Automotive radar using genetic notching sparse array processing to achieve direction of arrival estimation

Abstract

This disclosure relates to automotive radar using genetic apodization sparse array processing for direction-of-arrival estimation. A system and method for determining multiple MIMO virtual antenna arrays are provided. Each of the multiple MIMO virtual antenna arrays is associated with a subset of multiple transmit antennas and a subset of multiple receive antennas. Signals received by the multiple receive antennas are used to determine a signal spectrum for each of the multiple MIMO virtual antenna arrays. Each signal spectrum includes a signal amplitude value and has an associated angle value, wherein the main lobe position of the signal spectrum of the multiple MIMO virtual antenna arrays is at the same angle across all signal spectra, and the side lobe positions of the signal spectrum are not at the same angle across all signal spectra. A first combination of MIMO virtual antenna array signal spectra is determined by combining the minimum values ​​of the signal spectra at each angle value. The first combination of MIMO virtual antenna array signal spectra is used for object detection.

Description

Technical Field

[0001] This disclosure generally relates to signal processing in civilian vehicle radar systems, and more specifically, to an improved method for signal processing of multiple-input multiple-output (MIMO) virtual arrays. Background Technology

[0002] Radar systems, such as automotive radar systems used in civilian vehicle applications, transmit electromagnetic signals and receive back reflections of the transmitted signals. The time delay and / or time delay variation between the transmitted and received signals can be determined, and said time delay and / or time delay variation can be used to calculate the distance and / or speed of objects that caused the reflection (e.g., cars, trucks, motorcycles, traffic signs, road infrastructure, etc.). For example, in civilian vehicle applications, automotive radar systems can be used to determine the distance and / or speed of oncoming vehicles and other obstacles.

[0003] Automotive radar systems enable Advanced Driver Assistance Systems (ADAS) functions, which are expected to improve driving safety and ultimately drive the deployment of fully autonomous driving platforms. These systems use radar as the primary sensor for ADAS operation.

[0004] Unlike non-civilian radars that typically use physical antenna arrays for angle estimation, automotive radar systems often employ multiple-input multiple-output (MIMO) signal processing as a cost-saving measure to construct virtual antenna arrays using fewer physical antennas. This MIMO processing allows for the determination of the angle of arrival of objects, such as those near a vehicle. Despite trade-offs in MIMO signal processing, such as reduced signal-to-noise ratio (SNR), increased measurement errors and biases, increased measurement correlation, increased cross-channel interference, increased ambiguity, and higher processing load, MIMO remains a popular design for cost-sensitive systems like civilian automotive radars. Due to these degradations, MIMO methods are generally not used in performance-driven systems, such as those in non-civilian radars. In MIMO radar systems, as part of the signal processing, several virtual antenna arrays are determined to detect potential objects. While using such virtual arrays can improve the resolution of the radar system, conventional virtual array processing can mask reflected signals from smaller objects, potentially causing the radar system to miss some objects near the vehicle. This masking is further enhanced when the virtual array is designed to be sparse or non-uniform, which often produces additional ambiguous sidelobes that further mask objects that overlap with the sidelobes. Summary of the Invention

[0005] The content section of this invention is neither intended nor should be construed as representing the entire span and scope of this disclosure. Additional benefits, features, and embodiments of this disclosure are set forth in the accompanying drawings and the description below, and as described in the claims. Therefore, it should be understood that the content section may not encompass all aspects and embodiments claimed herein.

[0006] Furthermore, the disclosure herein is not intended to be restrictive or limiting in any way. Moreover, this disclosure is intended to provide those skilled in the art with an understanding of one or more representative embodiments supporting the claims. Therefore, it is important that the claims be considered within the scope of constructions having various features of this disclosure, provided that such constructions do not depart from the scope of the methods and apparatus consistent with this disclosure (including the originally filed claims). Furthermore, this disclosure is intended to cover and include obvious improvements and modifications thereof.

[0007] In some aspects, the technology described herein relates to a system comprising: a plurality of transmitter modules configured to transmit a plurality of transmitted radar signals using a plurality of transmit antennas; a plurality of receiver modules configured to receive reflections of the plurality of transmitted radar signals using a plurality of receive antennas; and a controller configured to: determine a plurality of MIMO virtual antenna arrays, wherein each of the plurality of MIMO virtual antenna arrays is associated with a subset of the plurality of transmit antennas and a subset of the plurality of receive antennas; determine a signal spectrum for each of the plurality of MIMO virtual antenna arrays using signals received by the plurality of receiver modules, wherein each signal spectrum includes a signal amplitude value and an associated angle, wherein the main lobe of each signal spectrum spans all signal spectra at the same angle, and the side lobes of the signal spectrum are located at angles other than those spanning all signal spectra; construct a first combined MIMO virtual antenna array signal spectrum by combining the minimum values ​​of the signal spectra at each angle value; and process the first combined MIMO virtual antenna array signal spectrum to detect an object.

[0008] In some aspects, the technology described herein relates to a system comprising: a plurality of transmitter modules configured to transmit a plurality of transmitted radar signals using a plurality of transmit antennas; a plurality of receiver modules configured to receive reflections of the plurality of transmitted radar signals using a plurality of receive antennas; and a controller configured to: determine a plurality of MIMO virtual antenna arrays, wherein each of the plurality of MIMO virtual antenna arrays is associated with a subset of the plurality of transmit antennas and a subset of the plurality of receive antennas; determine a signal spectrum for each of the plurality of MIMO virtual antenna arrays using signals received by the plurality of receiver modules, wherein each signal spectrum includes a signal amplitude value and has an associated angle value; construct a first combined MIMO virtual antenna array signal spectrum by combining the minimum values ​​of the signal spectra at each angle value; and process the first combined MIMO virtual antenna array signal spectrum to detect an object.

[0009] In some aspects, the techniques described herein relate to a method comprising: determining a plurality of MIMO virtual antenna arrays, wherein each of the plurality of MIMO virtual antenna arrays is associated with a subset of a plurality of transmit antennas and a subset of a plurality of receive antennas; determining a signal spectrum for each of the plurality of MIMO virtual antenna arrays using signals received by the plurality of receive antennas, wherein each signal spectrum includes a signal amplitude value and has an associated angle value, wherein the main lobe position of the signal spectrum of the plurality of MIMO virtual antenna arrays is at the same angle across all signal spectra, and the side lobe position of the signal spectrum is not at the same angle across all signal spectra; constructing a first combined MIMO virtual antenna array signal spectrum by combining the minimum values ​​of the signal spectra at each angle value; and processing the first combined MIMO virtual antenna array signal spectrum to detect an object. Attached Figure Description

[0010] The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification. The drawings illustrate embodiments of the invention and, together with the description, serve to explain the principles of the invention.

[0011] In the diagram:

[0012] Figure 1-1 illustrates an example radar system in which the radar signal processing procedures of this disclosure can be implemented.

[0013] Figure 1-2 graphically illustrates, at a high level, the processing steps that can be implemented by the vehicle radar system processor to process the received digital signals.

[0014] Figure 2-1 is a diagram depicting the spacing of the TX antenna elements.

[0015] Figure 2-2 is a diagram depicting the spacing of the RX antenna elements.

[0016] Figure 2-3 is a diagram depicting the spacing of the MIMO virtual antenna array.

[0017] Figure 2-4 is a graph depicting an example beamforming spectrum measured by a MIMO virtual antenna array with the spacing depicted in Figure 2-3.

[0018] Figure 3 This is a flowchart depicting a method for constructing a genetically apodized sparse array (HASA) virtual antenna array, according to the present disclosure.

[0019] Figure 4-1 is a diagram depicting the spacing of the TX antenna elements.

[0020] Figure 4-2 is a diagram depicting the spacing of the RX antenna elements.

[0021] Figure 4-3 is a diagram depicting the spacing of the HASA virtual antenna array.

[0022] Figure 4-4 is a graph depicting an example beamforming spectrum measured by a MIMO virtual antenna array with the spacing depicted in Figure 4-3.

[0023] Figures 4-5 to 4-7 depict example spectra of individual virtual antenna arrays of the HASA virtual antenna array.

[0024] Figure 5 This is a flowchart depicting a method for using a HASA virtual antenna array to process received radar signals to determine the angle and elevation of objects near the radar system.

[0025] Figure 6 This is a flowchart depicting a method for processing radar signals with reduced sidelobe amplitude.

[0026] Figures 7-1 to 7-6 are graphs depicting the experimental results of the HASA array signal processing method used in this invention. Detailed Implementation

[0027] This disclosure generally relates to signal processing in vehicle radar systems, and more specifically, to an improved method for signal processing of multiple-input multiple-output (MIMO) virtual arrays.

[0028] To improve the resolution of automotive radar systems, many such systems employ multiple-input multiple-output (MIMO) virtual array processing. Signals transmitted by different transmitter antennas are combined in various ways with signals received by different receiver antenna arrays to generate a large dataset of received signals for various virtual antenna combinations. This processing method produces a large dataset of received signals that can be processed to detect objects near the radar system, resulting in a higher resolution compared to other radar signal processing techniques.

[0029] However, while such virtual array processing methods improve the apparent resolution of vehicle radar systems (without increasing the number of physical antennas) through increased aperture and array thinning factor, they can also degrade the object dynamic range (DR) performance of the system. Therefore, the amplitude of the signal peak associated with a potential object relative to baseline noise and sidelobe levels decreases with increasing aperture and thinning factor, potentially leading to lost peaks and lost objects. Consequently, objects with weak reflective signals (e.g., smaller objects, or objects that absorb some radar signal energy or have a low radar cross-section) cannot be easily and definitively detected during such signal processing.

[0030] Therefore, this disclosure provides an automotive radar system configured to implement a MIMO virtual array signal processing method that can improve the DR performance of the radar system, wherein manageable computer resource requirements are suitable for use in an automotive radar system.

[0031] For illustration, Figure 1-1 depicts a simplified schematic block diagram of an automotive radar system 100 including a radar unit 10 connected to a radar controller processor 20. The radar system 100 may be implemented in the form of an integrated circuit, wherein the radar unit 10 and the radar controller processor 20 are formed as separate integrated circuits (chips) or a single chip, depending on the application.

[0032] Within the radar system 100, each radar unit 10 includes one or more transmitting antenna elements 102 and receiving antenna elements 104 respectively connected to one or more radio frequency (RF) transmitter (TX) units 11 and receiver (RX) units 12. For example, each radar unit 10 is shown as including individual antenna elements 102, 104 (e.g., TX1,i, RX1,j) respectively connected to three transmitter modules 11 and four receiver modules 12, but these numbers are not limiting, and other numbers are also possible, such as four transmitter modules 11 and six receiver modules 12, or a single transmitter module 11 and / or a single receiver module 12.

[0033] Each radar unit 10 also includes a chirp generator 112 configured and connected to supply a chirped input signal to the transmitter module 11. For this purpose, the chirp generator 112 is configured to receive separate and independent local oscillator (LO) signals and chirp start trigger signals. The operation of the transmitter module 11 can be controlled by a controller 110, which can be fully or partially implemented by the processor 20. A chirped signal 113 is generated and typically transmitted to the transmitter module 11 according to a predefined transmission schedule, wherein the chirped signal 113 is filtered at the RF conditioning module 114 and amplified at the power amplifier 115 before being fed to the corresponding transmission antenna 102 (TX1,i) for radiation.

[0034] Radar signals transmitted by transmitter antenna elements 102 (TX1,i, TX2,i) can be reflected by an object, and a portion of the reflected radar signal reaches receiver antenna elements 104 (RX1,i) at radar unit 10. At each receiver module 12, the received RF antenna signal is amplified by a low-noise amplifier (LNA) 120 and then fed to a mixer 121, where the received signal is mixed with a transmitted chirp signal generated by an RF conditioning module 114. The resulting intermediate frequency signal is fed to a first high-pass filter (HPF) 122. The resulting filtered signal is fed to a first variable gain amplifier 123, which amplifies the signal before feeding it to a first low-pass filter (LPF) 124. This re-filtered signal is fed to an analog-to-digital converter (ADC) 125 and output as a digital signal 126 (D1) by each receiver module 12. The receiver modules compress object signal echoes with various delays into multiple sinusoidal frequencies, the frequencies of which correspond to the round-trip delay of the echo signal.

[0035] The radar system 100 includes a radar controller processing unit 20 connected to (e.g., via controller 110) supply input control signals to the radar system 10 and receive digital output signals (e.g., digital signal 126) generated by receiver module 12.

[0036] In one or more embodiments, the radar controller processing unit 20 may be embodied as a microcontroller unit (MCU) or other processing unit configured and arranged for signal processing tasks, such as, but not limited to, object identification; calculation of object distance, object velocity, and object orientation; and generation of control signals. For example, the radar controller processing unit 20 may be configured to generate calibration signals, receive data signals, receive sensor signals, generate spectrum shaping signals (e.g., ramp generation in the case of frequency modulated continuous wave (FMCW) radar), and / or register programming or state machine signals for radio frequency (RF) circuit enable sequences. Additionally, the radar controller processor 20 can be configured to program the transmitter module 11 to operate according to a MIMO scheme, for example, in a time-division manner by sequentially transmitting chirps for coordinated communication between antenna elements 102 TX1,i, RX1,j; in a code-division manner by transmitting orthogonally phase-coded chirped sequences from the transmitting antenna elements; or in a Doppler-division manner by transmitting linear series phase-coded chirped sequences with a design offset in their Doppler spectrum from the transmitting antenna elements.

[0037] The radar controller processor 20 is configured to process the digital signal 126 to ultimately identify the distance to detected objects and the angular position and velocity of those objects relative to the radar system 100. These data points can be output as a point cloud, which identifies the distance to detected objects and the confidence level for each object detection in three-dimensional space. Typically, the digital signal 126 comprises a series of digital values ​​representing the amplitude of the radar signal acquired over time and received by the receiving antenna element 104. Typically, each digital value is associated with a specific number of chirps and a sample size.

[0038] Figure 1-1 illustrates a series of signal processing steps implemented by processor 20 to properly process the digital signal 126 received from radar unit 10 to identify potential nearby objects. To complement Figure 1-1, Figure 1-2 graphically illustrates, in a high-level manner, the processing steps that can be implemented by processor 20 to process the digital signal 126.

[0039] Specifically, the content of digital signal 126 consists of a series of data frames comprising (e.g., captured by ADC 125 of receiver unit 12) multiple digital sample values, wherein the sample values ​​are arranged in a two-dimensional matrix generated based on a pulse signal sequence. The data structure constituting a single captured frame is depicted by matrix 150 in Figures 1-2. As depicted, the single-frame data in matrix 150 comprises a two-dimensional matrix having a first dimension, referred to as the "fast time" dimension, and representing data values ​​captured from different pulse signals. A second dimension of matrix 150 is referred to as the "slow time" dimension and represents data values ​​captured in response to different chirped signals, which may be included within a specific pulse signal transmitted by transmitter module 11. As shown in Figures 1-2, signal processing may involve processing multiple data frames represented by several matrices 150. In this disclosure, this may involve processing individual data frames, as described herein, to identify a set of peaks within the frame. Alternatively, and as described herein, this may involve processing different sub-segments of the ADC data stream to identify a set of candidate peaks that ultimately span the entire radar cube combination to perform final peak detection. Typically, during this signal processing, the data frames represented by matrix 150 are captured for each receive channel. Thus, Figures 1-2 depict multiple matrices 150, each associated with a different receive channel and received as input data for the signal processing chain.

[0040] For a sub-segment of radar cube data that may include data represented by matrix 150, radar controller processor 20 initially performs a fast time-range fast Fourier transform (FFT) 21 (Figure 1-1) to generate new frame data represented by matrix 152. FFT 21 is performed on the 1D data array (i.e., signal) associated with each different chirp in the original input matrix 150 to generate 1D transformed signals of the same length. The FFTs of each chirp in the original input frame represented by matrix 150 are combined to generate a transformed frame as indicated by matrix 152. This process is repeated for each frame associated with each receive channel. The resulting data frame, representing the range map, is represented as matrix 152 in Figure 1-2 and can be used to determine the distance to a specific object, as reflected in the range map.

[0041] In the next step, the radar controller processor 20 performs an additional Fast Fourier Transform (FFT) 22 (Figure 1-1) (referred to as slow-time or Doppler FFT) on the range map to generate new range-Doppler frame data represented by matrix 154. However, in this step, FFT 22 is applied along the opposite dimension to FFT 21. Thus, FFT 22 is performed on the 1D data array (i.e., signals) associated with each range segment in matrix 152 to generate 1D transformed signals of the same length. The FFTs of each signal in the frames of matrix 152 are combined to generate range-Doppler data frames as indicated by matrix 154. This process is repeated for each frame associated with each receive channel. The range-Doppler data frames associated with matrix 154 provide information about a potential object moving from one sample number to the next over time. Using the data frame associated with the resulting matrix 154, the data encoded therein can be processed to begin identifying potential objects, and in the case of detected objects, to determine their velocity and direction of arrival.

[0042] Therefore, the radar controller processor 20 performs constant false alarm rate (CFAR) object detection 23 (Figure 1-1) and 156 (Figure 1-2).

[0043] If a potential object is detected, the radar controller processor 20 executes multiple-input multiple-output (MIMO) array measurement constructs 24 (Figure 1-1) and 158 (Figure 1-2) to determine the direction of arrival (DOA) for each object at steps 25 (Figure 1-1) and 160 (Figure 1-2). A final object information dataset, which may include the object identifier DOA and other relevant information (e.g., object speed), is then passed by the radar controller processor 20 to the ADAS or other systems configured to utilize the object information to control one or more vehicle systems.

[0044] High-resolution imaging automotive radar systems are crucial for the safe and efficient operation of autonomous driving (AD) and higher-level advanced driver assistance systems (ADAS). In these systems, the signal processing pipeline is configured to utilize a sparse array design, which effectively achieves relatively high angular resolution compared to other signal processing methods without requiring excessive computational resources that may not be available in automotive systems.

[0045] However, in these sparse signal methods, a certain amount of object detection ambiguity is unavoidable due to array thinning effects or spatial undersampling, and therefore, higher signal sidelobes may exist in the beamforming spectral output.

[0046] To illustrate this, Figures 2-1 through 2-4 include several graphs depicting example TX element spacing (Figure 2-1) and RX element spacing (Figure 2-2) of a MIMO radar system. Figure 2-3 depicts the resulting MIMO virtual antenna array spacing, and Figure 2-4 shows the resulting beamforming spectrum.

[0047] In Figure 2-1, the horizontal axis represents the real-world location of the different TX antenna elements 202 in the TX antenna array (e.g., one or more transmission antenna elements 102 of Figure 1-1). Typically, the antennas are spaced apart from each other non-uniformly by a multiple of a unit value, which may be, for example, equal to half or greater than the wavelength of the signal received by the radar system.

[0048] Similarly, in Figure 2-2, the horizontal axis represents the real-world locations of the different RX antenna elements 204 in the RX antenna array (e.g., one or more receive antenna elements 104 of Figure 1-1). In some embodiments, the spacing of the RX antenna elements 204 may be similar to the spacing of the TX antenna elements 202.

[0049] Each of the different TX antenna elements 202 and different RX antenna elements 204 is configured to transmit and receive radar signals respectively. The transmitted signals are encoded or can be separated from each other (e.g., by time, frequency, or code) such that a signal received by a particular RX antenna element 204 can be associated with a particular TX antenna element 202 associated with said signal.

[0050] Through MIMO processing (e.g., performed by the MIMO array measurement configuration 24 of FIG1-1), various TX antenna elements 202 and different RX antenna elements 204 are paired together in different combinations to form a MIMO virtual array as shown in FIG2-3. Specifically, in FIG2-3, the horizontal axis represents the virtual positions of the different virtual antenna elements 206 that constitute the entire MIMO virtual array.

[0051] Signals associated with the various TX antenna elements 202 and RX antenna elements 204 of the virtual array are processed via MIMO to form a beamforming spectrum for a set of angular positions. This combined beamforming spectrum can then be processed to identify peaks in the spectrum that can be associated with a detected object at a specific angular position. An example beamforming spectrum is shown in Figures 2-4 via trace 208. As shown by example trace 208, this conventional processing method produces a center peak 210 that can be associated with a detected object. However, the spectrum includes prominent sidelobes 212, which can interfere with the detection of true signal peaks in the spectrum under various conditions and thus interfere with accurate object detection.

[0052] To provide an improved MIMO virtual array beamforming spectrum with reduced sidelobe interference, this disclosure provides an improved method for MIMO virtual antenna array construction and signal processing, referred herein as the Genetic Apodized Sparse Array (HASA) signal processing method. The HASA method of this invention comprises two components: virtual antenna array construction, which is completed during the antenna array design phase; and array processing, which is completed by the radar system at runtime.

[0053] During the design phase of the radar system's virtual antenna array, TX and RX antenna arrays (e.g., one or more transmission antenna elements 102 and antenna element 104 of Figure 1-1) are used to form multiple linear sparse virtual arrays in several configurations. These configurations are selected such that various combinations of array gain modes produce dispersed sidelobe angular positions, ensuring that sidelobes in signals captured by different array combinations do not appear at the same angular position across different antenna combinations. Therefore, when signals are combined, sidelobes do not constructively interfere with each other, effectively suppressing those sidelobes by detecting inconsistencies across different signals, while simultaneously maintaining the detected main lobe or object peak. This dispersion of sidelobe positions can be utilized by mini-pooling (i.e., by constructing a new angular signal or spectrum that includes the lowest values ​​in all available spectra) to effectively remove or cancel sidelobe energy in the processed output spectrum. It should be noted that, without loss of generality, in this disclosure, the mini-pooling operation generally includes operations such as ordered statistics and parameterized smoothing minifunctions.

[0054] Once the optimized virtual antenna array (also known as a HASA virtual antenna array) is constructed, sparse array signal measurements (also known as beam vectors (BV)) of the sparse HASA virtual antenna array are captured. In the first method, the sparse array is zero-padded and processed via a Fast Fourier Transform (FFT) to obtain a beamforming spectrum for each HASA virtual array combination. A min-pooling operation is then applied across each of the beamforming spectra to produce a new spectrum with reduced sidelobe amplitude. Due to the complementary design of the HASA virtual antenna array (described below), the min-pooling operation suppresses sidelobes in the resulting processed signal while largely preserving the true object peaks, effectively increasing the dynamic range of the processed spectrum. In the case of sidelobe energy in the processed spectral data, any such sidelobe energy is reduced by the min-pooling operation compared to sidelobes present in any of the individual linear sparse arrays. It should be noted that the min-pooling operation is typically configured to suppress sidelobes that do not correspond to any true target. Therefore, instead of using the maximum peak value of the spectrum to determine the strongest target using minimum pooling, the strongest target is determined by max-pooling the maximum peak value of the spectrum. This optional process is used to avoid potential destructive interference between targets with the same azimuth but different elevation angles. Except for the maximum peak value, all other spectra are determined using minimum pooling.

[0055] In another optional processing step, sidelobes in the spectrum of individual linear sparse arrays are further suppressed by recursively recovering the next highest amplitude peak by successively eliminating the highest amplitude signal peak in each linear sparse array. In each iteration of the method, the highest amplitude peak is identified from the interpolated maximum peak (or optionally, the maximum peak of the max-pooled spectrum) of the minimum-pooled spectrum. The recovered peak and its coherently reconstructed signal can then be used to backfill missing elements in the sparse array. Thus, complete array measurements and its beamforming spectrum can be obtained. The resulting beamforming spectrum is further minimum-pooled to obtain the final output.

[0056] Using the output, normal radar signal processing can be restored. Specifically, a vertical array can be constructed by extracting array values ​​from multiple processed individual sparse arrays appearing at each angular location, where peaks have been identified across various spectra, as described below. Each peak is associated with a potentially detected object, and the elevation angle of each object is then estimated from each vertical array. The elevation angle can be determined using any suitable angle estimation method. Once processing is complete, the final output of the signal processing method includes an object point cloud (e.g., in the range-Doppler-azimuth-elevation-amplitude dimension), which can then be provided to perception, drive control, and other automotive subsystems or ADAS (e.g., via data interface 26 of Figure 1-1).

[0057] When constructing the HASA virtual antenna array of this invention, complementary MIMO array geometries that produce high dynamic range (HDR) performance are selected. This can be achieved by utilizing inconsistent angular sidelobe positions (e.g., where positions are indicated by angular locations) across multiple virtual array combinations relative to the main lobe position. In other words, across various virtual arrays configured differently, the real-object peak will appear at the same angular location across the entire spectrum, but the sidelobes will not. Because the sidelobes will appear at different angular locations for each of the different virtual arrays, this inconsistency in sidelobe positions can be utilized to enable the reduction of sidelobe levels in the final spectrum via minimum pooling operations.

[0058] Conventional sparse array designs consist of a dataset using multiple rows of virtual antenna arrays with identical or highly similar geometries, which produce identical or highly similar angular spectral measurements. While these geometries are typically optimized to suppress sidelobes while maintaining a minimum aperture to support the desired resolution (as defined by the -3 dB to -3 dB main lobe beamwidth), it is important to note that these geometries are identical or highly similar for each row in the resulting dataset and therefore for each virtual antenna array. Consequently, sidelobe interference often exists at the same angular locations within the spectrum captured by the various virtual arrays, making it difficult to detect the true peaks across the captured signal.

[0059] Compared to the methods described above, in the HASA method of the present invention for MIMO virtual antenna array signal processing, each individual virtual antenna array associated with a row of sparse array datasets in the HASA virtual antenna array is constructed using a different virtual antenna array geometry. These different array geometries are optimized such that their angular spectra produce the output spectrum after applying minimum pooling, wherein the sidelobe energy is minimized by distributing the sidelobe energy over a large angular range. The geometry of the virtual antenna can be optimized in a continuous and iterative manner for efficient searching.

[0060] To illustrate, Figure 3 This is a flowchart depicting a method 300 for constructing a HASA virtual antenna array according to this disclosure. Method 300 can be implemented offline during the design phase of developing a radar system. A key objective of method 300 is to have a combination of virtual array angular spectra that are inherently complementary, meaning that the sidelobe locations are not perfectly consistent, and therefore it allows for simple operations, such as min-pooling (i.e., selecting the lowest value of a set of spectra at a specific angle), to utilize its inconsistent nature to filter out or otherwise cancel out ambiguous sidelobes. On the other hand, because the main lobe is inherently consistent (i.e., located at the same angular location), the main lobe (i.e., the real object signal) is not filtered out by min-pooling operations.

[0061] Combination Figure 3 Method 300, Figures 2-1, 2-2, 2-3, and 2-4 include graphs depicting the TX element spacing (Figure 2-1), RX element spacing (Figure 2-2), resulting MIMO virtual antenna array spacing (Figure 2-3), and the resulting beamforming spectrum after minimum pooling (Figure 2-4) for example embodiments. In the examples presented in Figures 2-1 to 2-4, data for a 16T16R (16 transmitters and 16 receivers) vehicle radar system is shown, which consists of three TX antenna subarrays of sizes 6, 5, and 5, and three RX antenna subarrays of sizes 6, 5, and 5. It should be understood that this method can be used in other radar systems with any number of TX and RX antennas. As depicted in Figure 2-4 and described below, the resulting minimum-pooled beamforming spectrum can provide a sidelobe signal amplitude better than -20 dB compared to approximately -12 dB of the main lobe signal amplitude in the spectrum of individual arrays.

[0062] Return to view Figure 3 At box 302, a set of transmit and receive subarray sizes is initially determined for a given application. This set of subarray sizes determines the multiple transmit antenna subarrays Nt and multiple receive antenna subarrays Nr to be constructed, along with their corresponding sizes, for the N virtual antenna arrays. Once the transmit and receive antenna subarrays are constructed, they are combined as described herein to form the N virtual antenna arrays of the HASA virtual antenna array.

[0063] Typically, the size specification for each antenna subarray includes the number of transmit antennas and receiver antennas in each virtual antenna array combination. In many cases, the number and size of transmit and receive antenna subarrays are determined by the desired aperture size and thinning factor. Given a desired aperture size, a larger thinning factor can reduce cost at the expense of poorer dynamic range performance and a larger number of subarrays, while a smaller thinning factor results in less cost savings but better dynamic range performance and fewer subarrays. A larger number of subarrays allows for the construction of more horizontally linear arrays, resulting in more robust elevation performance.

[0064] Using the subarray size determined at box 302, a set of antenna subarrays is determined in a set of initial steps to meet the requirements of the subarray size determined at box 302. Once a set of transmit and receive antenna subarrays has been initially determined, an iterative process is performed to further optimize the determined subarray configuration.

[0065] Therefore, at block 304, a first transmission antenna subarray (e.g., different combinations of one or more transmission antenna elements 102 of FIG. 1-1) is determined by determining a first configuration of the transmission antenna elements based on the subarray size constraints of the first virtual transmission array determined at block 302, such that a set of sidelobes in the subarray's array factor, far-field radiation pattern, or directional gain pattern has minimum signal energy. For a particular antenna subarray, the sidelobe amplitude can be determined based on the subarray's array factor pattern, which is a set of calculated values ​​generated under the assumption that there exists an infinitely long distance of signal incident on the antenna subarray from different angles, and that each value is the sum of the complex signal values ​​received by all receiving antennas in the array for each angle. The antenna array factor can be calculated using a zero-padding / filling FFT of 1 and 0 vectors, where 1 corresponds to an antenna and 0 corresponds to a missing location, assumed to be on a uniform grid. Therefore, by performing this calculation, a first configuration of the first transmission antenna subarray elements that produces a set of sidelobes with minimum signal energy can be determined. Referring to Figure 4-1, for example, the diagram illustrates an example of a first transmit antenna subarray at antenna array index 1, which comprises a set of transmit antennas (indicated by point 402), each located at a different x-index position. The specific combination of transmit antenna elements and their corresponding positions are represented in... Figure 3 The first transmission antenna subarray is defined at box 304.

[0066] Similarly, at box 306, a first receiving antenna subarray (e.g., different combinations of one or more receiving antenna elements 104 in Figure 1-1) is determined by defining a first configuration of the receiving antenna elements (based on the subarray size constraints determined at box 302), which minimizes the signal energy of a set of sidelobes in its array factor. Referring to Figure 2-2, for example, a graph illustrates a set of dummy receiving antennas at antenna array index 1 (reflected by point 404), each dummy receiving antenna located at a different x-index position. The specific combination of receiving antenna elements and their corresponding positions can be represented as... Figure 3 The first receiving antenna subarray is defined at box 306.

[0067] Upon completion of block 306, the first transmit and receive antenna subarrays have been determined, which can then be combined into the first virtual antenna array of the HASA virtual antenna array.

[0068] At block 308, a second transmit antenna subarray (e.g., different combinations of one or more transmit antenna elements 102 of FIG. 1-1) is determined by a second configuration of the transmitter antenna elements (based on the subarray size constraints determined at block 302), such that a set of sidelobes in its array factor has minimum signal energy. Since a first complete HASA virtual antenna array has already been generated at the completion of block 306, the second transmit antenna subarray can be further optimized using the minimum-pooled spectrum or array factor sampled for the second transmit antenna subarray and the first receive subarray. Therefore, at this initial stage, this may involve combining the second transmit antenna subarray with the first receive antenna subarray, and optimizing the second transmit antenna subarray by determining the configuration of the second transmit antenna subarray with the minimum amplitude sidelobes in the minimum-pooled angular spectrum or array factor that produces the resulting virtual antenna array.

[0069] Referring to Figure 2-1, for example, the diagram shows a group of transmission antennas at antenna array index 2 (reflected by point 406), each transmission antenna located at a different x-index position. The depicted combination of transmission antenna elements and their corresponding positions can be represented in... Figure 3 The second transmission antenna subarray is defined at box 308.

[0070] Similar to block 308, at block 310, a second receiving antenna subarray (e.g., different combinations of one or more receiving antenna elements 104 of FIG. 1-1) is determined by determining a second configuration of the receiving antenna elements (based on the subarray size constraints determined at block 302), which minimizes the signal energy of a set of sidelobes in its array factor. Since two transmitting antenna subarrays have already been determined at the completion of block 308 (i.e., at blocks 304 and 308), the second receiving antenna subarray can be further optimized by determining the subarray configuration based on the calculated minimum-pooled spectrum or array factor for the second receiving antenna subarray. At this stage, this may involve iteratively combining the second receiving antenna subarray with each of the first transmitting antenna subarray (block 304) and the second transmitting antenna subarray (block 308), and then optimizing the second receiving antenna subarray by determining the configuration of the second transmitting antenna subarray with the minimum amplitude sidelobes in the minimum-pooled angular spectrum or array factor that produces the resulting virtual antenna array.

[0071] Referring to Figure 2-2, for example, the diagram shows a group of receiving antennas at antenna array index 2 (reflected by point 408), each receiving antenna located at a different x-index position. The depicted combination of receiving antenna elements and their corresponding positions can be represented in... Figure 3 The second receiving antenna subarray is defined at box 310.

[0072] As reflected in boxes 312 and 314, this process of iteratively determining the optimized (min-pooled) set of transmit and receive antenna subarrays continues until all N arrays are determined, where N = Nt*Nr, where Nt is the number of transmit subarrays and Nr is the number of receive subarrays, and N is the number of virtual receive antennas (or simply virtual antennas), as described in the subarray size constraints determined at box 302.

[0073] Each new transmit antenna subarray is optimized by combining the transmit antenna subarray with each previously determined receive antenna subarray and then optimizing the transmit antenna subarray by determining the configuration of the transmit antenna subarray that produces the minimum amplitude sidelobe in the minimum pooled angular spectrum or array factor of the resulting virtual antenna array. Similarly, each new receive antenna subarray is optimized by combining the receive antenna subarray with each previously determined transmit antenna subarray and then optimizing the receive antenna subarray by determining the configuration of the receive antenna subarray that produces the minimum amplitude sidelobe in the minimum pooled angular spectrum or array factor of the resulting virtual antenna array.

[0074] At the end of this process, the complete set of transmit and receive antenna subarrays has been determined based on the number and size of the subarrays identified at box 302. Although the complete set of transmit and receive antenna subarrays has been optimized at least partially at this point (making it possible to create a complete set N of virtual antenna arrays), method 300 envisions an iterative process in which the complete sets of transmit and receive antenna subarrays are each sequentially optimized to further improve the overall configuration of the transmit and receive antenna subarrays and thereby improve the resulting HASA virtual antenna array.

[0075] Therefore, at box 316, it is determined whether method 300 has already run for the threshold number of iterations. If so, method 300 terminates at box 318.

[0076] If not, method 300 moves to block 320, where the transmit antenna subarray configuration is optimized again, this time given the complete set of receive antenna subarrays already generated. Specifically, each transmit antenna subarray is optimized by combining each transmit antenna subarray with each previously determined receive antenna subarray and then by determining the configuration of the transmit antenna subarray that produces the minimum-pooled angular spectrum or minimum amplitude sidelobe in the array factor of the resulting virtual antenna array. This process can be performed sequentially for each transmit antenna subarray.

[0077] At box 322, the receive antenna subarray configuration is optimized again, this time considering the complete set of previously generated transmit antenna subarrays. Specifically, each receive antenna subarray is optimized by combining each receive antenna subarray with each previously determined transmit antenna subarray and then optimizing the configuration of the receive antenna subarray that produces the minimum amplitude sidelobes in the minimum pooled angular spectrum or array factor of the resulting virtual antenna array. This process can be performed sequentially for each receive antenna subarray.

[0078] It should be noted that during this iterative process, the individual transmit and receive antenna subarrays do not need to be optimized in any particular order. The transmit antenna subarrays can be optimized sequentially before the receive antenna subarrays. Alternatively, the method can switch between optimizing the transmit and receive antenna subarrays. In one or more embodiments, the iterative process of blocks 320 and 322 can actually be further optimized by alternatively optimizing the individual transmit and receive antenna subarrays in a random or pseudo-random order.

[0079] This iterative process is often referred to as a "greedy" algorithm, as opposed to global exhaustive search. Greedy algorithms provide an efficient way to search for a "good enough" solution, but may not guarantee that the algorithm will reach a globally ideal solution.

[0080] Once the threshold number of iterations is reached at box 316, the method exits by outputting an optimized HASA array comprising a set of virtual antenna arrays representing each possible combination of optimized transmit and receive antenna subarrays. An example of such an array is depicted in Figure 2-3, where each row in the graph represents a different combination of optimized transmit and receive antenna subarrays within a MIMO virtual antenna array. The horizontal spacing along the x-axis in each virtual antenna array represents the horizontal position of the antennas in each virtual array. Because in the specific examples of Figures 4-1 to 4-4, a set of optimized transmit antenna subarrays comprises 3 subarrays and a set of optimized receive antenna arrays comprises 3 subarrays, the resulting combined HASA virtual antenna array, as shown in Figure 4-3, comprises 9 (i.e., 3 by 3) complete virtual antenna arrays. Therefore, each virtual transmit subarray generated by method 300 has been combined with each virtual receive subarray generated by method 300 to create the HASA virtual antenna array.

[0081] The HASA virtual antenna array generated by method 300 is optimized by the optimization of each individual transmit and receive antenna subarray selected to reduce the amplitude of individual sidelobes, thereby also reducing the amplitude of sidelobes in the signal spectrum captured by the virtual array.

[0082] For illustration, Figures 4-5 through 4-7 depict example spectra that can be generated by individual virtual antenna arrays in a set of HASA virtual antenna arrays determined by method 300. In each spectrum, the horizontal axis represents the angular position, while the vertical axis represents the signal amplitude. As illustrated, in each spectrum, the main lobe 450 (which represents the actual detected object signal) is located at the same angular position. However, due to the optimization process described above, the side lobes 452 of each spectrum are located at different angular positions. Therefore, when these spectra are combined together using a min-pooling operation to produce an output spectrum, the peak of the side lobe 452 is effectively canceled out, but the peak of the main lobe 450 is preserved. The output spectrum comprises the lowest amplitude across all spectra generated by the virtual antenna arrays at each angular position (e.g., the spectrum in Figure 4-4). This min-pooled combined spectrum represents the output of the HASA MIMO virtual antenna array and can be processed (e.g., via box 25 in Figure 1-1) to perform angle of arrival estimation for the object associated with the peak of the main lobe 450.

[0083] To illustrate the minimum-pooled output generated by the virtual antenna array depicted in Figure 4-2, Figure 4-4 is a graph depicting the spectrum (trace 409) of an example signal generated by the HASA virtual antenna array shown in Figure 2-3 and generated according to method 300. As depicted by trace 409, this processing method produces a center peak 410 (also called the main lobe), which represents the real signal and is associated with the detected object. However, for example, the sidelobes 412 are now significantly reduced compared to the sidelobes 212 in Figure 2-4 (e.g., compared to the sidelobes in Figure 2-4). This results in a significant reduction in sidelobe interference and can increase the likelihood of detecting real objects by the radar system.

[0084] Therefore, when using the HASA virtual antenna array generated by method 300 to capture the spectrum depicted in Figures 2-4, a minimum pooling operation (also known as an apodization process) can be used to combine the signals captured by each virtual antenna array in the HASA virtual antenna array in a manner that reduces sidelobe amplitude. Specifically, the operation takes the amplitude spectrum from all the receiving antenna arrays in the HASA virtual antenna array as input and outputs the minimum amplitude across all captured spectra at each angle. Thus, as long as at least one of those spectra does not have high sidelobes at a particular angular location (which is likely due to the optimization process used to generate the virtual antenna array according to method 300), the negative impact of sidelobes (e.g., excessive noise) appearing only in the subset of spectra at a particular angle will be canceled out or otherwise significantly reduced in the minimum pooling output. Therefore, the optimization process, including the HASA virtual array construction and minimum pooling operation of method 300, can be described as an iterative process of identifying complementary array geometries (i.e., transmit and receive antenna array configurations) that generate non-overlapping sidelobes at as many angular locations as possible, and then performing minimum pooling on the captured spectrum at each angle to effectively remove sidelobe interference. By utilizing the diversity of virtual antenna array geometries in this way, the HASA virtual antenna array configuration generated by method 300 may achieve lower sidelobes compared to any individual sparse array.

[0085] Using the HASA virtual antenna array determined by method 300 Figure 5 A method 500 is described for using a HASA virtual antenna array to process received radar signals to identify and determine the angle of an object near the radar system. For example, method 500 can be implemented by the controller 110 of Figure 1-1.

[0086] As input to method 500, a HASA virtual antenna array (e.g., generated by method 300) is used to process radar signals received by a vehicle radar system (e.g., radar system 100 of Figure 1-1) into a MIMO measurement array. At block 502, the measurement array is processed by performing a zero-padded / filled FFT on the HASA BVs to generate a set of angular spectra, which may also be referred to as a set of spectra or HASA beam vectors (BVs). At block 504, a minimum pooling analysis is performed across those spectra to produce an output spectrum with reduced sidelobe peaks by reducing the amplitude of sidelobe peaks that exist only at specific angular locations in a small number of processed spectra. As described above, the minimum pooling method involves selecting the minimum value from all HASA BVs at each angular location in the set of HASA BVs to use as the signal amplitude of the processed spectrum at that angular location.

[0087] Due to the way the HASA virtual array is constructed (i.e., according to method 300 and as described above), the true signal peaks (i.e., the peaks that should be maintained) will exist across the entire spectrum, and therefore, the min-pooling operation will ensure that the true signal peaks (i.e., the main lobe) are retained in the final output array. However, because the peaks associated with the sidelobe signals will be offset from each other in each individual spectrum (because the virtual antenna array associated with each HASA BV has a different geometry), the amplitudes of those sidelobe-related peaks will be minimized by the min-pooling process to essentially remove them from the processed spectrum. That is, if a sidelobe signal exists at a specific angular location within a subset of the HASA BV, it is highly likely that in at least one of the other HASA BVs, the sidelobe peak will not exist at that angular location, and the min-pooling process will ignore the peak and instead use a lower signal level (i.e., no sidelobe peak) as the signal amplitude at that angular location in the output data. Thus, the sidelobe signals are filtered out, resulting in a significant reduction in the sidelobe peak amplitudes.

[0088] Normal signal processing can be performed using the spectrum processed at block 504 and the removed sidelobe signal. Therefore, at block 506, peak detection is performed on the resulting spectrum (e.g., via CFAR block 23 in Figure 1-1) to determine the angles associated with the remaining peaks, and thereby determine the possible DOA of the object detected by the radar system. At block 508, a Discrete Fourier Transform (DFT) is performed on the vertical data array, and at block 510, the processed vertical array is used to determine the elevation angle of the detected object. At block 512, the elevation angle and DOA are improved relative to each other, and the resulting angle information (and range data) is used to generate a point cloud dataset as output at block 514, which specifies the distance, DOA, and elevation angle of the detected object. The point cloud can then be passed to ADAS, for example, enabling driver guidance and feedback based on the position of the detected object in the geometry surrounding the vehicle.

[0089] In radar systems, as the number of detected objects increases, the need for reduced sidelobe signal amplitude also increases. Empirically, for each additional detected object, the original absolute sidelobe level needs to be reduced by 6 dB to obtain the worst-case DR (i.e., if the maximum sidelobe of one object is -20 dB, then the worst-case DR for two objects should be estimated as 12-4 dB, and the worst-case DR for three objects should be estimated as 8 dB, assuming a detection margin of 6 dB; and conversely, if the maximum sidelobe of one object is -12-2-2, then the worst-case DR for two objects will be 6 dB, and the DR for three objects will be 0 dB, resulting in insufficient DR).

[0090] Therefore, as more and more objects need to be detected with the same DR performance, or when increasingly weaker objects (i.e., those that produce weaker radar signal reflections) need to be detected (i.e., with higher DR), reducing the maximum sidelobe signal amplitude may be necessary. In this case, without increasing the number of virtual arrays and elements, additional processing steps can be used to increase the overall system DR, thereby increasing the likelihood that the radar system will detect those additional objects.

[0091] To illustrate, Figure 6 This is a flowchart depicting a method 600 for processing radar signals with reduced sidelobe signal amplitude to improve the likelihood of detecting multiple objects.

[0092] As input to method 600, the HASA virtual antenna array (e.g., according to the above) Figure 3Method 300 is used to process the received radar signal to generate a set of spectra (i.e., beam vectors (BV)) at box 601. Missing elements in the BV are zero-padded, and at box 602, an FFT is applied to the BV to generate a set of angular spectra. The missing elements resulting from the individual virtual antenna arrays in the HASA virtual antenna array are sparse arrays because they only include a subset of the available antennas. These missing elements increase the inaccuracy of the final radar system object detection and angle of arrival estimation processes. This method provides a way to backfill those missing elements to improve system accuracy.

[0093] Therefore, at block 604, minimum pooling is performed on the spectrum or BV determined at block 602 as described above to produce a HASA spectrum with reduced sidelobe levels. After processing via the minimum pooling operation, at block 606, the dominant peak present in the processed HASA spectrum (which is likely associated with the first detected object) is processed to determine the peak location (i.e., angular location) using peak interpolation. Using the identified peak locations, the individual spectra captured by each virtual antenna array in the HASA virtual antenna array are processed again (e.g., at block 602) to determine amplitude and phase parameters for the spectrum of each virtual array at the peak location. Using these estimated parameters, at block 608, the peak signal is coherently reconstructed individually for each virtual array in the HASA virtual antenna array, including both missing and absent virtual array elements, to reconstruct the signal for each virtual array object. This reconstruction process can follow the Inverse Discrete Fourier Transform (IFT), which converts a domain signal containing the spectrum of peaks defined by their interpolated peak frequency locations and associated amplitude and phase values ​​into a spatial domain signal. This spatial domain signal is a discrete sample of the signal from the padded array, including both missing and unmissing antenna elements. Using the reconstructed and padded (i.e., no missing elements) array signal generated at block 608, the method performs two separate operations, which can be performed entirely or partially in parallel.

[0094] In the first method, at block 610, for each virtual antenna array, the reconstructed object signal of the missing elements of the HASA virtual antenna array is coherently accumulated in a buffer (e.g., in a memory accessible to the controller 110 of Figure 1-1). This accumulated data can later be used to perform a backfill operation, as described below.

[0095] In the parallel second process, at block 612, the reconstructed object signal of the non-missing element is coherently canceled or subtracted from the BV of each virtual array used in the HASA virtual antenna array. Here, coherent cancellation may mean suppressing the complex-valued sinusoidal signal, which is done by first estimating its frequency, amplitude, and phase parameters, then generating another complex-valued sinusoidal signal with the estimated frequency, amplitude, and phase, and thirdly subtracting the original signal from the generated signal. If the estimation is accurate, the original complex sinusoidal signal is said to be coherently canceled or subtracted. The spectrum or BV of these object-moved or coherently canceled object signals can then be used for the next iteration of the process, and the signal processing in blocks 602, 604, 606, and 608 is repeated to continuously improve the reconstructed signal using the accumulated missing element signals until the stopping criterion is met as determined in block 614. Essentially, this process (shown by blocks 602, 604, 606, 608, 612, and 614) represents an iterative process in which object signals associated with clear objects or main lobe peaks in the HASA spectrum are iteratively removed from the HASA spectrum, thereby allowing the detection and processing of additional lower amplitude peaks (i.e., representing other objects near the radar system). As part of this processing, the reconstructed signal provides estimated values ​​for missing elements in the original HASA BV (e.g., generated at block 601).

[0096] The stopping criteria in box 614 typically include (as an example) a maximum number of iterations threshold, a maximum DR threshold for stopping iterations once the desired DR is reached, and / or a minimum SNR threshold for stopping iterations once no significant object is found relative to the noise and sidelobe base.

[0097] When the stop criterion of block 614 is reached, at block 616, the accumulated reconstructed missing element signals (i.e., stored when block 610 is executed) are used to backfill the missing elements from the original spectrum or BV (e.g., initially generated at block 601). The backfilled spectra can then be processed according to this disclosure (e.g., via min-pooling) to determine the angle of arrival for the detected object. Therefore, at block 618, the backfilled BV is processed by an FFT and min-pooling operation for each spectrum, which is then combined at block 620 to perform peak detection and interpolation across all spectra to determine the angle amplitude spectrum. Next, at block 622, the angle amplitude spectrum is processed via DFT to generate a vertical array BV for each angle. At block 624, angle estimation is performed on the vertical BV array to determine angle of arrival data. The angle of arrival data is used to construct an output point cloud, which can be provided to a vehicle ADAS as described herein.

[0098] Figures 7-1 to 7-6 are a series of graphs depicting the experimental results of the HASA array signal processing method used in this invention. In each of Figures 7-1 to 7-6, Figures 7-1, 7-3, and 7-5 depict the results of basic HASA BV processing (i.e., azimuth spectrum) (i.e., the spectrum obtained by processing according to...). Figure 3 The processing spectrum captured by the MIMO virtual array configured according to Method 300), and Figures 7-2, 7-4, and 7-6 depict the results of DR-enhanced HASA BV processing (e.g., by the MIMO virtual array configured according to Method 300 and according to...). Figure 6 The method further processes the spectrum captured by the MIMO virtual array.

[0099] Figures 7-1 and 7-2 depict the results for two object conditions, where the reflected signal power of the strongest object is 25 dB higher than that of the second object. In this example, the noise power is 10 dB lower than the signal power of the weaker object in the BV. Calibration errors are assumed to be negligible. FFT beamforming is based on a filled uniform virtual array with a -30 dB Chebyshev window applied. The results depicted in Figures 7-1 and 7-2 show that basic HASABV processing may not be able to recover the weaker object (as shown in Figure 7-1, the amplitude of the second object peak is only at the ambient noise level); however, processing using DR enhancement as shown in Figure 7-2 (e.g., according to...) can recover the weaker object. Figure 6 Method 600 can recover signals from two objects.

[0100] In one or more embodiments, the filled array follows a fractional unit spacing design, where the 0.7λ (wavelength) unit spacing is further divided four times to form a quarter-space or 0.7 / 4λ uniform grid on which the TX and RX antennas can be located (while maintaining a minimum spacing of 0.7λ with the nearest antenna). Thus, the filled uniform array has a spacing of 0.7 / 4λ. This fractional spacing design can help reduce sidelobe levels and may outperform other methods. The quarter-space is used as an example, and other fractional designs are possible (e.g., ½, 1 / 8, etc.).

[0101] Furthermore, in the method of this invention, the constructed virtual arrays can have the same or similar apertures that produce the same or similar main lobe beamwidths. This increases the likelihood that the resolution performance is the same or similar across all array objects. This feature minimizes inconsistencies or discrepancies in the determined main lobe positions and widths across the various virtual arrays.

[0102] Figures 7-3 and 7-4 depict the test results for a scenario involving two objects, where the object with the highest amplitude reflected signal is 25 dB higher than the object with the lower amplitude. The noise level is 10 dB lower than the signal power of the weakest object. Again, it is assumed that the calibration error is at a level typical of well-calibrated systems. In these results, a -30 dB Chebyshev window was applied for the FFT beamforming process. The depicted results show that the basic HASA BV processing (Figure 7-3) may not be able to recover the weaker object signal (i.e., in the top graph, the peak signal of the second object is hidden within the ambient noise level), and that the DR enhancement processing can recover the weaker object peak signal (i.e., in Figure 7-4, in the lower graph, the peak signal of the second object is clearly observed above the ambient noise).

[0103] Therefore, the results depicted in Figures 7-3 and 7-4 show that basic HASA BV processing may not be able to recover weaker objects that DR enhancement processing can successfully recover.

[0104] Figures 7-5 and 7-6 depict the test results for a scenario with three objects, where the object with the highest amplitude reflected signal is approximately 22 dB higher than the object with the lower amplitude. The noise level is 10 dB lower than the signal power of the weakest object. Again, it is assumed that the calibration error is at a level typical of well-calibrated systems. In these results, a -30 dB Chebyshev window was applied for the FFT beamforming process. The depicted results show that the basic HASA BV processing (Figure 7-5) may not be able to recover the signal of the weaker object (i.e., in the top graph, the peak signals of the second and third objects are hidden in the ambient noise level), and that the DR enhancement processing can recover the peak signal of the weaker object (i.e., in Figure 7-6, the peak signals of the second and third objects are clearly observed above the ambient noise).

[0105] Therefore, the results depicted in Figures 7-5 and 7-6 show that basic HASA BV processing may not be able to recover weaker objects that DR enhancement processing can successfully recover.

[0106] In some aspects, the technology described herein relates to a system comprising: a plurality of transmitter modules configured to transmit a plurality of transmitted radar signals using a plurality of transmit antennas; a plurality of receiver modules configured to receive reflections of the plurality of transmitted radar signals using a plurality of receive antennas; and a controller configured to: determine a plurality of MIMO virtual antenna arrays, wherein each of the plurality of MIMO virtual antenna arrays is associated with a subset of the plurality of transmit antennas and a subset of the plurality of receive antennas; determine a signal spectrum for each of the plurality of MIMO virtual antenna arrays using signals received by the plurality of receiver modules, wherein each signal spectrum includes a signal amplitude value and an associated angle, wherein the main lobe of each signal spectrum spans all signal spectra at the same angle, and the side lobes of the signal spectrum are located at angles other than those spanning all signal spectra; construct a first combined MIMO virtual antenna array signal spectrum by combining the minimum values ​​of the signal spectra at each angle value; and process the first combined MIMO virtual antenna array signal spectrum to detect an object.

[0107] In some aspects, the technology described herein relates to a system in which the controller is further configured to: determine a reconstruction signal for the object for each of the plurality of MIMO virtual antenna arrays; determine a second signal spectrum for each of the plurality of MIMO virtual antenna arrays by coherently eliminating the reconstruction signal for the object from the signal spectrum of each of the plurality of MIMO virtual antenna arrays; construct a second combined MIMO virtual array signal spectrum by combining the minimum values ​​of the second signal spectrum at each angular value; and use the second combined MIMO virtual array signal spectrum to determine the direction of arrival of the object.

[0108] In some aspects, the techniques described herein relate to a system in which each MIMO virtual antenna array includes a missing antenna element, and the controller is further configured to accumulate the reconstructed signal for the object to determine a signal spectral value for the missing antenna element of each MIMO virtual antenna array.

[0109] In some aspects, the techniques described herein relate to a system in which the controller is further configured to use the signal spectrum values ​​for the missing antenna element to generate a backfill signal spectrum for each of the plurality of MIMO virtual antenna arrays.

[0110] In some aspects, the techniques described herein relate to a system in which the controller is configured to construct a third combination of MIMO virtual array signal spectra by combining the minimum values ​​of the backfill signal spectrum at each angle value; and to use the third combination of MIMO virtual array signal spectra to determine the angle of arrival of the object.

[0111] In some aspects, the technology described herein relates to a system in which the first antenna elements of the plurality of transmitting antennas are non-uniformly spaced apart from each other by a unit value or a multiple of the unit value, and the second antenna elements of the plurality of receiving antennas are non-uniformly spaced apart from each other by the unit value or a multiple of the unit value.

[0112] In some respects, the techniques described herein relate to a system in which the unit value is equal to or greater than half the wavelength of the signal received by the plurality of receiver modules.

[0113] In some aspects, the technology described herein relates to a system comprising: a plurality of transmitter modules configured to transmit a plurality of transmitted radar signals using a plurality of transmit antennas; a plurality of receiver modules configured to receive reflections of the plurality of transmitted radar signals using a plurality of receive antennas; and a controller configured to: determine a plurality of MIMO virtual antenna arrays, wherein each of the plurality of MIMO virtual antenna arrays is associated with a subset of the plurality of transmit antennas and a subset of the plurality of receive antennas; determine a signal spectrum for each of the plurality of MIMO virtual antenna arrays using signals received by the plurality of receiver modules, wherein each signal spectrum includes a signal amplitude value and has an associated angle value; construct a first combined MIMO virtual antenna array signal spectrum by combining the minimum values ​​of the signal spectra at each angle value; and process the first combined MIMO virtual antenna array signal spectrum to detect an object.

[0114] In some aspects, the techniques described herein relate to a system in which the main lobe positions of the signal spectrum of the plurality of MIMO virtual antenna arrays are located at the same angle across all signal spectra, and the side lobe positions of the signal spectrum are not located at the same angle across all signal spectra.

[0115] In some aspects, the technology described herein relates to a system in which the controller is further configured to: determine a reconstruction signal for the object for each of the plurality of MIMO virtual antenna arrays; determine a second signal spectrum for each of the plurality of MIMO virtual antenna arrays by coherently eliminating the reconstruction signal for the object from the signal spectrum of each of the plurality of MIMO virtual antenna arrays; construct a second combined MIMO virtual array signal spectrum by combining the minimum values ​​of the second signal spectrum at each angular value; and use the second combined MIMO virtual array signal spectrum to determine the direction of arrival of the second object.

[0116] In some respects, the techniques described herein relate to a system in which the antenna elements of the plurality of transmission antennas are non-uniformly spaced apart from each other by a unit value or a multiple of the unit value.

[0117] In some respects, the techniques described herein relate to a system in which the antenna elements of the plurality of receiving antennas are non-uniformly spaced apart from each other by the unit value or multiples of the unit value.

[0118] In some respects, the techniques described herein relate to a system in which the unit value is equal to or greater than half the wavelength of the signal received by the plurality of receiver modules.

[0119] In some aspects, the techniques described herein relate to a method comprising: determining a plurality of MIMO virtual antenna arrays, wherein each of the plurality of MIMO virtual antenna arrays is associated with a subset of a plurality of transmit antennas and a subset of a plurality of receive antennas; determining a signal spectrum for each of the plurality of MIMO virtual antenna arrays using signals received by the plurality of receive antennas, wherein each signal spectrum includes a signal amplitude value and has an associated angle value, wherein the main lobe position of the signal spectrum of the plurality of MIMO virtual antenna arrays is at the same angle across all signal spectra, and the side lobe position of the signal spectrum is not at the same angle across all signal spectra; constructing a first combined MIMO virtual antenna array signal spectrum by combining the minimum values ​​of the signal spectra at each angle value; and processing the first combined MIMO virtual antenna array signal spectrum to detect an object.

[0120] In some aspects, the techniques described herein relate to a method further comprising: determining a reconstruction signal for the object for each of the plurality of MIMO virtual antenna arrays; determining a second signal spectrum for each of the plurality of MIMO virtual antenna arrays by coherently eliminating the reconstruction signal of the object from the signal spectrum of each of the plurality of MIMO virtual antenna arrays; constructing a second combined MIMO virtual array signal spectrum by combining the minimum values ​​of the second signal spectrum at each angular value; and using the second combined MIMO virtual array signal spectrum to determine the direction of arrival of the second object.

[0121] In some aspects, the techniques described herein relate to a method in which each MIMO virtual antenna array includes a missing antenna element, and the method further includes accumulating the reconstructed signal for the object to determine a signal spectral value for the missing antenna element of each MIMO virtual antenna array.

[0122] In some aspects, the techniques described herein relate to a method that further includes using the signal spectrum values ​​for the missing antenna element to generate a backfill signal spectrum for each of the plurality of MIMO virtual antenna arrays.

[0123] In some aspects, the techniques described herein relate to a method that further includes: constructing a third combination of MIMO virtual array signal spectra by combining the minimum values ​​of the backfill signal spectrum at each angle value; and using the third combination of MIMO virtual array signal spectra to determine the angle of arrival of the second object.

[0124] In some aspects, the techniques described herein relate to a method in which the antenna elements of the plurality of transmitting antennas are non-uniformly spaced apart from each other by a unit or a multiple of a unit value, and the antenna elements of the plurality of receiving antennas are non-uniformly spaced apart from each other by a unit or a multiple of a unit value.

[0125] In some respects, the techniques described herein relate to a method in which the unit value is equal to or greater than half the wavelength of the signal received by the plurality of receiving antennas.

[0126] As those skilled in the art will appreciate, aspects of this disclosure can be embodied as systems, processes, methods, and / or program products. Therefore, aspects of this disclosure can take the form of entirely hardware embodiments, entirely software embodiments (including firmware, resident software, microcode, etc.), or embodiments combining software and hardware aspects (which may be generally referred to herein as "circuit," "circuit system," "module," or "system"). Furthermore, aspects of this disclosure can take the form of program products embodied in one or more computer-readable storage media on which computer-readable program code is embodied. (However, any combination of one or more computer-readable media may be utilized. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium.)

[0127] Computer-readable storage media can be, for example, but not limited to, electronic, magnetic, optical, electromagnetic, infrared, biological, atomic, or semiconductor systems, devices, controllers, or apparatuses, or any suitable combination thereof, wherein the computer-readable storage medium itself is not a transient signal. More specific examples (not an exhaustive list) of computer-readable storage media may include: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (“RAM”), read-only memory (“ROM”), erasable programmable read-only memory (“EPROM” or flash memory), optical fiber, portable compressed optical disc read-only memory (“CD-ROM”), optical storage devices, magnetic storage devices, or any suitable combination thereof. In the context of this document, a computer-readable storage medium can be any tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device, controller, or apparatus. Program code embodied on a computer-readable signal medium can be transmitted using any suitable medium, including but not limited to wireless, wired, fiber optic cable, RF, etc., or any suitable combination thereof.

[0128] Computer-readable signal media may include propagated data signals embodying computer-readable program code therein, for example, in baseband or as part of a carrier wave. Such propagated signals may take any of a variety of forms, including, but not limited to, electromagnetic, optical, or any suitable combination thereof. Computer-readable signal media may be any computer-readable medium that is not a computer-readable storage medium but can transmit, propagate, or deliver programs for use by or in conjunction with an instruction execution system, device, controller, or apparatus.

[0129] The block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of circuit systems, systems, methods, processes, and program products according to various embodiments of the present disclosure. In this regard, certain blocks in the block diagrams may represent modules, segments, or portions of code, which include one or more executable program instructions for implementing specified logical functions. It should also be noted that in some implementations, the functions mentioned in the various blocks may occur in a different order than that shown in the figures. For example, depending on the functionality involved, two blocks shown consecutively may actually be executed substantially simultaneously, or these blocks may sometimes be executed in reverse order.

[0130] Modules implemented in software for execution by various types of processors may, for example, comprise one or more physical or logical blocks of computer instructions, which may be organized, for example, as objects, programs, or functions. However, the executable files of the identified modules need not be physically located together, but may include disparate instructions stored in different locations, which, when logically joined together, constitute the module and implement its stated purpose. In practice, a module of executable code can be a single instruction, or many instructions, and may even be distributed across several different code segments in different programs and across several memory devices. Similarly, operational data (e.g., a knowledge base with adjusted weights and / or biases described herein) may be identified and represented within modules herein, and said operational data may be embodied in any suitable form and organized within any suitable type of data structure. Operational data may be collected as a single dataset or may be distributed across different locations, including across different storage devices. The data may provide electronic signals on a system or network.

[0131] These program instructions may be provided to one or more processors and / or controllers of a general-purpose computer, a special-purpose computer, or other programmable data processing equipment (e.g., a controller) to produce a machine such that the instructions, which are executed via the processor of the computer or other programmable data processing equipment, create a circuit system or component for implementing the functions / actions specified in the block diagram.

[0132] It should also be noted that each block of the block diagram, and combinations of blocks within the block diagram, can be implemented by a dedicated hardware-based system (e.g., which may include one or more graphics processing units) or a combination of dedicated hardware and computer instructions that performs the specified function or action. For example, a module can be implemented as hardware circuitry, including custom VLSI circuitry or gate arrays, off-the-shelf semiconductors such as logic chips, transistors, controllers, or other discrete components. Modules can also be implemented in programmable hardware devices such as field-programmable gate arrays, programmable array logic, programmable logic devices, application-specific integrated circuits (ASICs), microcontrollers, system-on-a-chip (SoCs), general-purpose processors, microprocessors, etc.

[0133] These program instructions may also be stored in a computer-readable storage medium that can direct a computer system, other programmable data processing equipment, controller or other means to function in a particular manner, such that the instructions stored in the computer-readable medium produce an article of writing, the article of writing including instructions that implement the functions / actions specified in the block diagram.

[0134] Program instructions may also be loaded onto a computer, other programmable data processing apparatus, controller or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer-implemented process, such that the instructions, which execute on the computer, other programmable apparatus or other device, provide for performing the process for carrying out the function / action specified in the block diagram.

[0135] The foregoing detailed description is illustrative in nature only and is not intended to limit the subject matter or the use of embodiments of this application and such embodiments.

[0136] As used herein, the term “exemplary” means “serving as an example, instance, or illustration.” Any embodiment described herein as exemplary is not necessarily to be construed as preferred or advantageous over other embodiments. Furthermore, one is not to be bound by any expressed or implied theory presented in the foregoing technical field, background art, or specific embodiments.

[0137] The connecting lines shown in the various figures contained herein are intended to represent exemplary functional relationships and / or physical couplings between various elements. It should be noted that many alternative or additional functional relationships or physical connections may exist in embodiments of the subject matter. In addition, certain terms may be used herein for reference only, and therefore these terms are not intended to be limiting, and unless the context clearly indicates otherwise, the terms “first,” “second,” and other such numerical terms referring to structures do not imply order or sequence.

[0138] As used herein, a “node” means any internal or external reference point, connection point, interface, signal line, conductive element, etc., where a given signal, logic level, voltage, data mode, current, or quantity exists. Furthermore, two or more nodes can be implemented with a single physical element (and although receiving or outputting at a common node, two or more signals can still be multiplexed, modulated, or otherwise distinguished).

[0139] The foregoing description refers to elements, nodes, or features being "connected" or "coupled" together. As used herein, unless otherwise explicitly stated, "connected" means that one element is directly engaged to (or directly communicates with) another element, and not necessarily mechanically. Similarly, unless otherwise explicitly stated, "coupled" means that one element is directly or indirectly engaged to (or directly or indirectly communicates with) another element electrically or otherwise, and not necessarily mechanically. Therefore, although the schematic diagrams shown depict an exemplary arrangement of elements, additional intervening elements, devices, features, or components may be present in embodiments of the depicted subject matter.

[0140] While at least one exemplary embodiment has been presented in the foregoing detailed description, it should be understood that numerous variations exist. It should also be understood that the exemplary embodiments described herein are not intended to limit the scope, applicability, or configuration of the claimed subject matter in any way. In fact, the foregoing detailed description will provide a convenient guide for those skilled in the art to implement the described embodiments. It should be understood that various changes can be made to the function and arrangement of the elements without departing from the scope defined by the claims, which includes known and foreseeable equivalents at the time of filing of this patent application.

Claims

1. A system, characterized by include: Multiple transmitter modules are configured to use multiple transmission antennas to transmit multiple transmitted radar signals; Multiple receiver modules are configured to use multiple receiving antennas to receive reflections of the multiple transmitted radar signals; as well as The controller is configured to: A plurality of MIMO virtual antenna arrays are determined, wherein each of the plurality of MIMO virtual antenna arrays is associated with a subset of the plurality of transmit antennas and a subset of the plurality of receive antennas. The signals received by the plurality of receiver modules are used to determine the signal spectrum for each of the plurality of MIMO virtual antenna arrays, wherein each signal spectrum includes a signal amplitude value and an associated angle, wherein the main lobe of each signal spectrum spans all signal spectra at the same angle, and the side lobes of the signal spectrum are located at angles that do not span all signal spectra. The first combination of MIMO virtual antenna array signal spectrum is determined by combining the minimum values ​​of the signal spectrum at each angle. The MIMO virtual antenna array signal spectrum of the first combination is processed to detect the object.

2. The system of claim 1, wherein, The controller is further configured to: For each of the plurality of MIMO virtual antenna arrays, a reconstruction signal for the object is determined; A second signal spectrum is determined for each of the plurality of MIMO virtual antenna arrays by coherently eliminating the reconstructed signal for the object from the signal spectrum of each of the plurality of MIMO virtual antenna arrays; The second combined MIMO virtual array signal spectrum is determined by combining the minimum values ​​of the second signal spectrum at each angle value; and The direction of arrival of the object is determined using the MIMO virtual array signal spectrum of the second combination.

3. The system of claim 2, wherein, Each MIMO virtual antenna array includes a missing antenna element, and the controller is further configured to accumulate the reconstructed signal for the object to determine a signal spectrum value for the missing antenna element of each MIMO virtual antenna array.

4. The system of claim 3, wherein, The controller is further configured to use the signal spectrum values ​​for the missing antenna element to generate a backfill signal spectrum for each of the plurality of MIMO virtual antenna arrays.

5. The system of claim 4, wherein, The controller is configured to The third combination of MIMO virtual array signal spectra is determined by combining the minimum values ​​of the backfill signal spectra at each angle; and The angle of arrival of the object is determined using the MIMO virtual array signal spectrum of the third combination.

6. A system, characterized by include: Multiple transmitter modules are configured to use multiple transmission antennas to transmit multiple transmitted radar signals; Multiple receiver modules are configured to use multiple receiving antennas to receive reflections of the multiple transmitted radar signals; as well as The controller is configured to: A plurality of MIMO virtual antenna arrays are determined, wherein each of the plurality of MIMO virtual antenna arrays is associated with a subset of the plurality of transmit antennas and a subset of the plurality of receive antennas. The signals received by the plurality of receiver modules are used to determine the signal spectrum for each of the plurality of MIMO virtual antenna arrays, wherein each signal spectrum includes signal amplitude values ​​and has associated angle values. The first combination of MIMO virtual antenna array signal spectrum is determined by combining the minimum values ​​of the signal spectrum at each angle. The MIMO virtual antenna array signal spectrum of the first combination is processed to detect the object.

7. The system of claim 6, wherein, The controller is further configured to: For each of the plurality of MIMO virtual antenna arrays, a reconstruction signal for the object is determined; A second signal spectrum is determined for each of the plurality of MIMO virtual antenna arrays by coherently eliminating the reconstructed signal for the object from the signal spectrum of each of the plurality of MIMO virtual antenna arrays; The second combined MIMO virtual array signal spectrum is determined by combining the minimum values ​​of the second signal spectrum at each angle value; and The arrival direction of the second object is determined using the MIMO virtual array signal spectrum of the second combination.

8. A method characterized by, include: A plurality of MIMO virtual antenna arrays are defined, wherein each of the plurality of MIMO virtual antenna arrays is associated with a subset of a plurality of transmit antennas and a subset of a plurality of receive antennas; The signals received by the plurality of receiving antennas are used to determine the signal spectrum for each of the plurality of MIMO virtual antenna arrays, wherein each signal spectrum includes a signal amplitude value and has an associated angle value, wherein the main lobe position of the signal spectrum of the plurality of MIMO virtual antenna arrays spans all signal spectra at the same angle, and the side lobe positions of the signal spectrum span all signal spectra at different angles. The first combined MIMO virtual antenna array signal spectrum is determined by combining the minimum values ​​of the signal spectrum at each angle value; and The MIMO virtual antenna array signal spectrum of the first combination is processed to detect the object.

9. The method of claim 8, wherein, Also includes: For each of the plurality of MIMO virtual antenna arrays, a reconstruction signal for the object is determined; A second signal spectrum is determined for each of the plurality of MIMO virtual antenna arrays by coherently eliminating the reconstructed signal of the object from the signal spectrum of each of the plurality of MIMO virtual antenna arrays; The second combined MIMO virtual array signal spectrum is determined by combining the minimum values ​​of the second signal spectrum at each angle value; and The arrival direction of the second object is determined using the MIMO virtual array signal spectrum of the second combination.

10. The method of claim 9, wherein, Each MIMO virtual antenna array includes a missing antenna element, and the method further includes accumulating the reconstructed signal for the object to determine a signal spectral value for the missing antenna element of each MIMO virtual antenna array.

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