations of the first aspect, the relative velocity V is determined according to the following formula (8):

[0036] V=N doppler δV+ΔV (8)

[0037] Secondly, a data processing apparatus is provided, comprising:

[0038] The control unit is used to control the detection device to transmit signals and to control the detection device to receive echo signals reflected by the target;

[0039] The determining unit is used to determine the real-time velocity V of the detection device. c With the maximum unambiguous velocity V of the detection device max It is used to determine unambiguous speed measurement intervals; it is also used to determine speed measurement intervals based on echo signals and real-time speed V. c Determine the relative velocity V of the target relative to the detection device, so that the relative velocity V is within the unambiguous velocity measurement range.

[0040] For example, the detection device can be a radar. Specifically, it can be an FMCW radar. More specifically, it can be a TDM-MIMO type FMCW radar, an FDM-MIMO type FMCW radar, or a CDM-MIMO type FMCW radar.

[0041] For example, the transmission signal sent by the detection device can be a pulse signal.

[0042] For example, the real-time speed and the maximum unambiguous speed of the detection device can be obtained from external devices or stored locally.

[0043] In conjunction with the second aspect, in some implementations of the second aspect, the unambiguous velocity measurement range determined based on the real-time velocity of the detection device is as follows:

[0044] [-V max -V c V max -V c ]

[0045] In conjunction with the second aspect, in some implementations of the second aspect, since the TDM-MIMO FMCW radar uses a round-robin method to transmit multi-channel pulse signals, and the dynamically updated unambiguous velocity measurement range is not symmetrical about the origin 0, the velocity deambiguation scheme for the TDM-MIMO FMCW radar includes a data preprocessing process. In this process, the determining unit is specifically used for:

[0046] The first data is determined, which is the data obtained after the echo signal undergoes a distance-dimensional fast Fourier transform; based on the real-time velocity V c and maximum unambiguous speed V max Determine the first window function T(ε); based on the first data and the first window function T(ε), determine the relative velocity V.

[0047] For example, the virtual antenna channel synthesized by the MIMO radar transceiver antenna can be more than one; therefore, the channel for receiving echo signals can also be more than one. Thus, the aforementioned first data can be multidimensional data obtained by performing a range-dimensional fast Fourier transform on the echo signal received by the detection device.

[0048] In conjunction with the second aspect, in some implementations of the second aspect, the determining unit is also used to determine the first window function T(ε) according to the following formulas (1)-(4):

[0049]

[0050] ε=1-V c / V max (2)

[0051]

[0052] β(ε)=[1,exp(jπε),...,exp(jπε(L-1))] (4)

[0053] Where ε represents the adjustment factor, α(ε) represents the first motion compensation factor, β(ε) represents the second motion compensation factor, and N t The number of transmitting antennas in the detection device is represented by L, and the number of pulse signals transmitted by the transmitting antennas within a preset time period is represented by T. w This represents the second window function between pulse signals. N represents r A 1-dimensional vector of all ones, where ⊙ represents the dot product. It represents the Kronecker product.

[0054] It should be understood that the second window function T w It is the window function added between chirps during the fast Fourier transform of the velocity dimension when the unambiguous velocity measurement interval is symmetrical about the origin 0. There are many forms of the second window function, among which the Hamming window is a commonly used one, specifically represented by the following formula (5):

[0055]

[0056] Where N represents the length of the Hamming window.

[0057] Another commonly used second window function is the Hanning window, which is specifically represented by the following formula (6):

[0058]

[0059] Where N represents the length of the Hanning window.

[0060] In conjunction with the second aspect, in some implementations of the second aspect, the determining unit is also used for:

[0061] The second data is determined, which is the first data after adding the first window function T(ε) and then performing a fast Fourier transform in the velocity dimension. Based on the second data, the Doppler channel index N of the velocity element length δV and the relative velocity V of the detection device is determined. doppler Based on the adjustment factor ε and the maximum unambiguous velocity V max Determine the compensation factor ΔV; based on the velocity element length δV and the Doppler channel index N. doppler And the compensation factor ΔV, determine the relative velocity V.

[0062] For example, the first data mentioned above can be multidimensional data obtained by performing a distance-dimensional fast Fourier transform on the echo signal received by the detection device. Therefore, the second data mentioned above can be multidimensional data obtained by performing a velocity-dimensional fast Fourier transform on the first data after adding a first window function T(ε).

[0063] In conjunction with the second aspect, in some implementations of the second aspect, the determining unit is also used to determine the compensation factor ΔV according to the following formula (7):

[0064] ΔV=-εV max (7)

[0065] In conjunction with the second aspect, in some implementations of the second aspect, the determining unit is also used to determine the relative velocity V according to the following formula (8):

[0066] V=N doppler δV+ΔV (8)

[0067] Thirdly, a data processing apparatus is provided, including a memory for storing computer instructions; and a processor for executing the computer instructions stored in the memory, such that the apparatus performs any possible implementation of the method design of the first aspect described above.

[0068] Fourthly, a means of transport is provided to perform the method of the first aspect; or, it includes a data processing apparatus of the second aspect.

[0069] In this application, a means of transport may include one or more different types of transport vehicles or movable objects that operate or move on land (e.g., highways, roads, railways, etc.), water (e.g., waterways, rivers, oceans, etc.), or space. For example, a means of transport may include automobiles, bicycles, motorcycles, trains, subways, airplanes, ships, aircraft, robots, or other types of transport vehicles or movable objects.

[0070] Fifthly, a computer storage medium is provided, characterized in that the computer storage medium stores computer instructions, which, when executed on a computer, cause the computer to perform any possible implementation of the method design of the first aspect above.

[0071] In a sixth aspect, a chip is provided, including a processor for performing the methods in any possible implementation of the method design of the first aspect described above.

[0072] For example, the chip could be a baseband chip.

[0073] In a seventh aspect, a computer program product is provided, wherein when the computer program code or instructions are executed on a computer, the computer performs the method in any possible implementation of the method design of the first aspect described above. Attached Figure Description

[0074] Figure 1 This is a functional block diagram of the vehicle 100 provided in the embodiments of this application.

[0075] Figure 2 This is a schematic diagram of the pulse signal transmission of the FMCW radar and the CDM-MIMO FMCW radar provided in the embodiments of this application.

[0076] Figure 3 This is a schematic diagram of the pulse signal transmission of the FMCW radar and the TDM-MIMO FMCW radar provided in the embodiments of this application.

[0077] Figure 4 This is a schematic diagram illustrating three methods for improving the maximum unambiguous velocity of the FMCW radar provided in the embodiments of this application.

[0078] Figure 5 This is a schematic diagram of 3D-FFT signal processing provided in an embodiment of this application.

[0079] Figure 6 This is a flowchart of conventional 3D-FFT signal processing for FMCW radar provided in an embodiment of this application.

[0080] Figure 7 This is a schematic diagram of the method for dynamically updating unambiguous speed measurement intervals provided in the embodiments of this application.

[0081] Figure 8 This is a flowchart of a method for dynamically updating unambiguous speed measurement intervals provided in an embodiment of this application.

[0082] Figure 9 This is a flowchart of the improved 3D-FFT velocity dimension data preprocessing method provided in the embodiments of this application.

[0083] Figure 10 This is a flowchart of the improved 3D-FFT speed calculation method provided in the embodiments of this application.

[0084] Figure 11 This is a schematic block diagram of a data processing apparatus 1100 provided in an embodiment of this application. Detailed Implementation

[0085] The technical solutions in this application will now be described with reference to the accompanying drawings.

[0086] Figure 1 This is a functional block diagram of a vehicle 100 provided in an embodiment of this application. The vehicle 100 may include a sensing system 120, a display device 130, and a computing platform 150. The sensing system 120 may include several sensors for sensing information about the surrounding environment of the vehicle 100. For example, the sensing system 120 may include a positioning system, which may be a Global Positioning System (GPS), a BeiDou system, or other positioning systems, an inertial measurement unit (IMU), lidar, millimeter-wave radar, ultrasonic radar, and one or more of the following: radar may also be an FMCW radar.

[0087] Some or all of the functions of vehicle 100 can be controlled by computing platform 150. Computing platform 150 may include processors 151 to 15n (n being a positive integer). A processor is a circuit with signal processing capabilities. In one implementation, the processor can be a circuit with instruction read and execute capabilities, such as a central processing unit (CPU), microprocessor, graphics processing unit (GPU) (which can be understood as a type of microprocessor), or digital signal processor (DSP). In another implementation, the processor can implement certain functions through the logical relationships of hardware circuits. These logical relationships are fixed or reconfigurable. For example, the processor may be a hardware circuit implemented using an application-specific integrated circuit (ASIC) or a programmable logic device (PLD), such as an FPGA. In reconfigurable hardware circuits, the process of the processor loading a configuration document and configuring the hardware circuit can be understood as the process of the processor loading instructions to implement some or all of the functions of the aforementioned units. In addition, it can also be hardware circuitry designed for artificial intelligence, which can be understood as an ASIC, such as a neural network processing unit (NPU), tensor processing unit (TPU), deep learning processing unit (DPU), etc. Furthermore, the computing platform 150 may also include a memory for storing instructions. Some or all of the processors 151 to 15n can call the instructions in the memory to execute them and achieve the corresponding functions.

[0088] The technical solutions of this application embodiment can be applied to the vehicle 100 described above, and can also be applied to other means of transportation. For example, a means of transportation may include one or more different types of transport vehicles or movable objects that operate or move on land (e.g., highways, roads, railways, etc.), water surface (e.g., waterways, rivers, oceans, etc.), or space. For example, a means of transportation may include automobiles, bicycles, motorcycles, trains, subways, airplanes, ships, aircraft, robots, or other types of transport vehicles or movable objects.

[0089] Compared to other ranging and speed measuring radars, FMCW radar has a simpler structure. FMCW radar technology is relatively mature, requiring lower peak transmit power, is easy to modulate, has low cost, and simple signal processing, making it a commonly used radar system in automotive radar. It should be understood that conventional FMCW radar uses a single-input single-output (SISO) signal transmission and reception mechanism. However, in autonomous driving scenarios, since conventional FMCW radars generally operate at low to medium pulse repetition frequencies, and the factors determining the maximum unambiguous speed of an FMCW radar are its pulse repetition frequency and the wavelength of the pulse repetition signal, when the pulse repetition signal wavelength remains constant, the lower the radar's pulse repetition frequency, the smaller its maximum unambiguous speed. Therefore, when the detected target speed is too high, exceeding the maximum unambiguous speed of a conventional FMCW radar, speed ambiguity will occur, meaning the FMCW radar will detect the wrong speed. Furthermore, in this scenario, distance and speed alone are insufficient for the target detection requirements of autonomous driving; angle parameters also need to be introduced, making the angular resolution of the FMCW radar also crucial. Therefore, in order to improve angular resolution, automotive radar typically adopts an FMCW system with a MIMO antenna architecture. MIMO radar mainly includes FDM-MIMO standard FMCW radar, CDM-MIMO standard FMCW radar, and TDM-MIMO standard FMCW radar.

[0090] The aforementioned MIMO radars all share the same velocity measurement principle: they estimate the target's slow-time frequency, or Doppler shift, by performing a Fourier transform on the slow-time sampled signal of the target echo. Since the Doppler shift is mathematically equal to the ratio between the target velocity and twice the target echo wavelength, the target velocity can be easily calculated based on the Doppler shift.

[0091] The key to MIMO radar is how to distinguish the detection signals from different transmitting antennas from the target echo data. FDM-MIMO and CDM-MIMO radars transmit detection signals simultaneously from their respective transmitting antennas. FDM-MIMO distinguishes signals by having the detection signals from each transmitting antenna operate in different frequency bands, CDM-MIMO radar distinguishes signals by modulating the detection signals from each transmitting antenna with different phase codes, and TDM-MIMO radar distinguishes signals by having the detection signals from each transmitting antenna operate in different time periods.

[0092] FDM-MIMO FMCW radar suffers from wasted system RF bandwidth, degrading its range resolution. Furthermore, it also suffers from the aforementioned problem of other performance indicators deteriorating in an effort to increase the maximum unambiguous velocity. CDM-MIMO FMCW radar, on the other hand, requires the introduction of phase shifters in different transmit links to achieve code division multiplexing by adding different phase differences to the pulse signals between each antenna channel. Figure 2 This is a schematic diagram of the pulse signal transmission of FMCW radar and CDM-MIMO FMCW radar. It can be seen that the process of adding different phase differences to each antenna channel of CDM-MIMO FMCW radar, in practical applications, introduces phase errors, leading to inaccurate detection results. Furthermore, it also suffers from the problem mentioned above where FMCW radar, in its pursuit of increasing its maximum unambiguous velocity, causes other performance indicators to deteriorate.

[0093] Considering factors such as hardware cost, TDM-MIMO-based FMCW radar is primarily used for target detection in current autonomous driving scenarios. Conventional FMCW radar employs a SISO signal transmission and reception mechanism, where one pulse repetition cycle consists of a single pulse signal cycle. However, a TDM-MIMO-based FMCW radar's pulse repetition cycle is composed of the combined pulse signal cycles transmitted by multiple antennas. Figure 3 This is a schematic diagram illustrating the transmission of pulse signals by FMCW radar and TDM-MIMO FMCW radar. The TDM-MIMO FMCW radar transmits pulse signals through two antenna channels using time-division multiplexing. For example, compared to conventional FMCW radar, as... Figure 3 The pulse repetition period of the TDM-MIMO FMCW radar shown is twice that of a conventional FMCW radar. Furthermore, a larger pulse repetition period results in a lower pulse repetition frequency. Therefore, the TDM-MIMO system further reduces the pulse repetition frequency of the FMCW radar. Moreover, the factors determining the maximum unambiguous velocity of an FMCW radar are its pulse repetition frequency and the wavelength of the repetitive signal. When the wavelength of the repetitive signal remains constant, a lower pulse repetition frequency results in a lower maximum unambiguous velocity. Therefore, the TDM-MIMO system further reduces the maximum unambiguous velocity of the FMCW radar.

[0094] Therefore, although TDM-MIMO FMCW radar can improve the angular resolution of targets, the problem of target velocity ambiguity will become more prominent. If the maximum unambiguous velocity of TDM-MIMO FMCW radar is to be increased, other radar indicators will also deteriorate.

[0095] In one embodiment, when the aforementioned FMCW radars of various types are stationary, they can generally accurately detect the speed of the normally moving vehicles. However, in autonomous driving scenarios, these FMCW radars typically detect targets while in motion. Therefore, the relative speed of the target relative to the FMCW radar can easily exceed the maximum unambiguous speed, leading to ambiguity in the target's speed. This necessitates a wider unambiguous speed measurement range for the FMCW radar and a higher maximum unambiguous speed requirement. For example, if the maximum unambiguous speed of the FMCW radar is 120 km / h, and the vehicle equipped with the FMCW radar and the target vehicle are traveling in opposite directions at 120 km / h, the relative speed of the target vehicle could be 240 km / h, exceeding the maximum unambiguous speed of the FMCW radar. Without speed deambiguation, the relative speed of the target detected by the FMCW radar will be ambiguous.

[0096] It should be understood that the maximum unambiguous speed of the FMCW radar and the target speed detected by the FMCW radar are parameters proposed in the radar coordinate system, namely relative speed, while the vehicle speed is a parameter based on the geodetic coordinate system, namely absolute speed.

[0097] To address the velocity ambiguity issue in FMCW radar, the most direct approach is to maximize its maximum unambiguous velocity. This leads to the proposal of an improved FMCW radar. This improved radar can enhance its maximum unambiguous velocity through three methods, thereby resolving the velocity ambiguity problem that easily occurs when there is relative motion between the FMCW radar and the target. However, depending on the method employed, the FMCW radar's performance metrics (such as range resolution and detection range) will deteriorate to varying degrees.

[0098] Figure 4 This is a schematic diagram of three ways to improve the maximum unambiguous velocity of FMCW radar.

[0099] Method 1: Maintain the maximum detection range of the FMCW radar unchanged, but reduce its radio frequency bandwidth, resulting in a decrease in range resolution. The worse the range resolution, the more difficult it is for the FMCW radar to distinguish between two or more closely spaced targets. For example, a decrease in range resolution can cause the FMCW radar to fail to distinguish between people and vehicles, thus preventing accurate target identification.

[0100] Method 2: Maintain the range resolution of the FMCW radar unchanged, but increase its frequency modulation slope. However, if the maximum detection range remains constant, the intermediate frequency (IF) sampling bandwidth of the analog-to-digital converter (ADC) will increase, placing high demands on the radar hardware and increasing its cost. Conversely, if the ADC IF sampling bandwidth remains constant, the maximum detection range will decrease, shortening the maximum target detection range and affecting the radar's early warning time for distant targets.

[0101] Method 3: Increasing the maximum unambiguous speed of FMCW radar by reducing the waveform duty cycle will lead to a decrease in the target point cloud data rate and a reduction in the number of data refresh frames in the same time period, which will also result in inaccurate target recognition and trajectory correlation.

[0102] In view of this, the data processing method provided in this application dynamically updates the unambiguous speed measurement range of the FMCW radar based on the current speed of the vehicle, reducing the speed ambiguity of the target detected by the FMCW radar, without expanding the maximum unambiguous speed of the FMCW radar, effectively saving the time and frequency resources of the FMCW radar, without causing the deterioration of other indicators of the FMCW radar, improving the overall performance of the FMCW radar, without introducing additional phase between channels, ensuring the accuracy of the detected target angle, and without increasing the hardware complexity of the FMCW radar.

[0103] Since TDM-MIMO FMCW radar uses a round-robin method to transmit multi-channel pulse signals, and when the above-mentioned method of dynamically updating the unambiguous velocity measurement range of FMCW radar is also used for TDM-MIMO FMCW radar, the unambiguous velocity measurement range of the radar is not necessarily symmetrical about the origin (0), but rather about the radar's moving speed. TDM-MIMO FMCW radar signals need to be processed by the three-dimensional fast fourier transform (3D-FFT) method. The conventional 3D-FFT processes signal data in the velocity dimension in a range symmetrical about the origin (0). In this embodiment, when TDM-MIMO FMCW radar performs signal processing in the velocity dimension of 3D-FFT, the window function between linear frequency modulated signals (chirp) can be modified according to the real-time speed, so that 3D-FFT can process the signal data based on the above-updated unambiguous velocity measurement range, and finally complete the calculation of the measured target speed.

[0104] It should be understood that the chirp signal mentioned above in this embodiment of the application can be understood as the pulse repetition signal mentioned above.

[0105] The data processing method provided in this application embodiment can include the above-described velocity deambiguation process. It can generate adjustment factors and compensation factors based on the radar's real-time velocity, and modify the window function between chirps in the 3D-FFT velocity dimension signal processing based on the adjustment factors. The velocity calculation result is corrected based on the compensation factors to obtain the velocity of the measured target. This data processing method is also applicable to FMCW radars implementing the TDM-MIMO standard, and can be used to update the unambiguous velocity measurement range based on the radar's real-time velocity. This reduces velocity ambiguity in FMCW radar detection of targets, and the method does not expand the maximum unambiguous velocity of the FMCW radar, saving radar time and frequency resources and improving the overall radar performance.

[0106] The above solution can be implemented based solely on FMCW radar without increasing hardware complexity. By optimizing the radar detection method, the radar can adjust the unambiguous velocity measurement range in real time, thus solving the problem of ambiguity in target velocity detection by FMCW radar.

[0107] To facilitate understanding of the embodiments of this application, the terms and concepts involved in the embodiments of this application will be explained first.

[0108] Maximum unambiguous velocity: When the maximum pulse phase shift from one pulse to the next that the FMCW radar can measure is 180°, the radial velocity of the target object corresponding to the 180° pulse phase shift is the maximum unambiguous velocity.

[0109] Doppler shift: When there is relative motion between the radar and the target, the signal waveform will be compressed or broadened. The radar's reflected echo will produce a frequency shift, which is proportional to the relative radial velocity between the scatterer and the radar. This is the Doppler shift.

[0110] Linear frequency modulation (LFM) signal: A linear frequency modulation (LFM) signal is a signal whose frequency changes linearly during its duration. It is a commonly used radar signal.

[0111] 3D-FFT: A fundamental method for ranging, velocity measurement, and angle measurement applied to millimeter-wave radar. Figure 5 This is a schematic diagram of 3D-FFT signal processing provided in an embodiment of this application. 3D-FFT processes the signal from the distance dimension, velocity dimension, and angle dimension respectively.

[0112] The distance dimension is the first dimension, and in this dimension, FFT processing is performed on the fast-time sampled signals within the chirp of all channels. Taking MIMO radar as an example, the aforementioned channels refer to the virtual antenna channels synthesized by the MIMO radar's transmit and receive antennas.

[0113] The velocity dimension is the second dimension, in which FFT processing is performed on the slow-time sampled signals between chirps of all virtual antenna channels.

[0114] The angular dimension is the third dimension, under which FFT processing is performed on the sampled signals between all virtual antenna channels.

[0115] Furthermore, the Fast Fourier Transform (FFT) can only transform time-domain data of finite length. Therefore, the time-domain signal needs to be truncated before performing the FFT. However, if the time-domain signal is not periodically truncated, abrupt changes will occur at both ends of the truncated time-domain waveform, leading to spectral leakage. Therefore, adding a window function can smooth out these abrupt changes at both ends of the truncated time-domain waveform, reducing the sidelobes of the spectral window and thus minimizing spectral leakage.

[0116] Figure 6 This is a flowchart of the conventional 3D-FFT signal processing for FMCW radar provided in the embodiments of this application. The specific 3D-FFT processing flow is as follows:

[0117] S610 performs ADC sampling processing on the chirp signal to obtain the processed digital sampled signal, and adds a first window function to the chirp signal;

[0118] It should be understood that in the FMCW radar signal processing scenario, the chirp signal is originally a continuous analog signal. The FMCW radar needs to sample this signal through an ADC to obtain a set of discrete digital sampled signals, which will cause spectral leakage. Therefore, it is necessary to add a window function to the chirp signal to reduce the side lobes of the spectral window and reduce spectral leakage. There are many forms of window functions, among which the Hamming window is commonly used, and it is specifically represented by the following formula (1):

[0119]

[0120] Where N represents the length of the Hamming window.

[0121] Another commonly used window function is the Hanning window, which is specifically represented by the following formula (2):

[0122]

[0123] Where N represents the length of the Hanning window.

[0124] In addition, there are other forms of window functions, which are not limited to in this application.

[0125] S620 performs a Fast Fourier Transform on the signal after adding a window function within the chirp to obtain the Fast Fourier Transform result signal in the distance dimension.

[0126] S630, obtain the fast Fourier transform result signal of the above distance dimension, and add a window function between chirps;

[0127] Similarly, the Fast Fourier Transform (FFT) results in the distance dimension also exhibit spectral leakage. Therefore, it is necessary to add window functions between chirps to reduce the sidelobes of the spectral window and minimize spectral leakage. Commonly used window functions include the Hamming window and the Hanning window.

[0128] S640 performs a fast Fourier transform on the signal after adding a window function between chirps to obtain the fast Fourier transform result signal in the velocity dimension.

[0129] S650, obtain the fast Fourier transform result signal of the above velocity dimension, and add a window function between the antenna channels;

[0130] Similarly, the Fast Fourier Transform (FFT) signal in the velocity dimension also exhibits spectral leakage. Therefore, a window function needs to be added between chirps to reduce the sidelobes of the spectral window and minimize spectral leakage. Commonly used window functions include the Hamming window and the Hanning window.

[0131] In one embodiment, for a TDM-MIMO radar, S650 also includes target motion error compensation operations.

[0132] S660 performs Fast Fourier Transform on the signal after adding window functions between antenna channels to obtain the Fast Fourier Transform result signal of any channel in the angular dimension.

[0133] It should be understood that in the above-described 3D-FFT process for the signal, the forms of the window functions added during signal processing in each dimension do not affect each other. For example, the window function added within a chirp is a Hanning window, while the window function added between chirps can be a Hanning window, a Hamming window, or other forms of window function. Similarly, the window function added between antenna channels can be a Hanning window, a Hamming window, or other forms of window function. This embodiment does not limit this.

[0134] It should be understood that the radar described above detects target objects through three-dimensional fast Fourier transform. The detection results are used for further analysis, such as target recognition, high-precision map construction, and path planning.

[0135] It should be understood that the above 3D-FFT is applicable to scenarios where TDM-MIMO FMCW radar detects targets.

[0136] The velocity element length is determined by the radar's ability to estimate the target's slow-time frequency, or Doppler frequency, by performing a Fourier transform on the slow-time sampled signal of the target echo. Since the Doppler frequency is mathematically equal to the ratio of the target velocity to twice the target echo wavelength, the target velocity can be calculated based on the Doppler frequency. A velocity element refers to the velocity interval corresponding to the interval between adjacent frequency points during the radar's slow-time Fourier transform; its size is defined as the velocity element length.

[0137] The Doppler channel index refers to the frequency index of the slow-time Fourier transform output.

[0138] Figure 7 This is a schematic diagram of a method for dynamically updating unambiguous speed measurement ranges provided in an embodiment of this application. Vehicle 701 is equipped with a detection device.

[0139] It should be understood that, in its operational state, the detection device transmits pulse signals to the surrounding environment at a specific pulse repetition frequency. When the pulse signal encounters a target object, it is reflected. A portion of the reflected signal is received by the detection device, and this process repeats. This reflected signal, received by the detection device, is typically referred to as the echo signal. It should be understood that the pulse signal is an electromagnetic wave signal, and its propagation speed is much greater than the speeds of vehicle 701, target 702, and target 703. Therefore, within a single chirp, the process of the detection device transmitting pulse signals and receiving echo signals can be approximated as unaffected.

[0140] In one embodiment, the detection device described above may be an FMCW radar.

[0141] For ease of understanding, this embodiment uses an FMCW radar as an example for detailed explanation.

[0142] In one embodiment, the maximum unambiguous velocity of the FMCW radar (denoted as: V) max Furthermore, in actual traffic scenarios, when stationary, the FMCW radar can accurately measure the speed of vehicles traveling normally on the road, meaning that the speed of the vehicle relative to the stationary FMCW radar is less than or equal to the aforementioned maximum unambiguous speed value.

[0143] It should be understood that the aforementioned maximum unambiguous speed is a performance parameter of the FMCW radar itself.

[0144] Preferably, the example given is that the FMCW radar can accurately measure the speed of vehicles driving normally on the road when stationary.

[0145] Under normal traffic conditions that meet the above requirements, there are two types of driving relationships between vehicles: driving in the same direction and driving in opposite directions.

[0146] Scenario 1: Vehicle 701, equipped with FMCW radar, is traveling in the opposite direction to target 702.

[0147] The speed of vehicle 701 is denoted as V. c The movement speed of target 702 is denoted as V1.

[0148] It should be understood that target 702 can be a moving vehicle, a stationary object, or a pedestrian walking, and this application embodiment does not limit this. However, whether it is a stationary object, a walking pedestrian, or other moving object, under the normal traffic scenario set in this embodiment, its speed value relative to the FMCW radar is less than or equal to the maximum unambiguous speed value of the FMCW radar.

[0149] Under the premise of satisfying the above, V c V1 and V2 should satisfy the following relationships respectively:

[0150] V c ∈[0,V max ],V1∈[-V max ,0]

[0151] The FMCW radar mounted on vehicle 701 emits pulse signals to the surrounding area at a specified pulse repetition frequency. Some of these pulse signals, upon encountering target 702, are received by the radar as echo signals. Simultaneously, the vehicle's driving system computing platform can detect the real-time vehicle speed V of vehicle 701 using the vehicle speed sensor. c It should be understood that this radar is mounted on vehicle 701, so the FMCW radar's movement speed is also V. c .

[0152] In this embodiment, the relative velocity of target 702 with respect to the radar is denoted as V1'. Therefore, when V1∈[-V max When [0, 0], V1' should satisfy the following relationship:

[0153] V1'∈[-V max -V c ,-V c ]

[0154] Based on the premise established in this embodiment, when the relative speed of target 702 relative to the radar satisfies the above relationship, the radar can obviously accurately detect the relative speed of target 702, achieve accurate identification of target 702, and avoid speed ambiguity.

[0155] Scenario 2: Vehicle 701, equipped with FMCW radar, is traveling in the same direction as target 703.

[0156] The speed of vehicle 701 is denoted as V. cThe movement speed of target 703 is denoted as V2.

[0157] In the normal traffic scenario set in this embodiment, the speed of target 703 relative to the FMCW radar is less than or equal to the maximum unambiguous speed value of the FMCW radar.

[0158] Under the above conditions, V c V1 and V2 satisfy the following relationships:

[0159] V c ∈[0,V max ],V2∈[0,V max ]

[0160] The FMCW radar mounted on vehicle 701 emits pulse signals to the surrounding area at a specified pulse repetition frequency. Some of these pulse signals, upon encountering target 703, are received by the radar as echo signals. Simultaneously, the vehicle's driving system computing platform can detect the real-time vehicle speed V of vehicle 701 using the vehicle speed sensor. c It should be understood that this radar is mounted on vehicle 701, so the FMCW radar's movement speed is also V. c .

[0161] In this embodiment, the relative velocity of target 703 with respect to the radar is denoted as V2'. Therefore, when V2∈[0,V max At that time, and V2' satisfies the following relationship:

[0162] V2'∈[-V c V max -V c ]

[0163] Based on the premise established in this embodiment, when the relative speed of target 703 relative to the radar satisfies the above relationship, the radar can obviously accurately detect the relative speed of target 703, accurately identify target 703, and there will be no speed ambiguity.

[0164] In one embodiment, the relative velocity of the target detected by the radar can also be the radial velocity of the target relative to the radar, that is, the velocity component of the measured target velocity in the direction of the radar echo signal, which is the projection of the velocity vector in the radar line of sight direction.

[0165] In both of the above scenarios, by obtaining the maximum unambiguous speed of the FMCW radar and the real-time vehicle speed of the vehicle equipped with the FMCW radar, the unambiguous speed measurement range within which the FMCW radar can accurately measure the target speed in both scenarios is determined as follows:

[0166] [-V max -V c Vmax -V c ]

[0167] It should be understood that, taking radar detection of vehicles on the road as an example, the unambiguous speed measurement range directly determined by the maximum unambiguous speed range of the FMCW radar is:

[0168] [-V max V max ]

[0169] As explained above, in scenarios where a vehicle equipped with an FMCW radar is in motion, if the FMCW radar also uses the aforementioned [-V]... max V max To avoid blurring the speed measurement range, situations can easily arise where the speed of the target being measured is ambiguous: for example, a vehicle equipped with radar and the vehicle being measured are traveling in opposite directions, and both have a speed of V. max At this point, the relative velocity of the target detected by the FMCW radar relative to itself is actually -2V. max This exceeds the unambiguous velocity measurement range of the FMCW radar [-V max V max Therefore, the detected relative velocity of the target is only the endpoint value V of the unambiguous interval. max This causes velocity ambiguity. The most direct way to solve this problem is to increase the radar's maximum unambiguous velocity, for example, by a factor of two, thereby expanding the FMCW radar's unambiguous velocity measurement range to [-2V]. max 2V max At this point, the length of the expanded unambiguous velocity measurement interval is 4V. max However, as described above, doubling the maximum unambiguous velocity of the FMCW radar would deteriorate other performance indicators of the FMCW radar, or increase the complexity and cost of the hardware.

[0170] Furthermore, taking the above scenario one as an example, when vehicles 701 and 702 are traveling in opposite directions, the radar should detect the relative speed of vehicle 702 within the range [-2V]. max Within the range [0, 0]. Taking the above scenario two as an example, when vehicles 701 and 703 are traveling in the same direction, the radar should detect the relative speed of vehicle 703 within the range [0, V]. max Therefore, considering both situations, if the maximum unambiguous velocity is directly expanded, the unambiguous velocity measurement range of the FMCW radar can only utilize the range [-2V]. max V max For the interval [V] max 2V max This means that the above-mentioned solution of increasing the maximum unambiguous speed of the FMCW radar is unusable, which also explains why the radar time and frequency resources are inevitably wasted.

[0171] In this embodiment, the FMCW radar dynamically updates the unambiguous speed measurement range by acquiring the maximum unambiguous speed and the real-time speed of vehicle 701: [-V max -V c V max -V c This interval eliminates the influence of the radar's own moving speed on the target velocity measurement process, ensuring as much as possible that the relative velocity of the detected target does not become blurred, and the length of this interval remains 2V. max Furthermore, the method proposed in this application can dynamically update its unambiguous velocity measurement range according to the radar's own moving speed, and the maximum range within which the radar can accurately measure velocity is [-2V]. max V max By utilizing a relatively small maximum unambiguous speed, the unambiguous speed measurement range is adaptively adjusted to cover [-2V]. max V max The method covers a wide range of time and frequency resources without wasting radar time and frequency resources, thus improving the overall performance of the radar. Furthermore, this method can be implemented based on FMCW radar without introducing new measurement errors or increasing hardware complexity.

[0172] Figure 8 A flowchart of a method for dynamically updating unambiguous speed measurement intervals provided in an embodiment of this application is shown.

[0173] S810 transmits a signal through a detection device and receives the echo signal reflected by the target;

[0174] In one embodiment, the detection device described above is an FMCW radar.

[0175] In one embodiment, the transmitted signal is a pulse signal, specifically, a pulse repetition signal.

[0176] S820 determines the unambiguous speed measurement range based on the real-time speed of the detection device and the maximum unambiguous speed of the detection device.

[0177] It should be understood that the real-time speed and maximum unambiguous speed of the detection device are known parameters, which can be obtained from external devices or locally stored data information. This application embodiment does not limit this.

[0178] In one embodiment, the FMCW radar can also adjust its pulse repetition frequency and / or pulse signal wavelength in real time based on road speed limit information, thereby adjusting its maximum unambiguous speed. Since road speed limits are usually much lower than the maximum speed of vehicles, the radar can further narrow its unambiguous speed range by dynamically updating its maximum unambiguous speed range based on its own moving speed and then dynamically updating its maximum unambiguous speed based on road speed limit information. This further saves the radar's time and frequency resources and improves the overall performance of the radar.

[0179] For ease of understanding, the embodiments of this application are only described in detail for scenarios with a fixed pulse repetition frequency. However, the method for dynamically updating the unambiguous speed measurement range provided in the embodiments of this application is also applicable in scenarios where the pulse repetition frequency is updated in real time according to road speed limit information.

[0180] In one embodiment, the radar can be mounted on a vehicle; therefore, the radar's real-time speed can be equal to the vehicle's real-time speed. It should be understood that the computing platform can obtain the vehicle's real-time speed through sensors, such as Hall effect sensors, or through other means. This application does not limit the method for obtaining the vehicle's real-time speed. Each time the radar transmits a pulse signal, it obtains the vehicle's real-time speed from the aforementioned sensors.

[0181] In one embodiment, according to Figure 6 The unambiguous speed measurement intervals in scenarios one and two shown are ultimately determined as follows:

[0182] [-V max -V c V max -V c ]

[0183] S830 determines the relative speed of the target with respect to the detection device based on the echo signal and the real-time speed of the detection device, ensuring that the relative speed is within the unambiguous speed measurement range.

[0184] It should be understood that the data processing method provided in this application dynamically updates the unambiguous speed measurement range of the FMCW radar based on the current speed of the vehicle, reducing the ambiguity in the radar's target speed detection without expanding the maximum unambiguous speed of the FMCW radar. Compared to improved FMCW radar, this method effectively saves the time and frequency resources of the FMCW radar, does not cause deterioration of other radar indicators, improves the overall performance of the FMCW radar, does not introduce additional phase between channels, ensures the accuracy of the detected target angle, and does not increase the hardware complexity of the radar.

[0185] For CDM-MIMO type FMCW radar, after updating the unambiguous velocity measurement range, the real-time velocity V of the radar can be directly used. c The target velocity obtained by performing a Fourier transform on the slow-time sampled signal can be used for compensation.

[0186] For FDM-MIMO FMCW radar, after updating the unambiguous velocity measurement range, the radar's real-time velocity V is directly utilized. c The target velocity obtained by performing a Fourier transform on the slow-time sampled signal can be used for compensation.

[0187] It should be understood that CDM-MIMO and FDM-MIMO FMCW radars transmit multi-channel pulse signals concurrently. Unlike these two types of radars, TDM-MIMO FMCW radars transmit multi-channel pulse signals in a round-robin manner. Furthermore, when a TDM-MIMO FMCW radar is in motion, it dynamically updates the unambiguous velocity measurement interval based on its real-time speed. This unambiguous velocity measurement interval is not symmetrical about the origin 0. In summary, the velocity unambiguity resolution scheme for TDM-MIMO FMCW radar needs to improve the 3D-FFT velocity dimension processing. Specifically, the window function added between chirps should be improved so that the velocity dimension fast Fourier transform is processed based on the sampled signal of the aforementioned unambiguous velocity measurement interval to calculate the velocity value of the target.

[0188] The aforementioned velocity deambiguation scheme consists of two parts: an improved 3D-FFT velocity dimension data preprocessing method and an improved 3D-FFT velocity calculation method. Therefore, for TDM-MIMO FMCW radar, the data processing method provided in this application embodiment can include the aforementioned velocity deambiguation process.

[0189] Figure 9 A flowchart of the improved 3D-FFT velocity dimension data preprocessing method provided in an embodiment of this application is shown.

[0190] S910, determine the first data, which is the data obtained after the echo signal undergoes a distance-dimensional fast Fourier transform.

[0191] In one embodiment, the TDM-MIMO FMCW radar has multiple-input multiple-output hardware characteristics. Therefore, this radar has multiple antenna channels receiving echo signals, and the radar needs to perform 3D-FFT processing on the echo signals received by multiple antenna channels. Therefore, the aforementioned first data can be multidimensional data obtained by performing a range-dimensional fast Fourier transform on the echo signals received by the detection device.

[0192] It should be understood that the aforementioned echo signal is a sampled signal.

[0193] Existing TDM-MIMO FMCW radars, upon receiving echo signals, first perform a range-dimensional FFT on the signal to obtain signal data after a range-dimensional Fast Fourier Transform. Since the radar has multiple antenna channels receiving echo signals, the data after the range-dimensional Fast Fourier Transform can be the signal data from multiple antenna channels; that is, the signal data is multidimensional. Similarly, the signal data after velocity and angle-dimensional Fast Fourier Transforms can also be multidimensional. This application does not limit this aspect.

[0194] S920, determine the first window function based on the real-time speed and the maximum unambiguous speed of the detection device.

[0195] In one embodiment, before determining the first window function, it is necessary to determine the adjustment factor ε and obtain the second window function T between chirp. w .

[0196] In one embodiment, the above-mentioned adjustment factor can be determined according to the following formula (3):

[0197] ε=1-V c / V max (3)

[0198] Among them, V c V represents the real-time velocity of the detection device. max This indicates the maximum unambiguous velocity of the detection device.

[0199] It should be understood that the second window function T w When the unambiguous velocity measurement interval is symmetrical about the origin 0, it is a window function added between chirps during the velocity dimension fast Fourier transform. Commonly used second window functions include the Hamming window and the Hanning window.

[0200] In addition, it is necessary to obtain the first motion compensation factor α(ε) within the chirp of each channel and the second motion compensation factor β(ε) between the chirps of each channel.

[0201] In one embodiment, the first motion compensation factor α(ε) can be a vector that can be determined by the following formula (4):

[0202]

[0203] In one embodiment, the second motion compensation factor β(ε) can be a vector that can be determined by the following formula (5):

[0204] β(ε)=[1,exp(jπε),...,exp(jπε(L-1))] (5)

[0205] In formulas (4) and (5) above, N t This indicates the number of transmitting antennas, where L represents the number of chirps transmitted by a single transmitting antenna within one frame.

[0206] It should be understood that, since the technical solution of this application is based on the above-mentioned unambiguous speed measurement range dynamically adjusted according to the real-time speed of the detection device, and the first motion compensation factor and the second motion compensation factor are also adjusted in real time according to the speed of the detection device, the first motion compensation factor can be used to perform corresponding motion compensation on the signal within the chirp, and the second motion compensation factor can be used to perform corresponding motion compensation on the signal between chirps.

[0207] In obtaining the second window function T w After determining the first motion compensation factor α(ε) and the second motion compensation factor β(ε), the first window function T(ε) between chirps can be determined based on the adjustment factor.

[0208] In one embodiment, the first window function T(ε) can be determined by the following formula (6):

[0209]

[0210] in, It is N r A 1-dimensional vector of all 1s, N r This indicates the number of receiving antennas, L represents the number of chirps transmitted by a single transmitting antenna in one frame, and ⊙ represents the dot product. It represents the Kronecker product.

[0211] It should be understood that formula (6) can be derived from formulas (3)-(5).

[0212] In one embodiment, based on the first window function T(ε) obtained by the above process, it is possible to obtain multidimensional data Y after adding the first window function between chirps on arbitrary distance channels.

[0213] In one embodiment, the multidimensional data Y after adding the first window function between chirps on arbitrary distance channels can be determined by the following formula (7):

[0214]

[0215] Where X represents the multidimensional data of the target echo in any distance channel after undergoing a distance-dimensional FFT.

[0216] It should be understood that X can be obtained by performing range-dimensional FFT processing on the echo signals of each range channel in the existing 3D-FFT processing flow.

[0217] S930, based on the first data and the first window function, determines the relative velocity of the target being measured.

[0218] It should be understood that after the above-mentioned improved 3D-FFT velocity dimension data preprocessing, improved 3D-FFT velocity calculation is still required.

[0219] Figure 10 A flowchart of the improved 3D-FFT speed calculation method provided in the embodiments of this application is shown.

[0220] S1010, determine the second data, which is the data after the first data is added with the first window function and then subjected to the velocity dimension fast Fourier transform.

[0221] In one embodiment, since the first data can be multidimensional data obtained by performing a distance-dimensional fast Fourier transform on the echo signal received by the detection device, the second data can be multidimensional data obtained by performing a velocity-dimensional fast Fourier transform on the first data after adding a first window function.

[0222] S1020, Based on the second data, determine the velocity element length and the Doppler channel index of the relative velocity of the detection device.

[0223] It should be understood that the aforementioned detection device can be a TDM-MIMO FMCW radar, and the velocity element length is a system parameter of the TDM-MIMO FMCW radar itself, which can be obtained directly.

[0224] In one embodiment, the Doppler channel index of the target being measured can be obtained through the slow-time Fourier transform described above.

[0225] S1030, determine the compensation factor based on the adjustment factor and the maximum unambiguous speed;

[0226] In one embodiment, the compensation factor can be determined according to the following formula (8):

[0227] ΔV=-εV max (8)

[0228] Where ΔV is the compensation factor mentioned above. It should be understood that ε and V in formula (8) max All parameters are those that have been determined in the above-mentioned improved 3D-FFT velocity dimension data preprocessing process, and the values ​​of the parameters remain unchanged.

[0229] S1040, based on the velocity element length of the detection device, the Doppler channel index of the target, and the compensation factor, determine the compensated velocity solution result of the target.

[0230] In one embodiment, the velocity calculation result of the measured target can be determined according to the following formula (9):

[0231] V=N doppler δV+ΔV (9)

[0232] Where, N doppler δV represents the Doppler channel index where the target is located, and δV represents the velocity element length of the detection device.

[0233] It should be understood that the improved 3D-FFT velocity deambiguation method described above enables TDM-MIMO FMCW radar to accurately calculate the velocity of the target by dynamically updating the unambiguous velocity measurement range based on its own moving speed, thereby reducing the occurrence of velocity ambiguity in radar target detection.

[0234] Since the angle dimension FFT in FMCW radar 3D-FFT processing is based on the result data after velocity dimension FFT processing, when there is no velocity ambiguity in the measured target, the multi-channel data after velocity dimension FFT processing is also accurate, which can effectively improve the accuracy of the angle dimension FFT data processing result in 3D-FFT processing.

[0235] In addition, the TDM-MIMO FMCW radar implements the data processing method in the above embodiments, which can dynamically update the radar's unambiguous velocity measurement range through the radar's real-time speed, reduce the ambiguity of the radar's target speed detection, and does not expand the radar's maximum unambiguous speed. This can effectively save the radar's time and frequency resources, does not cause the deterioration of other radar indicators, improves the overall performance of the radar, and does not increase the radar's hardware complexity.

[0236] Figure 11 A schematic block diagram of a data processing apparatus 1100 provided in an embodiment of this application is shown. Figure 11 As shown, the detection device 1100 includes:

[0237] Control unit 1110 is used to control the detection device to transmit signals and to control the detection device to receive echo signals reflected by the target;

[0238] Determining unit 1120 is used to determine the real-time velocity V of the detection device. c With the maximum unambiguous velocity V of the detection device max Determine the unambiguous speed measurement range.

[0239] Optionally, the determining unit 1120 is further configured to: determine the following unambiguous speed measurement interval based on the maximum unambiguous speed and the real-time speed:

[0240] [-V max -V cV max -V c ]

[0241] Optionally, the determining unit 1120 is further configured to: determine the speed of the target relative to the detection device based on the echo signal and the real-time speed, so that the calculated speed is within the unambiguous speed measurement range.

[0242] Optionally, the determining unit 1120 is further configured to: determine first data, which is data obtained after the echo signal undergoes a distance-dimensional fast Fourier transform; and determine a first window function based on the real-time speed and maximum unambiguous speed of the detection device.

[0243] Optionally, the determining unit 1120 is further configured to: determine second data, which is the data obtained by adding a first window function to the first data and then performing a fast Fourier transform in the velocity dimension; determine the velocity unit length and the Doppler channel index of the relative velocity of the detection device based on the second data; determine the compensation factor based on the adjustment factor and the maximum unambiguous velocity; and determine the relative velocity based on the velocity unit length, the Doppler channel index, and the compensation factor.

[0244] This application also provides an FMCW radar, which includes the FMCW radar described above capable of implementing the aforementioned methods. Furthermore, the radar also includes a control chip connected to the antenna device of the FMCW radar. The control chip is used to control the antenna device to transmit or receive signals.

[0245] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0246] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0247] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0248] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0249] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0250] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0251] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method of data processing, characterized by, include: The device transmits a signal and receives the echo signal reflected by the target, wherein the transmitted signal includes a pulse signal; Determine the first data, which is the data obtained after the echo signal undergoes a distance-dimensional fast Fourier transform; According to the real-time speed of the detection device With the maximum unambiguous speed of the detection device Determine the unambiguous speed measurement interval and the first window function. ; The first window function is determined according to the following formulas (1)-(4): （1） （2） （3） （4） in, Indicates the regulating factor. Indicates the first motion compensation factor. This represents the second motion compensation factor. This indicates the number of transmitting antennas of the detection device. L This indicates the number of pulse signals transmitted by the transmitting antenna within a preset time period. This represents the second window function between the pulse signals. express A dimensional vector of all 1s. This indicates the number of receiving antennas in the detection device. Represents the dot product. Indicates the Kronecker product; Based on the first data and the first window function Determine the relative velocity of the target with respect to the detection device. , so that the relative velocity Within the unambiguous speed measurement range.

2. The method according to claim 1, characterized in that, The unambiguous speed measurement range is: 。 3. The method according to claim 1 or 2, characterized in that, The first data and the first window function are used. Determine the relative velocity ,include: Determine the second data, which is the first data plus the first window function. Then, the data after undergoing a velocity-dimensional fast Fourier transform; Based on the second data, determine the velocity unit length of the detection device. and the relative velocity Doppler channel index ; According to the regulation factor and the maximum unambiguous speed Determine the compensation factor ; According to the speed unit length The Doppler channel index and the compensation factor Determine the relative velocity .

4. The method according to claim 3, characterized in that, According to the adjustment factor and the maximum unambiguous speed Determine the compensation factor ,include: The compensation factor is determined according to the following formula (5). : 。（5） 5. The method according to claim 3 or 4, characterized in that, According to the speed unit length The Doppler channel index and the compensation factor Determining the relative velocity includes: The relative velocity is determined according to the following formula (6). : 。（6） 6. The method according to any one of claims 1 to 5, characterized in that, The detection device is a frequency-modulated continuous wave (FMCW) radar based on the time-division multiplexing multiple-input multiple-output (TDM-MIMO) standard.

7. A data processing apparatus, characterized in that, include: A control unit is used to control the detection device to transmit signals and to control the detection device to receive echo signals reflected by the target, wherein the transmitted signals include pulse signals; The determining unit is used to determine the real-time speed of the detection device. With the maximum unambiguous speed of the detection device Determine the unambiguous speed measurement interval and the first window function. It is also used to determine the first window function according to the following formulas (1)-(4): （1） （2） （3） （4） in, Indicates the regulating factor. Indicates the first motion compensation factor. This represents the second motion compensation factor. This indicates the number of transmitting antennas of the detection device. L This indicates the number of pulse signals transmitted by the transmitting antenna within a preset time period. This represents the second window function between the pulse signals. express A dimensional vector of all 1s. This indicates the number of receiving antennas in the detection device. Represents the dot product. Represents the Kronecker product; also used to determine the product based on the first data and the first window function. Determine the relative velocity of the target with respect to the detection device. , so that the relative velocity Within the unambiguous speed measurement range.

8. The apparatus according to claim 7, characterized in that, The unambiguous speed measurement range is: 。 9. The apparatus according to claim 7 or 8, characterized in that, The determining unit is specifically used for: Determine the second data, which is the first data plus the first window function. Then, the data after undergoing a velocity-dimensional fast Fourier transform; Based on the second data, determine the velocity unit length of the detection device. and the relative velocity Doppler channel index ; According to the regulation factor and the maximum unambiguous speed Determine the compensation factor ; According to the speed unit length The Doppler channel index and the compensation factor Determine the relative velocity .

10. The apparatus according to claim 9, characterized in that, The determining unit is specifically used for: The compensation factor is determined according to the following formula (5). : 。（5） 11. The apparatus according to claim 9 or 10, characterized in that, The determining unit is specifically used for: The relative velocity is determined according to the following formula (6). : 。（6） 12. The apparatus according to any one of claims 7 to 11, characterized in that, The detection device is a frequency-modulated continuous wave (FMCW) radar based on the time-division multiplexing multiple-input multiple-output (TDM-MIMO) standard.

13. A data processing apparatus, characterized in that, include: Memory, used to store computer instructions; A processor for executing computer instructions stored in the memory to cause the apparatus to perform the method as described in any one of claims 1 to 6.

14. A means of transportation, characterized in that, Includes the apparatus as described in any one of claims 7 to 13.

15. A computer storage medium, characterized in that, The computer storage medium stores computer instructions, which, when executed on the computer, cause the computer to perform the method as described in any one of claims 1 to 6.

16. A chip, characterized in that, Includes a processor for performing the method as described in any one of claims 1 to 6.

17. A computer program product, characterized in that, When the computer program code or instructions are executed on a computer, the computer causes the computer to perform the method as described in any one of claims 1 to 6.

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