Vehicle-mounted MIMO millimeter wave radar target orientation estimation method and system

Abstract

The embodiment of the invention relates to the technical field of vehicle-mounted millimeter-wave radars, and particularly discloses a vehicle-mounted MIMO millimeter-wave radar target orientation estimation method and system. According to the embodiment of the invention, the vehicle-mounted MIMO millimeter wave radar is controlled and processed to generate a plurality of radar echo signals; carrying out basic preprocessing and clutter suppression on the signal; carrying out one-dimensional Fourier transform and two-dimensional Fourier transform to respectively obtain a target distance and a target speed; performing target detection; and performing target azimuth estimation by adopting a beam forming algorithm to determine a target azimuth angle. Radar detection and signal processing can be carried out through a virtual array of the vehicle-mounted MIMO millimeter wave radar, a target distance and a target speed are obtained through one-dimensional Fourier transform and two-dimensional Fourier transform, then target detection is carried out, a beam forming algorithm is adopted to carry out target azimuth estimation, the azimuth resolution is effectively improved, azimuth estimation errors are avoided, and the target azimuth estimation accuracy is improved. Therefore, the accurate perception of the vehicle to the traffic environment is improved.

Description

Technical Field

[0001] This invention belongs to the field of vehicle-mounted millimeter-wave radar technology, and particularly relates to a target azimuth estimation method and system for vehicle-mounted MIMO millimeter-wave radar. Background Technology

[0002] In recent years, with the advancement and development of information technology and the passage of time, autonomous driving technology has gradually become an inevitable trend in the future development of the electric vehicle industry. This has led to the widespread application and development of Advanced Driver Assistance Systems (ADAS). In practical applications, to achieve highly reliable driving decisions, high-precision, high-visibility onboard sensors are required. Common onboard sensors include: millimeter-level electromagnetic wave radar, temperature sensors, humidity sensors, visual displays, and lidar.

[0003] In the field of automotive millimeter-wave radar, radar is limited by size and cannot integrate too many antennas. Therefore, MIMO technology, which can obtain a large number of signal channels with fewer antennas, has been widely developed and applied in the field of automotive millimeter-wave radar.

[0004] In existing technologies, when vehicle-mounted radar performs target location estimation, the location resolution is low due to limitations in antenna aperture and signal processing algorithms, which easily leads to location estimation errors and affects the vehicle's accurate perception of the traffic environment. Summary of the Invention

[0005] The purpose of this invention is to provide a target azimuth estimation method and system for vehicle-mounted MIMO millimeter-wave radar, aiming to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the embodiments of the present invention provide the following technical solutions: A target azimuth estimation method for vehicle-mounted MIMO millimeter-wave radar, the method specifically includes the following steps: The vehicle-mounted MIMO millimeter-wave radar is controlled by transmitting radar detection signals in a modulation manner, receiving multiple mixed signals, and processing them to generate multiple radar echo signals. The radar echo signals are subjected to basic preprocessing and clutter suppression to generate multiple effective radar signals. One-dimensional Fourier transform and two-dimensional Fourier transform are performed on multiple radar effective signals to obtain the target range and target velocity, respectively; Construct a distance-Doppler map, perform target detection, and obtain the target's location coordinates; By combining the target's position coordinates, target distance, and target velocity, a beamforming algorithm is used to estimate the target's azimuth and determine the target's azimuth angle.

[0007] A vehicle-mounted MIMO millimeter-wave radar target azimuth estimation system, comprising a radar detection and control unit, a signal clutter suppression unit, a Fourier transform processing unit, a position coordinate acquisition unit, and a target azimuth estimation unit, wherein: The radar detection control unit is used to control the vehicle-mounted MIMO millimeter-wave radar. It transmits radar detection signals using a modulation method, receives multiple mixed signals, and processes and generates multiple radar echo signals. The signal clutter suppression unit is used to perform basic signal preprocessing and clutter suppression on multiple radar echo signals to generate multiple effective radar signals. The Fourier transform processing unit is used to perform one-dimensional Fourier transform and two-dimensional Fourier transform on multiple radar effective signals to obtain target distance and target velocity, respectively. The location coordinate acquisition unit is used to construct a distance-Doppler map, perform target detection, and obtain the target's location coordinates; The target azimuth estimation unit is used to combine the target position coordinates, the target distance and the target velocity, and use a beamforming algorithm to estimate the target azimuth and determine the target azimuth angle.

[0008] Compared with the prior art, the beneficial effects of the present invention are: This invention, through the control of an onboard MIMO millimeter-wave radar, generates multiple radar echo signals; performs basic signal preprocessing and clutter suppression; performs one-dimensional and two-dimensional Fourier transforms to obtain target range and velocity, respectively; performs target detection; and employs a beamforming algorithm to estimate target azimuth and determine the target azimuth angle. By utilizing the virtual array of the onboard MIMO millimeter-wave radar, radar detection and signal processing are performed. Target range and velocity are obtained through one-dimensional and two-dimensional Fourier transforms, followed by target detection and beamforming algorithm-based target azimuth estimation. This effectively improves azimuth resolution, avoids azimuth estimation errors, and enhances the vehicle's accurate perception of the traffic environment. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention.

[0010] Figure 1 A flowchart of the method provided by an embodiment of the present invention is shown.

[0011] Figure 2 An application architecture diagram of the system provided in an embodiment of the present invention is shown. Detailed Implementation

[0012] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0013] Understandably, in the field of automotive millimeter-wave radar, radar is limited by size and cannot integrate too many antennas. Therefore, MIMO technology, which obtains a large number of signal channels with fewer antennas, has been widely developed and applied in the field of automotive millimeter-wave radar. In existing technologies, when automotive radar performs target azimuth estimation, the azimuth resolution is low due to limitations in antenna aperture and signal processing algorithms, which easily leads to azimuth estimation errors and affects the vehicle's accurate perception of the traffic environment.

[0014] To address the aforementioned issues, this invention controls an onboard MIMO millimeter-wave radar, employing modulation to transmit radar detection signals, receiving multiple mixed signals, and processing them to generate multiple radar echo signals. Basic preprocessing and clutter suppression are performed on these multiple radar echo signals to generate multiple effective radar signals. One-dimensional and two-dimensional Fourier transforms are then performed on these effective radar signals to obtain target range and velocity, respectively. A range-Doppler map is constructed for target detection, obtaining target position coordinates. Combining the target position coordinates, target range, and target velocity, a beamforming algorithm is used to estimate the target's azimuth and determine its azimuth angle. This allows for radar detection and signal processing via a virtual array of the onboard MIMO millimeter-wave radar. By acquiring target range and velocity through one-dimensional and two-dimensional Fourier transforms, and then performing target detection and beamforming algorithm for target azimuth estimation, the azimuth resolution is effectively improved, azimuth estimation errors are avoided, and the vehicle's accurate perception of the traffic environment is enhanced.

[0015] Figure 1 A flowchart of the method provided by an embodiment of the present invention is shown.

[0016] Specifically, the target azimuth estimation method for vehicle-mounted MIMO millimeter-wave radar includes the following steps: Step S101: Control the vehicle-mounted MIMO millimeter-wave radar, transmit radar detection signals using modulation, receive multiple mixed signals, and process to generate multiple radar echo signals.

[0017] In this embodiment of the invention, the vehicle-mounted MIMO millimeter-wave radar is controlled to generate radar detection signals using modulation methods such as linear frequency modulated continuous wave or orthogonal frequency division multiplexing. Multiple transmitting antennas corresponding to the radar detection signals are matched, and initial phase and / or frequency offsets corresponding to the multiple transmitting antennas are planned. Then, according to the multiple initial phase and / or frequency offsets, the multiple transmitting antennas are controlled to transmit the corresponding radar detection signals. This allows the multiple transmitting antennas to achieve signal orthogonality through different initial phases or frequency offsets, thus avoiding mutual interference between the multiple radar detection signals. Multiple receiving antennas then receive multiple mixed signals, which are then amplified, filtered, and / or analog-to-digital converted to generate multiple digital radar echo signals.

[0018] Specifically, in the preferred embodiment provided by the present invention, controlling the vehicle-mounted MIMO millimeter-wave radar, transmitting radar detection signals using a modulation method, receiving multiple mixed signals, and processing to generate multiple radar echo signals specifically includes the following steps: The vehicle-mounted MIMO millimeter-wave radar is controlled by using linear frequency modulated continuous wave or orthogonal frequency division multiplexing to generate radar detection signals. Match multiple transmitting antennas corresponding to the radar detection signals; Plan the initial phase and / or frequency offsets corresponding to the multiple transmitting antennas; According to the multiple initial phases and / or multiple frequency offsets, control the multiple transmitting antennas to transmit corresponding radar detection signals; Multiple receiving antennas are used to receive multiple mixed signals; The multiple mixed signals are amplified, filtered, and / or converted from analog to digital to generate multiple radar echo signals.

[0019] In a preferred embodiment of the present invention, the specific steps for planning the frequency offsets corresponding to the multiple transmitting antennas in the step of planning the initial phase and / or frequency offsets corresponding to the multiple transmitting antennas are as follows: The dynamic nonlinear index is obtained by generating a dynamic nonlinear index that adjusts dynamically with the current ambient temperature. Iterate through each transmitting antenna and perform an exponential operation on the transmitting antenna number according to the dynamic nonlinear exponent, and generate a nonlinear growth factor based on the result of the exponential operation. Based on the radar hardware architecture, the total bandwidth and the total number of transmitting antennas are obtained, and the ratio of the bandwidth allocated to each transmitting antenna to the total bandwidth is calculated to obtain the allocation ratio coefficient of each transmitting antenna. Based on the allocation ratio coefficient and nonlinear growth factor of each transmitting antenna, the frequency offset of each transmitting antenna is calculated, and the corresponding frequency offset value of each transmitting antenna is obtained.

[0020] In a preferred embodiment of the present invention, the specific steps for planning the initial phases and / or frequency offsets corresponding to the plurality of transmitting antennas are as follows: Determine the number of available polarization channels based on the radar hardware architecture; Based on the number of available polarization channels, a unique number is assigned to each polarization channel to obtain the polarization channel number; based on the polarization channel number, the horizontal / vertical polarization state is configured for the available polarization channels according to the orthogonality principle to obtain the polarization channel configuration table; The polarization channel number corresponding to each transmitting antenna is found according to the polarization channel configuration table. Based on the correlation between the transmitting antenna number and the parity of the polarization channel, a spatial-polarization two-dimensional alternating reversal mechanism is constructed to automatically generate phase polarity reversal between adjacent antennas or different polarization channels, thus obtaining the dynamic alternation factor. The nonlinear phase gradient of different transmit antenna numbers is calculated using cubic operations, and the enhancement weight of the corresponding polarization channel number of the transmit antenna is calculated using square operations. The nonlinear phase gradient and the corresponding enhancement weight are used to calculate the phase, and a corresponding dynamic alternation factor is introduced during the phase calculation process to form the phase calculation term. The reference phase is determined based on the system clock, and the phase calculation terms are superimposed on the reference phase to obtain the phase parameters. The phase parameters are normalized by phase period to obtain the initial phase parameter matrix of the antenna-polarization channel combination.

[0021] In this embodiment of the invention, by dynamically binding polarization parameters (horizontal / vertical) with frequency offset, signals in the same frequency band exhibit orthogonal characteristics in different polarization channels. Electromagnetic waves in different polarization states have different reflection and attenuation characteristics during propagation. Combined with the non-uniform distribution in the frequency domain (generated by a logarithmic function), a multi-dimensional orthogonal signal space can be formed. Signals with different polarization directions can be separated at the receiving end through polarization filtering, reducing co-channel interference. A nonlinear phase gradient is constructed using the cubic function of the transmit antenna index and the square function of the polarization channel, breaking the periodic arrangement of the traditional linear array. The phase difference introduced by the cubic function increases rapidly with the transmit antenna index. The non-uniform phase distribution can improve the equivalent aperture of the array and suppress the grating lobe effect, avoiding the generation of grating lobes in the virtual array. Furthermore, the nonlinear exponent and polarization activation mode are dynamically adjusted by monitoring environmental parameters. For example, temperature changes affect device stability, and the frequency offset error is compensated by adjusting the nonlinear exponent; the polarization mode is switched when the target velocity changes to optimize signal penetration.

[0022] Furthermore, the vehicle-mounted MIMO millimeter-wave radar target azimuth estimation method further includes the following steps: Step S102: Perform basic preprocessing and clutter suppression on the multiple radar echo signals to generate multiple effective radar signals.

[0023] In this embodiment of the invention, multiple preprocessed echo signals are generated by performing basic preprocessing to remove the DC component from multiple radar echo signals (the DC component can be removed by subtracting the average value of the signal, etc.). Then, the radar operating frequency range and signal bandwidth are obtained. Based on the radar operating frequency range and signal bandwidth, a filter is planned. Then, the multiple preprocessed echo signals are subjected to noise and interference suppression processing through the filter to generate multiple effective radar signals, thereby reducing the interference of noise and clutter on the signal.

[0024] Specifically, in the preferred embodiment provided by the present invention, the basic preprocessing and clutter suppression of the multiple radar echo signals to generate multiple effective radar signals specifically includes the following steps: The radar echo signals are subjected to basic preprocessing to remove the DC component, thereby generating multiple preprocessed echo signals. Obtain the radar's operating frequency range and signal bandwidth; Based on the radar operating frequency range and the signal bandwidth, plan the filter; The filter is used to suppress noise and interference in the multiple preprocessed echo signals, generating multiple effective radar signals.

[0025] In a preferred embodiment of the present invention, the step of planning the filter based on the radar operating frequency range and the signal bandwidth specifically includes the following steps: Obtain the polarization channel number, and calculate the sign factor based on the parity of the polarization channel number; Based on the symbol factor and signal bandwidth, the bandwidth expansion factor of the current channel is calculated. The center frequency is obtained based on the radar's operating frequency range, and the frequency variables are obtained based on the frequency point sequence that the filter design needs to cover. Calculate the frequency offset for each frequency point based on the center frequency and frequency variables; A Gaussian function is constructed using the bandwidth expansion factor of the current channel. Based on the frequency offset of each frequency point, the Gaussian attenuation coefficient corresponding to each frequency point is calculated using the Gaussian function. The polarization state is obtained from the polarization channel configuration table. Based on the polarization state, the Gaussian attenuation coefficient is compensated for polarization direction to calculate the frequency domain response correction factor after polarization modulation. The polarization state is used to compensate for the polarization direction of the Gaussian attenuation coefficient. That is, the Gaussian attenuation coefficient and the polarization state are used to construct a product term. If the polarization state is +1, it is horizontal polarization, and the sign of the product term is retained. If the polarization state is -1, it is vertical polarization, and the product term is inverted. Based on the dynamic nonlinear exponent, the frequency domain response correction factor after polarization modulation is enhanced by exponentiation using power operation to obtain the nonlinear enhanced frequency domain response coefficient. The rectangular window is obtained by calculating the rectangular window function based on the frequency offset and signal bandwidth of each frequency point; The frequency domain response coefficients after nonlinear enhancement are multiplied point by point with the rectangular window to obtain the filter transfer function for all frequency points; The time-domain impulse response sequence is obtained by performing normalization and inverse Fourier transform on the filter transfer function at all frequencies. The number of valid points to be extracted is determined based on real-time computing resources, and the first few valid points are extracted from the time-domain impulse response sequence. Several effective points are stored as FIR filter coefficient matrices and grouped by polarization channel to obtain the time-domain filter coefficient set for each polarization channel.

[0026] In this embodiment of the invention, the polarization coding parameters at the transmitting end are directly linked to the filter design at the receiving end. The stopband characteristics of the filter are dynamically adjusted using polarization state parameters (horizontal polarization = +1, vertical polarization = -1). When horizontal polarization = +1, out-of-band interference is suppressed and out-of-band attenuation is enhanced. When vertical polarization = -1, signal distortion is compensated and passband flatness is optimized, making signals with different polarization directions separable in the frequency domain. The frequency response characteristics of the filter are optimized through polarization parameter feedback. Furthermore, a dynamic nonlinear exponent is introduced into the power operation in the filter design. When the dynamic nonlinear exponent is high, the stopband attenuation speed is enhanced and near-end interference is suppressed. When the dynamic nonlinear exponent is low, the passband ripple and stopband performance are balanced. A steep transition band can be achieved through the higher-order attenuation characteristics (super-Gaussian curve) of the nonlinear power function. Furthermore, a polarization adaptive bandwidth expansion mechanism was designed, which dynamically adjusts the bandwidth expansion factor according to the parity of the polarization channel number. When the number of channels is odd, it forms a wide stopband suppression, and when the number of channels is even, it accurately filters out narrowband interference. By utilizing the multi-resolution characteristics of polarization diversity, the frequency domain resources can be flexibly allocated.

[0027] Furthermore, the vehicle-mounted MIMO millimeter-wave radar target azimuth estimation method further includes the following steps: Step S103: Perform one-dimensional Fourier transform and two-dimensional Fourier transform on the multiple radar effective signals to obtain the target distance and target velocity, respectively.

[0028] In this embodiment of the invention, a one-dimensional Fourier transform is performed on multiple effective radar signals to convert the time-domain signals into frequency-domain signals. According to the principle of millimeter-wave radar, the frequency of the signal is proportional to the distance to the target. By detecting the peak position in the frequency-domain signal, the target distance can be determined. Then, a two-dimensional Fourier transform is performed on the signal. Since the frequency of the signal is proportional to the Doppler frequency of the target, and the Doppler frequency is related to the relative velocity of the target, the target velocity can be calculated by detecting the peak position in the Doppler dimension.

[0029] Specifically, in the preferred embodiment provided by the present invention, the step of performing one-dimensional Fourier transform and two-dimensional Fourier transform on the plurality of radar effective signals to obtain the target distance and target velocity respectively includes the following steps: Perform a one-dimensional Fourier transform on the multiple effective radar signals; Peak detection in the frequency domain is performed to determine the target distance; Perform a two-dimensional Fourier transform on the signal; Peak detection is performed on the Doppler axis to determine the target velocity.

[0030] In a preferred embodiment of the present invention, the step of performing peak detection in the frequency domain to determine the target distance specifically includes the following steps: Extract the offset of the frequency point to be detected from the frequency domain data after one-dimensional Fourier transform; Obtain the current bandwidth expansion factor, and use the current bandwidth expansion factor to apply Gaussian function to exponentially decay the offset of the frequency point to be detected, and then use polarization state to correct it, to obtain the polarization-corrected Gaussian attenuation value. The resolution of each frequency point is calculated based on the number of frequency points in the frequency domain data after the one-dimensional Fourier transform and the signal bandwidth. Using the ratio of signal bandwidth to resolution as the denominator and the number of frequency points as the numerator, the ratio of the numerator to the denominator is calculated using the natural logarithm, and then adjusted using a dynamic nonlinear exponent to obtain the nonlinearly corrected logarithmic adjustment term. The signal amplitudes of all frequencies are obtained from the frequency domain data after one-dimensional Fourier transform, and the average power is calculated based on the signal amplitudes of all frequencies. The average power is then normalized to obtain the normalized signal average power. The polarization compensation term denominator is constructed based on the Gaussian attenuation value after polarization correction, and the ratio of the normalized signal average power to the polarization compensation term denominator is adjusted using the logarithmic adjustment term after nonlinear correction to obtain the adaptive detection threshold at the current frequency point. Obtain the signal power at the current frequency point and compare it with the adaptive detection threshold of the current frequency point; if it exceeds the adaptive detection threshold of the current frequency point, mark the current frequency point as a potential target and obtain a list of potential targets; Check if the frequency points in the potential target list have reached their peak values. If they have, block several adjacent frequency points in the potential target list to avoid repeated detection. Record all frequency points that exceed the threshold and replace all frequency points that exceed the threshold with distance values ​​to obtain the target distance.

[0031] In this embodiment of the invention, by reusing the polarization coding parameters of the transmitter, the threshold characteristics are adjusted in real time in the detection algorithm of the receiver. Horizontal polarization = +1, which reduces the detection threshold in the out-of-band region (far from the center frequency) and enhances the sensitivity of weak targets (such as pedestrians at a distance). Vertical polarization = -1, which increases the threshold in the passband (near the center frequency) and suppresses residual noise of the filter (such as rain and fog reflection). In this way, the polarization state of the transmitter and the detection logic of the receiver are linked to form a closed-loop control.

[0032] Furthermore, the vehicle-mounted MIMO millimeter-wave radar target azimuth estimation method further includes the following steps: Step S104: Construct a distance-Doppler map, perform target detection, and obtain the target location coordinates.

[0033] In this embodiment of the invention, a range-Doppler map is constructed, and the CFAR detection algorithm is used on the range-Doppler map to detect the target and obtain the target location coordinates.

[0034] Specifically, in the preferred embodiment provided by the present invention, the steps of constructing a distance-Doppler map, performing target detection, and obtaining the target position coordinates specifically include the following steps: Construct a distance-Doppler plot; On the distance-Doppler map, the CFAR detection algorithm is used to detect the target and obtain the target position coordinates.

[0035] Furthermore, the vehicle-mounted MIMO millimeter-wave radar target azimuth estimation method further includes the following steps: Step S105: Combining the target position coordinates, the target distance, and the target velocity, a beamforming algorithm is used to estimate the target azimuth and determine the target azimuth angle.

[0036] In this embodiment of the invention, a virtual array is constructed based on multiple transmitting antennas and multiple receiving antennas. The target position coordinates, target distance and target velocity are combined to calculate a weight vector. The weight vector is then multiplied with the signal of the virtual array to obtain the beamforming output. Based on the beamforming output, power calculation and power comparison are performed in different directions to determine the target azimuth angle. Specifically, the direction corresponding to the maximum value of the beamforming output power is the target azimuth angle.

[0037] Specifically, in the preferred embodiment provided by the present invention, the step of combining the target position coordinates, the target distance, and the target velocity to perform target azimuth estimation using a beamforming algorithm and determining the target azimuth angle specifically includes the following steps: A virtual array is constructed based on the plurality of transmitting antennas and the plurality of receiving antennas; Calculate the weight vector by combining the target position coordinates, the target distance, and the target velocity; The weight vector is multiplied by the signal of the virtual array to obtain the beamforming output; Based on the beamforming output, power calculations and comparisons are performed in different directions to determine the target azimuth angle.

[0038] Furthermore, Figure 2 An application architecture diagram of the system provided in an embodiment of the present invention is shown.

[0039] In another preferred embodiment of the present invention, the vehicle-mounted MIMO millimeter-wave radar target azimuth estimation system includes: The radar detection control unit 101 is used to control the vehicle-mounted MIMO millimeter-wave radar, transmits radar detection signals in a modulation manner, receives multiple mixed signals, and processes and generates multiple radar echo signals.

[0040] In this embodiment of the invention, the radar detection control unit 101 controls the vehicle-mounted MIMO millimeter-wave radar, enabling the vehicle-mounted MIMO millimeter-wave radar to generate radar detection signals using modulation methods such as linear frequency modulated continuous wave or orthogonal frequency division multiplexing, and matching multiple transmitting antennas corresponding to multiple radar detection signals. The initial phase and / or frequency offset corresponding to multiple transmitting antennas are planned, and then the multiple transmitting antennas are controlled to transmit the corresponding radar detection signals according to the multiple initial phase and / or frequency offsets. This allows the multiple transmitting antennas to achieve signal orthogonality through different initial phases or frequency offsets, thereby avoiding mutual interference between multiple radar detection signals. Then, multiple receiving antennas receive multiple mixed signals, and then amplify, filter and / or convert the multiple mixed signals to digital form radar echo signals.

[0041] Specifically, in the preferred embodiment provided by the present invention, the radar detection and control unit 101 specifically includes: The detection signal generation module 1011 is used to control the vehicle-mounted MIMO millimeter-wave radar and generates radar detection signals by using linear frequency modulated continuous wave or orthogonal frequency division multiplexing. The transmitting antenna matching module 1012 is used to match the transmitting antennas corresponding to multiple radar detection signals; The transmission parameter planning module 1013 is used to plan the initial phase and / or frequency offset corresponding to multiple transmission antennas; The signal transmission control module 1014 is used to control the multiple transmitting antennas to transmit corresponding radar detection signals according to the multiple initial phases and / or the multiple frequency offsets; The signal receiving module 1015 is used to receive multiple mixed signals through multiple receiving antennas; The mixed signal processing module 1016 is used to amplify, filter, and / or convert the multiple mixed signals to analog-to-digital signals to generate multiple radar echo signals.

[0042] Furthermore, the vehicle-mounted MIMO millimeter-wave radar target azimuth estimation system also includes: The signal clutter suppression unit 102 is used to perform basic signal preprocessing and clutter suppression on multiple radar echo signals to generate multiple effective radar signals.

[0043] In this embodiment of the invention, the signal clutter suppression unit 102 generates multiple preprocessed echo signals by performing basic preprocessing to remove the DC component from multiple radar echo signals (the DC component can be removed by subtracting the average value of the signal, etc.). Then, it obtains the radar operating frequency range and signal bandwidth, plans a filter based on the radar operating frequency range and signal bandwidth, and then uses the filter to suppress noise and interference in the multiple preprocessed echo signals, generating multiple effective radar signals, thereby reducing the interference of noise and clutter on the signal.

[0044] Specifically, in the preferred embodiment provided by the present invention, the signal clutter suppression unit 102 specifically includes: The basic preprocessing module 1021 is used to perform basic preprocessing on the multiple radar echo signals to remove the DC component, and generate multiple preprocessed echo signals. Data acquisition module 1022 is used to acquire the radar operating frequency range and signal bandwidth; The filter planning module 1023 is used to plan a filter based on the radar operating frequency range and the signal bandwidth. The suppression processing module 1024 is used to suppress noise and interference in multiple preprocessed echo signals through the filter, thereby generating multiple effective radar signals.

[0045] Furthermore, the vehicle-mounted MIMO millimeter-wave radar target azimuth estimation system also includes: The Fourier transform processing unit 103 is used to perform one-dimensional Fourier transform and two-dimensional Fourier transform on multiple radar effective signals to obtain target distance and target velocity, respectively.

[0046] In this embodiment of the invention, the Fourier transform processing unit 103 converts the time-domain signal into a frequency-domain signal by performing a one-dimensional Fourier transform on multiple effective radar signals. According to the principle of millimeter-wave radar, the frequency of the signal is proportional to the distance to the target. By detecting the peak position in the frequency-domain signal, the target distance can be determined. Then, a two-dimensional Fourier transform of the signal is performed. Since the frequency of the signal is proportional to the Doppler frequency of the target, and the Doppler frequency is related to the relative velocity of the target, the target velocity can be calculated by detecting the peak position in the Doppler dimension.

[0047] Specifically, in the preferred embodiment provided by the present invention, the Fourier transform processing unit 103 specifically includes: The signal one-dimensional Fourier transform module 1031 is used to perform one-dimensional Fourier transform on multiple radar effective signals; The target distance determination module 1032 is used to perform peak detection in the frequency domain to determine the target distance; The signal two-dimensional Fourier transform module 1033 is used to perform two-dimensional Fourier transform of signals; The target velocity determination module 1034 is used to perform peak detection in Doppler vision to determine the target velocity.

[0048] Furthermore, the vehicle-mounted MIMO millimeter-wave radar target azimuth estimation system also includes: The position coordinate acquisition unit 104 is used to construct a distance-Doppler map, perform target detection, and acquire the target position coordinates.

[0049] In this embodiment of the invention, the position coordinate acquisition unit 104 constructs a range-Doppler map and uses the CFAR detection algorithm on the range-Doppler map to detect the target and obtain the target position coordinates.

[0050] The target azimuth estimation unit 105 is used to combine the target position coordinates, the target distance and the target velocity, and use a beamforming algorithm to estimate the target azimuth and determine the target azimuth angle.

[0051] In this embodiment of the invention, the target azimuth estimation unit 105 constructs a virtual array based on multiple transmitting antennas and multiple receiving antennas, calculates a weight vector by combining the target position coordinates, target distance and target velocity, and then multiplies the weight vector with the signal of the virtual array to obtain the beamforming output. Based on the beamforming output, the power is calculated and compared in different directions to determine the target azimuth angle. Specifically, the direction corresponding to the maximum value of the beamforming output power is the target azimuth angle.

[0052] It should be understood that although the steps in the flowcharts of the various embodiments of the present invention are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the various embodiments may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps.

[0053] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0054] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0055] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

[0056] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A target azimuth estimation method for vehicle-mounted MIMO millimeter-wave radar, characterized in that, The method specifically includes the following steps: The vehicle-mounted MIMO millimeter-wave radar is controlled by transmitting radar detection signals in a modulation manner, receiving multiple mixed signals, and processing them to generate multiple radar echo signals. The radar echo signals are subjected to basic preprocessing and clutter suppression to generate multiple effective radar signals. One-dimensional Fourier transform and two-dimensional Fourier transform are performed on multiple radar effective signals to obtain the target range and target velocity, respectively; Construct a distance-Doppler map, perform target detection, and obtain the target's location coordinates; By combining the target's position coordinates, target distance, and target velocity, a beamforming algorithm is used to estimate the target's azimuth and determine the target's azimuth angle.

2. The target azimuth estimation method for vehicle-mounted MIMO millimeter-wave radar according to claim 1, characterized in that, The control of the vehicle-mounted MIMO millimeter-wave radar, which employs modulation to transmit radar detection signals, receives multiple mixed signals, and processes them to generate multiple radar echo signals, specifically includes the following steps: The vehicle-mounted MIMO millimeter-wave radar is controlled by using linear frequency modulated continuous wave or orthogonal frequency division multiplexing to generate radar detection signals. Match multiple transmitting antennas corresponding to the radar detection signals; Plan the initial phase and / or frequency offsets corresponding to the multiple transmitting antennas; According to the multiple initial phases and / or multiple frequency offsets, control the multiple transmitting antennas to transmit corresponding radar detection signals; Multiple mixed signals are received through multiple receiving antennas; The multiple mixed signals are amplified, filtered, and / or converted from analog to digital to generate multiple radar echo signals.

3. The target azimuth estimation method for vehicle-mounted MIMO millimeter-wave radar according to claim 2, characterized in that, In the step of planning the initial phase and / or frequency offsets corresponding to the multiple transmitting antennas, the specific steps for planning the frequency offsets corresponding to the multiple transmitting antennas are as follows: The dynamic nonlinear index is obtained by generating a dynamic nonlinear index that adjusts dynamically with the current ambient temperature. Iterate through each transmitting antenna and perform an exponential operation on the transmitting antenna number according to the dynamic nonlinear exponent, and generate a nonlinear growth factor based on the result of the exponential operation. Based on the radar hardware architecture, the total bandwidth and the total number of transmitting antennas are obtained, and the ratio of the bandwidth allocated to each transmitting antenna to the total bandwidth is calculated to obtain the allocation ratio coefficient of each transmitting antenna. Based on the allocation ratio coefficient and nonlinear growth factor of each transmitting antenna, the frequency offset of each transmitting antenna is calculated, and the corresponding frequency offset value of each transmitting antenna is obtained.

4. The target azimuth estimation method for vehicle-mounted MIMO millimeter-wave radar according to claim 3, characterized in that, In the step of planning the initial phase and / or frequency offset corresponding to the plurality of transmitting antennas, the specific steps for planning the initial phase corresponding to the plurality of transmitting antennas are as follows: Determine the number of available polarization channels based on the radar hardware architecture; Based on the number of available polarization channels, a unique number is assigned to each polarization channel to obtain the polarization channel number; based on the polarization channel number, the horizontal / vertical polarization state is configured for the available polarization channels according to the orthogonality principle to obtain the polarization channel configuration table; The polarization channel number corresponding to each transmitting antenna is found according to the polarization channel configuration table. Based on the correlation between the transmitting antenna number and the parity of the polarization channel, a spatial-polarization two-dimensional alternating reversal mechanism is constructed to automatically generate phase polarity reversal between adjacent antennas or different polarization channels, thus obtaining the dynamic alternation factor. The nonlinear phase gradient of different transmit antenna numbers is calculated using cubic operations, and the enhancement weight of the corresponding polarization channel number of the transmit antenna is calculated using square operations. The nonlinear phase gradient and the corresponding enhancement weight are used to calculate the phase, and a corresponding dynamic alternation factor is introduced during the phase calculation process to form the phase calculation term. The reference phase is determined based on the system clock, and the phase calculation terms are superimposed on the reference phase to obtain the phase parameters. The phase parameters are normalized by phase period to obtain the initial phase parameter matrix of the antenna-polarization channel combination.

5. The target azimuth estimation method for vehicle-mounted MIMO millimeter-wave radar according to claim 1, characterized in that, The process of performing basic preprocessing and clutter suppression on multiple radar echo signals to generate multiple valid radar signals specifically includes the following steps: The radar echo signals are subjected to basic preprocessing to remove the DC component, thereby generating multiple preprocessed echo signals. Obtain the radar's operating frequency range and signal bandwidth; Based on the radar operating frequency range and the signal bandwidth, plan the filter; The filter is used to suppress noise and interference in the multiple preprocessed echo signals, generating multiple effective radar signals.

6. The target azimuth estimation method for vehicle-mounted MIMO millimeter-wave radar according to claim 5, characterized in that, The process of planning the filter based on the radar operating frequency range and the signal bandwidth specifically includes the following steps: Obtain the polarization channel number, calculate the sign factor based on the parity of the polarization channel number; and calculate the bandwidth expansion factor of the current channel based on the sign factor and the signal bandwidth. The center frequency is obtained based on the radar's operating frequency range, and the frequency variables are obtained based on the frequency point sequence that the filter design needs to cover. Based on the center frequency and frequency variables, calculate the frequency offset of each frequency point; construct a Gaussian function using the bandwidth expansion factor of the current channel, and calculate the Gaussian attenuation coefficient corresponding to each frequency point based on the frequency offset of each frequency point using the Gaussian function. The polarization state is obtained from the polarization channel configuration table. Based on the polarization state, the Gaussian attenuation coefficient is compensated for polarization direction, and the frequency domain response correction factor after polarization modulation is calculated. Based on the dynamic nonlinear exponent, the frequency domain response correction factor after polarization modulation is enhanced by exponentiation using power operation to obtain the nonlinear enhanced frequency domain response coefficient. The rectangular window is obtained by calculating the rectangular window function based on the frequency offset and signal bandwidth of each frequency point; The frequency domain response coefficients after nonlinear enhancement are multiplied point by point with the rectangular window to obtain the filter transfer function for all frequency points; The time-domain impulse response sequence is obtained by performing normalization and inverse Fourier transform on the filter transfer function at all frequencies. The number of valid points to be extracted is determined based on real-time computing resources, and the first few valid points are extracted from the time-domain impulse response sequence. Several effective points are stored as FIR filter coefficient matrices and grouped by polarization channel to obtain the time-domain filter coefficient set for each polarization channel.

7. The target azimuth estimation method for vehicle-mounted MIMO millimeter-wave radar according to claim 6, characterized in that, The step of performing one-dimensional Fourier transform and two-dimensional Fourier transform on multiple effective radar signals to obtain the target range and target velocity respectively includes the following steps: Perform a one-dimensional Fourier transform on the multiple effective radar signals; Peak detection in the frequency domain is performed to determine the target distance; Perform a two-dimensional Fourier transform on the signal; Peak detection is performed on the Doppler axis to determine the target velocity.

8. The target azimuth estimation method for vehicle-mounted MIMO millimeter-wave radar according to claim 7, characterized in that, The process of determining the target distance by performing peak detection in the frequency domain specifically includes the following steps: Extract the offset of the frequency point to be detected from the frequency domain data after one-dimensional Fourier transform; Obtain the current bandwidth expansion factor, and use the current bandwidth expansion factor to apply Gaussian function to exponentially decay the offset of the frequency point to be detected, and then use polarization state to correct it, to obtain the polarization-corrected Gaussian attenuation value. The resolution of each frequency point is calculated based on the number of frequency points in the frequency domain data after the one-dimensional Fourier transform and the signal bandwidth. Using the ratio of signal bandwidth to resolution as the denominator and the number of frequency points as the numerator, the ratio of the numerator to the denominator is calculated using the natural logarithm, and then adjusted using a dynamic nonlinear exponent to obtain the nonlinearly corrected logarithmic adjustment term. The signal amplitudes of all frequencies are obtained from the frequency domain data after one-dimensional Fourier transform, and the average power is calculated based on the signal amplitudes of all frequencies. The average power is then normalized to obtain the normalized signal average power. The polarization compensation term denominator is constructed based on the Gaussian attenuation value after polarization correction, and the ratio of the normalized signal average power to the polarization compensation term denominator is adjusted using the logarithmic adjustment term after nonlinear correction to obtain the adaptive detection threshold at the current frequency point. Obtain the signal power at the current frequency point and compare it with the adaptive detection threshold of the current frequency point; if it exceeds the adaptive detection threshold of the current frequency point, mark the current frequency point as a potential target and obtain a list of potential targets; Check if the frequency points in the potential target list have reached their peak values. If they have, block several adjacent frequency points in the potential target list to avoid repeated detection. Record all frequency points that exceed the threshold and replace all frequency points that exceed the threshold with distance values ​​to obtain the target distance.

9. The target azimuth estimation method for vehicle-mounted MIMO millimeter-wave radar according to claim 8, characterized in that, The process of constructing a distance-Doppler map, performing target detection, and obtaining the target's location coordinates includes the following steps: Construct a distance-Doppler plot; On the distance-Doppler map, the CFAR detection algorithm is used to detect the target and obtain the target position coordinates.

10. The target azimuth estimation method for vehicle-mounted MIMO millimeter-wave radar according to claim 9, characterized in that, The process of combining the target position coordinates, the target distance, and the target velocity, and using a beamforming algorithm to estimate the target azimuth and determine the target azimuth angle specifically includes the following steps: A virtual array is constructed based on the plurality of transmitting antennas and the plurality of receiving antennas; Calculate the weight vector by combining the target position coordinates, the target distance, and the target velocity; The weight vector is multiplied by the signal of the virtual array to obtain the beamforming output; Based on the beamforming output, power calculations and comparisons are performed in different directions to determine the target azimuth angle.

11. A vehicle-mounted MIMO millimeter-wave radar target azimuth estimation system, wherein the system is applied to the vehicle-mounted MIMO millimeter-wave radar target azimuth estimation method according to any one of claims 1 to 10, characterized in that, The system includes a radar detection and control unit, a signal clutter suppression unit, a Fourier transform processing unit, a position coordinate acquisition unit, and a target azimuth estimation unit, wherein: The radar detection control unit is used to control the vehicle-mounted MIMO millimeter-wave radar. It transmits radar detection signals using a modulation method, receives multiple mixed signals, and processes and generates multiple radar echo signals. The signal clutter suppression unit is used to perform basic signal preprocessing and clutter suppression on multiple radar echo signals to generate multiple effective radar signals. The Fourier transform processing unit is used to perform one-dimensional Fourier transform and two-dimensional Fourier transform on multiple radar effective signals to obtain target distance and target velocity, respectively. The location coordinate acquisition unit is used to construct a distance-Doppler map, perform target detection, and obtain the target's location coordinates; The target azimuth estimation unit is used to combine the target position coordinates, the target distance and the target velocity, and use a beamforming algorithm to estimate the target azimuth and determine the target azimuth angle.

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