Vehicle radar system and methods for calibrating it

Abstract

Method for calibrating a vehicle radar system, wherein the vehicle radar system comprises a transmitting antenna arrangement with a plurality of transmitting antennas and a receiving antenna arrangement with a plurality of receiving antennas, wherein the method comprises the following steps: Transmitting a large number of signals using the transmitting antenna array; Receive a multitude of signals with the receiving antenna arrangement; Receiving a multitude of antenna responses based on the multitude of received signals, each of the antenna responses containing positional information regarding a target object; Applying a multitude of hypothetical calibration matrices to each of the multitude of receiving antenna responses to obtain a multitude of calibrated array responses, each of the multitude of hypothetical calibration matrices incorporating calibration information relating to the vehicle radar system; Applying at least one beam-shaping matrix to each of the multitude of calibrated array reactions to obtain a multitude of beam-shaping images; Deriving at least one uncertainty metric for each of the plurality of beamforming images, wherein each of the plurality of uncertainty metrics is representative of a beamforming image resolution; Selecting at least one of the multitude of hypothetical calibration matrices based on the multitude of uncertainty metrics, wherein the selected hypothetical calibration matrix is ​​associated with the uncertainty metric of the one with the best beamforming image resolution; and Using the selected hypothetical calibration matrix to calibrate the vehicle radar system.

Description

Technical field

[0001] The present disclosure relates generally to radar systems, and in particular to systems and methods for calibrating them. background

[0002] Many modern vehicles are equipped with advanced safety and driver assistance systems that require robust and precise object detection and tracking systems to control the vehicle's maneuvers. These systems utilize periodic or continuous object detection and control algorithms to estimate various object parameters, such as relative object range, speed, direction of travel, and size. For example, radar devices detect and locate objects (i.e., the reflected signal is returned as an echo to the radar and processed there to determine various pieces of information, such as the travel time of the transmitted / received signals).

[0003] Advanced radar systems in use today can employ a multiple-input multiple-output (MIMO) concept, which uses multiple antennas at the transmitter to send independent waveforms and multiple antennas at the receiver to receive the radar echoes. In a "co-localized" MIMO radar configuration, the antennas in both the transmitter and receiver are positioned close enough together that each antenna observes the same aspect of an object, thus creating the impression of a point target. A matched filter bank is used in the MIMO receiver to extract the waveform components. When signals are transmitted from different antennas, the echoes of each signal carry independent information about detected objects and their respective propagation paths.Phase differences caused by different transmitting antennas, together with those caused by different receiving antennas, mathematically form a virtual antenna array, resulting in a larger virtual aperture with fewer antenna elements. Conceptually, the virtual array is created by interleaving each of the transmitter (tx) and receiver (rx) antenna elements such that the elements in the virtual array represent tx-rx pairs for the respective transmitter (tx) and receiver (rx) antennas in the MIMO configuration. For co-localized MIMO antennas, a transmitting array with n transmitting antennas and a receiving array with m receiving antennas generates a virtual array with mxn virtual receiving elements. In other words, the waveforms are extracted by the matched filters at the receiver such that a total of mxn extracted signals are present in the virtual array.The virtual receiver elements mxn can be used to create a beamforming image.

[0004] Over time, certain aspects of the radar configuration, such as predefined values ​​used in generating the beamforming image, may need to be adjusted or calibrated to maintain the desired level of accuracy in a given radar configuration.

[0005] DE 10 2014 208 899 A1 describes a method for calibrating an antenna pattern of a MIMO radar sensor with Ntx transmit antenna elements and Nrx receive antenna elements, comprising the following steps: before commissioning the radar sensor: - storing an antenna pattern that assigns a respective control vector to each of several angles, composed of a transmit control vector and a receive control vector; after commissioning: - performing a radar measurement to locate an object, - checking whether the located object is a single target or multiple targets, - if it is a single target: - performing a SIMO measurement with each of the transmit antenna elements, - estimating the angle of the object based on the measurement results, - calculating a first reference value for each transmit antenna element, dependent on the components of the transmit control vector, - calculating a second,- The comparison parameter for each transmitting antenna element depends on the results of the SIMO measurements, and - Correcting the transmitting control vector based on a known relationship between the first and second comparison parameters for each transmitting antenna element.

[0006] US 2012 / 0081247A1 describes a radar system. The radar system includes a transmitting antenna and a receiving antenna formed by a group of radiation elements. Antenna beams are calculated in P directions using a BFC function. Target detection by sidelobes of the beams is processed by an algorithm that compares the levels received in a range-velocity resolution cell, whereby a single detection for each range-velocity resolution cell is not possible.Processing means assume that for a given resolution cell of the radar, either in velocity mode or range mode, or alternatively depending on the implemented processing, there may be more than one echo with a sufficient signal-to-noise ratio to be detectable; and if there is more than one detectable echo for each resolution cell from the multitude of beams formed by BFC, only the echo and BFC that receive the maximum power or the maximum signal-to-noise ratio are considered valid.

[0007] US 2017 / 0301988A1 describes an antenna system and a method configured to compute calibration element voltage gain patterns as functions of a digital antenna model and a variety of complex beamshaping voltages, determine a calibration cross-path transfer function from the variety of complex voltages, and remove the calibration element voltage gain patterns from the calibration cross-path transfer functions to determine a beamshaping network transfer function. The beamshaping network transfer function and the far-field element voltage gain patterns are combined to obtain a system transfer function, which is used to revise a calibration table.

[0008] US 2018 / 0088220A1 describes a method that includes: simultaneous transmission of coded waveforms across multiple elements for multiple frames, construction of a first multi-input single-output (MISO) system from the codes to model transmit-receive paths, solving the system and RF data observation using linear model theory, providing an IR set for the medium, and applying the estimates to a secondary MISO system constructed analogously to the first, but with pulses suitable for beamforming, in the form of a focused set of single-cycle pulses for an ideally focused reconstruction. Description of the invention

[0009] The invention is defined by the claims.

[0010] According to one aspect, a method for calibrating a vehicle radar system is provided, comprising a transmitting antenna array with a plurality of transmitting antennas and a receiving antenna array with a plurality of receiving antennas. The method may include the following steps: transmitting a plurality of transmitting signals with the transmitting antenna array; receiving a plurality of receiving signals with the receiving antenna array; obtaining a plurality of antenna responses based on the plurality of received signals, each of the antenna responses containing position information with respect to a target object; applying a plurality of hypothetical calibration matrices to each of the plurality of receiving antenna responses to obtain a plurality of calibrated array responses, each of the plurality of hypothetical calibration matrices containing calibration information with respect to the vehicle radar system.Applying a first beam-shaping matrix and a second beam-shaping matrix to each of the plurality of calibrated array reactions to obtain a plurality of beam-shaping images; deriving at least one uncertainty metric for each of the plurality of beam-shaping images, wherein each of the plurality of uncertainty metrics is representative of a beam-shaping image resolution; selecting at least one of the plurality of hypothetical calibration matrices based on the plurality of uncertainty metrics, wherein the selected hypothetical calibration matrix is ​​associated with the uncertainty metric of the best beam-shaping image resolution; and using the selected hypothetical calibration matrix to calibrate the vehicle radar system.

[0011] According to another aspect, a vehicle radar system is provided, which is mounted on a carrier vehicle. The vehicle radar system may comprise: a transmitter; a transmitting antenna arrangement with a plurality of transmitting antennas coupled to the transmitter, wherein the transmitting antenna arrangement transmits a plurality of transmit signals; a receiving antenna arrangement with a plurality of receiving antennas, wherein the receiving antenna arrangement receives a plurality of received signals; and a receiver coupled to the receiving antenna arrangement, wherein the receiver can be configured to: receive a plurality of antenna responses based on the plurality of received signals, wherein each of the antenna responses includes positional information regarding a target object;Applying a plurality of hypothetical calibration matrices to each of the plurality of receive antenna responses to obtain a plurality of calibrated array responses, wherein each of the plurality of hypothetical calibration matrices includes calibration information regarding the vehicle radar system; applying at least one beamshaping matrix to each of the plurality of calibrated array responses to obtain a plurality of beamshaping images; deriving at least one uncertainty metric for each of the plurality of beamshaping images, wherein each of the plurality of uncertainty metrics is representative of a beamshaping image resolution; selecting at least one of the plurality of hypothetical calibration matrices based on the plurality of uncertainty metrics, wherein the selected hypothetical calibration matrix is ​​associated with the uncertainty metric with the best beamshaping image resolution;and using the selected hypothetical calibration matrix to calibrate the vehicle radar system. Brief description of the drawings

[0012] One or more embodiments of the invention are described below in conjunction with the accompanying drawings, wherein identical designations denote identical elements and wherein the following applies: Fig. Figure 1 is a schematic block diagram of a carrier vehicle and a target object, wherein the carrier vehicle includes an exemplary vehicle radar system; Fig. Figure 2 is a flowchart illustrating an exemplary procedure for calibrating a vehicle radar system; and Fig. 3 is a flowchart that shows a detailed embodiment of several of the steps in the flowchart of Fig. 2 depicts, including those assigned to a calibration search process. Detailed description

[0013] The vehicle radar system and calibration procedure described herein incorporates system calibration to enable the calculation of target parameters with improved accuracy. Generally, the calibration procedure uses a number of hypothetical calibration matrices, representing reasoned estimates for possible system or array calibrations, to obtain a number of beamforming images. For each beamforming image, an uncertainty metric is derived, with the uncertainty metric generally representing the quality or resolution of the beamforming image. The procedure then selects hypothetical calibration matrices based on their uncertainty metrics, assigning the selected matrices to those with the best beamforming image resolution (e.g., the least amount of image blur).The selected hypothetical calibration matrices are then used to generate new calibration matrices, which in turn can be used to calibrate the vehicle radar system to obtain more accurate target parameters. This type of calibration can be used, for example, to avoid image distortions caused by convolution with a beamform, resulting in blurring and sidelobes. According to one example, the procedure involves new calibration matrices that include a first calibration matrix for a height dimension and a second calibration matrix for an azimuthal dimension (so-called 2D calibration), and the procedure can provide this calibration on the fly (i.e., while the vehicle is driving).

[0014] The vehicle radar system and the procedure described below are directed towards a multiple-input multi-output (MIMO) radar system and a procedure for calibrating the vehicle radar system. Fig. Figure 1 illustrates a possible architecture for a MIMO vehicle radar system 10 with which the disclosed method can be implemented. While the approach and methodology described herein relate to the in Fig. Referring to the radar configuration shown in Figure 1, a person skilled in the field will recognize that the vehicle radar system 10 is merely exemplary, and in many respects the schematic block diagrams in these figures serve to simplify the explanation. Other configurations and embodiments can certainly be used instead, since the vehicle radar system and method described herein represent only one possible example.

[0015] The vehicle radar system 10 can be a MIMO system comprising a transmitter 12, a transmit antenna array 20 with a number of transmit antennas 22-26, a receive antenna array 30 with a number of receive antennas 32-38, a receiver 14, a radar control module 16, and any other suitable hardware, firmware, software, and / or other components useful for the operation of such a system. According to one example, the transmitter 12 is communicatively coupled to a transmit antenna array 20 with N transmit antennas 22-26 configured to create a sensor field of view that monitors a particular area of ​​interest. The transmit antenna array 20 is configured to send electromagnetic signals (i.e., coded transmit signals) 42-46 that are reflected by one or more target objects 18 within the field of view of the vehicle radar system 10. According to the example in Fig. In the non-restrictive example shown in Figure 1, the transmitting antenna arrangement 20 is mounted at the front of the carrier vehicle, includes three transmitting antennas 22-26, and is configured to transmit radar signals in a direction generally parallel to the longitudinal axis of the carrier vehicle. However, this is only one possibility. For example, the transmitting antenna arrangement 20 could be mounted at a location other than the front of the carrier vehicle, it could include more or fewer than three transmitting antennas, and it could be oriented in a different direction.

[0016] The transmitter 12 can be a standalone module or unit; it can be part of a larger module, unit, system, etc.; it can include a number of submodules, subunits, subsystems, etc.; or it can be configured according to some other arrangement or architecture, provided that it is configured to generate electromagnetic signals for transmission via the transmitting antenna arrangement 20 according to the method disclosed herein. In a non-limiting example, the transmitter 12 includes a baseband processor configured to manage radio operations, including generating signals for transmission using the antenna arrangement 20.The baseband processor may include hardware, firmware, and / or software typically found on such transmitters, including random-access memory (RAM, including static RAM (SRAM) and dynamic RAM (DRAM)) or other types of memory, including flash memory, other solid-state memory, or other suitable storage. In further embodiments, the baseband processor of the transmitter 12 is incorporated into a radar control module 16. The transmitter 12 may include waveform generators, oscillators, amplifiers, mixers, combiners, filters, converters, and / or processors, to name only a few possible components. By way of example only, a waveform generator may be configured to produce waveforms or signals with different pulse widths, different waveform types, and / or different pulse repetition intervals (PRI) within a given coherent processing interval (CPI).The waveforms or signals can then be digitized by a digital-to-analog (D / A) converter and converted into a high-frequency carrier by a boost converter. The boost converter can consist of intermediate frequency (IF) and / or high-frequency (RF) oscillators, filters, and / or synchronization circuits. A transmit amplifier can then generate a transmit signal that can be fed to a circulator or similar device. This is just one possible configuration for transmitter 12, as numerous other configurations are certainly possible.

[0017] The reflected signals 52-58 reflect the target object 18 and are received as echoes by a receiving antenna array 30 with M receiving antennas 32-38. According to this non-restrictive example, there are three transmitting antennas 22-26 (N = 3) and four receiving antennas 32-38 (M = 4). This results in a virtual antenna array with M x N virtual receiver elements (M x N = 12 in the illustrated example). The transmitting antennas 22-26 and the receiving antennas 32-38 can be designed or configured to transmit or receive signals of a specific frequency or frequency range. As mentioned above in connection with the transmitting antenna array, although the receiving antenna array 30 is illustrated in the drawings as a quadruple antenna array mounted on the front of the carrier vehicle and oriented in the forward direction of the vehicle, this is not required.The receiving antenna arrangement 30 can be mounted on other sections of the vehicle, it can be oriented in other directions, and it can have more or fewer antenna elements than four, to name just a few possibilities.

[0018] The receiver 14 is configured to process and extract information from the reflected signals or echoes relating to the target 18, such as its range, azimuth or azimuth angle (collectively, "azimuth"), altitude or elevation angle (collectively, "altitude"), and rate of travel or speed. The receiver 14 may be a standalone module or unit; it may be part of a larger module, unit, system, etc. (e.g., the receiver may be part of the radar control module 16; it may be part of a module, unit, system, etc., that also includes the transmitter 12, etc.); it may be configured according to another arrangement or architecture, provided that it is configured to process electromagnetic signals received by the receiving antenna arrangement 30 according to the method disclosed herein.According to a non-restrictive example, receiver 14 includes hardware, firmware, and / or software typically found on receivers such as amplifiers, mixers, decouplers, oscillators, combiners, filters, and converters. The functions performed by receiver 14 may vary, but generally include various filtering, amplification, conversion, and digitization functions, as well as signal processing functions such as analyzing various features of the signals and waveforms to determine information such as phase, frequency, and amplitude. As understood by those skilled in the art, the techniques used to extract this information from the signals and waveforms may vary, and may include, without limitation, phase-in-phase analysis, quadrature analysis, and frequency-domain analysis using Fourier transforms.In one embodiment, the receiver 14 may also include components for performing pulse compression and noise suppression functions (e.g., Doppler filtering). In at least one embodiment, the transmitter 12 and / or the receiver 14 includes a combination of radio receiver circuits configured to perform the signal processing functionality described herein, as set forth in [reference]. Fig. 2 is shown.

[0019] In one embodiment, the receiver 14 may include a baseband processor as described above in relation to the transmitter 12. In some embodiments, the transmitter 12 and the receiver 14 may share a common baseband processor, such as one integrated as part of the radar control module 16. For example, all or certain sections of the receiver 14 may be integrated into the radar control module 16 along with all or certain sections of the transmitter 12. The receiver 14 and / or the radar control module 16 may include a radio chipset comprising an integrated circuit and connected to, or including, a processor and memory.The receiver 14 and / or the radar control module 16 may also include certain components or circuits configured to connect the radio chipset and circuitry to a vehicle communication system, enabling the vehicle radar system 10 to communicate with other components, modules, and / or systems throughout the vehicle and beyond. For example, the vehicle radar system 10 may be part of the vehicle's electronics, allowing it to communicate with other vehicle system modules 140 via a central vehicle communication bus 150.

[0020] Likewise, the receiver 14 can include any combination of hardware, firmware, and / or software required to perform Doppler frequency shift filtering and range filtering, thus enabling Doppler frequency shift and range binning (hereinafter referred to as "Doppler range binning"). Doppler range binning can be used to isolate response or echo information about specific targets, and thereby a variety of Doppler range bins can be generated, each bin being assigned a specific range of Doppler frequency shift values ​​and a specific range of distance values ​​to a target object.When determining whether a particular Doppler range bin contains information regarding a target object, the receiver 14 can determine whether the energy concentration at or around the Doppler range bin is above a threshold. If so, the corresponding information can be used to obtain an array response for that particular Doppler range bin. In some scenarios, multiple Doppler range bins may contain information that informs the vehicle radar system 10 that multiple target objects are being detected. In this case, multiple Doppler range bins can be used to obtain separate responses for each of the target objects and / or Doppler range bins. These separate responses can be used as part of the radar calibration process, which is explained in more detail below.

[0021] In one embodiment, the radar system 10 is implemented on a carrier vehicle 100, and the transmitter 12, the receiver 14, and / or the radar control module 16 are part of a vehicle control module installed on the carrier vehicle. In another embodiment, one or more components or parts of the radar system 10 may be implemented or hosted in a remote facility, such as a backend or a cloud-based facility. The control module may include any variety of electronic processing devices, storage devices, input / output (I / O) devices, and / or other known components and may perform various control and / or communication-related functions. Depending on the embodiment, the control module may be a standalone vehicle electronics module, or it may be integrated into or embedded in another vehicle electronics module (e.g.,It can be a steering control module, brake control module, or it can be part of a larger network or system (e.g., an autonomous driving system, a traction control system (TCS), an electronic stability control system (ESC), an anti-lock braking system (ABS), a driver assistance system, an adaptive cruise control system, a lane departure warning system), to name just a few possibilities. This control module is not limited to a particular embodiment or arrangement.

[0022] Furthermore, a vehicle electronics system can include various vehicle modules, including an engine control unit (ECU) 120, an on-board computer 130, and other vehicle system management systems (VSMs) 140. The ECU 120 can be used to control various aspects of engine operation, such as fuel ignition and ignition timing. The ECU 120 is connected to the communication bus 150 and can receive operating instructions from a body control module (BCM) (not shown) or other VSMs, including the on-board computer 30. The ECU 120 can control an internal combustion engine (ICE) and / or electric drive motors (or other primary drives).

[0023] The onboard computer 130 is a vehicle system module that includes a processor and memory. Furthermore, the onboard computer 30 can, at least in some embodiments, be an infotainment unit (e.g., infotainment head unit, in-vehicle infotainment unit (IVI)), a vehicle head unit, a center stack module (CSM), or a vehicle navigation module. The processor can be used to execute various types of digitally stored instructions, such as software or firmware programs stored in memory, which enable the computer 130 to provide a wide variety of services. In one embodiment, the processor can execute programs or process data to perform at least part of the method described herein. For example, the processor can receive signals or data from various vehicle system modules (e.g., VSM 140), including sensor data.In a specific embodiment, the on-board computer 130 can determine when the procedure 200 should be initiated (see below in . Fig. 2) For example, the on-board computer can receive sensor data from a vehicle sensor (e.g., a camera, radar, lidar, or other sensor installed on the vehicle) and, based on the received sensor data, determine that speed information (and / or other information, including spatial information) regarding a target object is desired. This information may be desirable, for example, when the vehicle is performing autonomous and / or semi-autonomous operations.

[0024] As previously mentioned, the radar control module 16 includes a processor and memory in at least some embodiments, and in some embodiments the transmitter 12 and / or the receiver 14 includes a processor and memory. The processor can be any type of device capable of processing electronic instructions, including microprocessors, microcontrollers, host processors, controllers, vehicle communication processors, and application-specific integrated circuits (ASICs). The memory can include volatile RAM or other temporary storage, as well as non-volatile computer-readable media (e.g., EEPROM) or any other electronic computer medium that stores some or all of the software for performing the various radio and / or signal processing functions described herein.

[0025] Fig. Figure 2 illustrates a flowchart depicting an exemplary procedure 200 for calibrating a vehicle radar system, thereby obtaining more accurate information regarding the target objects. According to the example, procedure 200 implements a calibration search process that can employ various techniques, such as an iterative cost optimization process using gradient-sinking techniques. As a result of the calibration search process, new calibration matrices are identified that can be used to calibrate the vehicle radar system, thus obtaining a more accurate beamforming image. It should be understood that it is not necessary to perform the steps of procedure 200 in the specific order or sequence shown and described, and that performing some or all of these steps in an alternative order is perfectly acceptable.In a non-restrictive example, all or some of the steps of procedure 200 are performed by the vehicle radar system 10 as a standalone system or as part of a larger vehicle system.

[0026] Procedure 200 can be initiated or started in response to a variety of different events, circumstances, scenarios, conditions, etc. For example, procedure 200 can begin when the carrier vehicle 100 is switched on (e.g., when the vehicle ignition is switched on and the vehicle starts) or put into operation, after which the procedure could run continuously, periodically, intermittently, or otherwise in the background. According to another example, procedure 200 can begin in response to a report that the vehicle radar system 10 is not properly calibrated (e.g., when a forward-facing camera on the carrier vehicle takes pictures of a target object and the information derived from these images does not match the information from the vehicle radar system 10).In yet another example, procedure 200 can be initiated when certain vehicle functions or features requiring input from the vehicle radar system 10 are switched on or otherwise activated (e.g., when one or more autonomous or semi-autonomous driving functions are activated). It is also possible for procedure 200 to be initiated on a periodic or routine basis (e.g., once per minute, hour, day, week, month, etc.). The aforementioned examples represent only some of the ways in which procedure 200 can be initiated or started, as other possibilities exist, such as manually initiating the calibration procedure.

[0027] Starting with step 210, the process generates one or more transmission signals T xand can do so according to several different techniques. According to one such technique, a waveform generator in the transmitter 12 produces a modulated signal MS1 in the form of a baseband signal centered around a carrier frequency. The modulated signal MS1 can have a bandwidth corresponding, for example, to linear frequency modulation (LFM) chirs or pulses. The modulated signal MS1 can be any suitable modulated signal or waveform for use with the vehicle radar system 10, including modulated signals with a center frequency in the range of 10 to 100 GHz. In one embodiment in which the vehicle radar system 10 is mounted at the front of a vehicle, the waveform generator produces a modulated signal MS1 with a center frequency of approximately 77 to 81 GHz.

[0028] After generating a modulated signal MS1, the modulated signal MS1 can be processed according to a communication access scheme to transmit the signals T x1 -T xN to obtain. Various communication access schemes can be used, including multiple access in time-division multiplexing (TDMA), multiple access in code-division multiplexing (CDMA), binary phase modulation (BPM), code-division multiplexing (CDM), orthogonal frequency-division multiplexing (OFDM), and other suitable techniques. In one embodiment, the method can mix the modulated signal MS1 with a code sequence (C1 to C). N ), to transmit a number of coded signals Tx1 to Tx N to generate, wherein the code sequence includes at least one separate code for each of the transmitting antennas in the transmitting antenna arrangement 20. Typically, the number of codes in the code sequence (C1 to C) is N ) equal to the number of transmitting antennas in the transmitting antenna arrangement 20 (e.g. in Fig. 1, N=3, so that three codes (C1 to C N ) and three transmitting antennas 22-26). As used herein, the terms "mixing," "unmixing," "blending," "unmixing," "mixer," and "unmixer," and their other forms, largely encompass all suitable signal processing techniques that involve mixing, unmixing, modulating, demodulating, encoding, decoding, multiplying, and / or otherwise applying or extracting a code or codeword from or onto a modulated signal or waveform. In one example, step 210 uses the frequency mixers to mathematically multiply the initially modulated signal MS1 by three separate codes (C1 to C3) to produce output signals in the form of coded transmit signals T x1 -T x3to obtain. The codes can be orthogonal codes, which allow transmission channels between the different transmitting antennas to be separated so that the signals received at receiver 14 can be separated accordingly. Such an encoding technique is often useful because when several signals are transmitted simultaneously in the same frequency range, the sum of all these transmissions is received at receiver 14. By mixing the transmitted signals with different codes (e.g., orthogonal codes), the sum or combination of the received signals at receiver 14 can then be separated, so that the separately transmitted signals can be separated or evaluated.

[0029] According to the non-restrictive example of Fig. The vehicle radar system 10 includes a transmitting antenna array 20 with three transmitting antennas 22-26 (N=3), and accordingly, a code sequence with three codes (C1, C2, C3), one for each transmitting antenna, can be used. The first code C1 can then be mixed with the modulated signal MS1 to obtain a first transmit signal Tx1. Likewise, the second code C2 can be mixed with the modulated signal MS1 to obtain a second transmit signal Tx2, and the third code C3 can be mixed with the modulated signal MS1 to obtain a third transmit signal Tx3. It should be noted that mixing and / or modulation techniques other than those described above can be used to generate transmit signals Tx1 to Tx3. N to generate. The process can then continue with step 220.

[0030] In step 220, the procedure transmits or sends the transmit signals Tx1 to Tx Nusing the transmitting antenna arrangement 20 with the N number of transmitting antennas. For example, if the procedure is carried out with the vehicle radar system 10 (where N = 3), three transmit signals Tx1, Tx2, Tx3 are transmitted: the transmit signal Tx1 is transmitted by the first transmitting antenna 22, the second transmit signal is transmitted by the second transmitting antenna 24 Tx2, and the third transmit signal is transmitted by the third transmitting antenna 26 Tx3. Fig. Figure 1 schematically illustrates three electromagnetic signals 42-46 that are transmitted via the antennas 22-26. In one embodiment, the transmit signals are Tx1 to Tx2. N simultaneously transmitted or sent over the same frequency band. In further embodiments, the transmitted signals Tx1 to Tx N It can be sent at different times, with different frequencies (e.g., with different center frequencies), with different codes, etc. The transmission signals Tx1 to Tx NThey can be transmitted using techniques known to those skilled in the art, such as binary phase modulation (BPM), code division multiplexing (CDM), code multiplex access (CDMA), time division multiplex access (TDMA), and any other suitable technique. The procedure is not limited to any one particular technique. The procedure then proceeds to step 230.

[0031] In step 230, a large number of received signals Rx1 to Rx are processed. M The signals are received at the receiving antenna array 30 and include at least one received signal for each receiving antenna in the receiving antenna array. For illustration, the collection of transmitted signals Tx1 to Tx reflects N the target object 18 in the form of reflected signals 52-58, which in turn are received at each of the M number of receiving antennas in the form of received signals Rx1 to Rx M are received. Thus, each of the received signals Rx1 to Rx representsM the sum or collection of transmitted signals Tx1 to Tx N dar (these signals have not yet been decoded or separated), but since the transmission paths to the various receiving antennas 32-38 are slightly different, the received signals Rx1 to Rx N somewhat differently (e.g., due to its relative position within the receiving antenna arrangement 30, antenna 32 can receive signal 52 shortly before the received signal 54 from antenna 34, and so on). In this sense, the received signals Rx1 to Rx M This represents data that is sampled or collected from the electromagnetic waves 52-58 reflected by the target object 18 in the radar system's field of view. For example, reflected electromagnetic waves 52-58 reach the receiving antennas 32-38 and can be sampled at a sampling frequency F. s are sampled and processed using known techniques to generate the received signals Rx1 to Rx Mto obtain. According to a non-restrictive example, the hardware and / or software used to perform these steps and to process the received signals is designated as Rx1 to Rx. M Part of the recipient 14.

[0032] In step 240, the procedure performs a pre-beam shaping process for the received signals Rx1 to Rx M through. The pre-beam shaping process can separate the received signals Rx1 to Rx. M(Step 242), performing Doppler frequency shift filters and / or range filters (i.e., Doppler range binning) (Step 244), and selecting one or more Doppler range bins to obtain one or more dual-range responses, each corresponding to a specific target object (Step 246). In general, the pre-beam shaping process of Step 240 is typically used to assist the procedure in filtering out unwanted signals and differentiating or distinguishing various target objects within the sensor's field of view, allowing the procedure to calibrate the vehicle radar system with respect to one target object at a time.

[0033] In step 242, each of the received signals Rx1 to Rx is M with the codes C1 to C N unmixed (or mixed) to separate received signals S 1,1 to S M,NTo illustrate this, the received signal Rx1 is fed into frequency mixers with the same code sequence C1 to C3 as previously used in step 210 and then separated; this results in separate received signals S. 1,1 to S 1,N Since in this example three codes C1 to C3 are used to receive the signal Rx1, three decoded or separate received signals S are generated. 1,1 to S 1,3 generated, corresponding to the receiving antenna 32 (Rx1); the same would happen for the other three receiving antennas 34-38, so that a total of twelve separate received signals are generated. Each of the separate received signals S 1,1 to S M,NIncludes position information regarding a target object (e.g., the separate received signals may contain position information in the form of altitude, azimuth, range, and / or Doppler frequency shift data; however, position information can include any data relating to the position, velocity, and / or acceleration of a target object). Various filtering and / or other signal processing techniques may be applied to the received signals Rx1 to Rx. M be carried out and / or the received signals S 1,1 to S M,N Separate. Step 242 can be performed, for example, in conjunction with a CDM or CDMA technique.

[0034] In other embodiments, such as those that do not use CDM or CDMA techniques, the separate received signals S 1,1 to S M,NEach signal is obtained according to the technique used, as recognized by experts. For example, when using a TDMA technique, the first transmitting antenna 22 can be used to transmit a transmit signal Tx1, and this signal can be received at each receiving antenna Rx1 to Rx2. M M received signals are obtained to generate M receive signals. These signals may already be separated due to the nature of the TDMA technique, so the separation step (step 242) may not be necessary. In such a case, the M receive signals correspond to Rx1 to Rx. M M, which were obtained based on the single transmit signal Tx1 from the first transmitting antenna, the separate receive signals S 1,1 to S M,1 . Subsequent transmission signals Tx2 to Tx N can be sent and received in the same way to combine the remaining separate received signals S 1,2 to S M,Nto obtain. Thus, in different scenarios, MxN separate received signals are obtained, with each of the separate received signals S 1,1 to S M,N corresponds to a specific transmitter-receiver pair.

[0035] In step 244, the separate received signals S 1,1 to S M,N filtered based on Doppler frequency shift and / or range filtering. The separate received signals S 1,1 to S M,NSignals can be organized or grouped into Doppler range bins, with each Doppler range bin containing signals that have substantially the same Doppler frequency shift f and / or range r. In this way, the method is able to distinguish between different target objects within the sensor's field of view, since all signals with substantially the same Doppler frequency shift f and / or range r are likely to be attributed to the same target object. The Doppler frequency shift f can be represented as a single value or by a range of values, and, according to an example, the observed Doppler frequency shift for each of the separate received signals S 1,1 to S M,Nis rounded to a single, nearby Doppler frequency shift f using rounding techniques known to those skilled in the art. Similarly, the range or distance r to the target object can be represented by a single value or a range of values. After a Doppler frequency shift f and / or a range r for the separate received signals S 1,1 to S M,N Once determined, these signals can be assigned or allocated to the different Doppler range bins or groups, so that the procedure can then focus on the signals of a specific target object.

[0036] In step 246, one or more Doppler range bins can be selected, each corresponding to a specific target object. Each of the Doppler range bins can be analyzed to determine whether the information contained in or associated with that particular Doppler range bin adequately indicates the presence of a target object. In one embodiment, a minimum energy threshold (or reflection intensity) can be used to determine whether a particular Doppler range bin sufficiently indicates the presence of a target object. In some scenarios, none of the Doppler range bins may exceed the minimum energy threshold, and in this case, procedure 200 can return to step 210 and then be executed again. After a Doppler range bin has been selected, the procedure proceeds to step 248.

[0037] In step 248, a receive antenna response is obtained for each selected Doppler range bin, containing position information about one or more target objects. According to a non-restrictive example, the receive antenna response X includes k Azimuth and altitude information for a given Doppler range bin (i.e., for a given target object with substantially the same Doppler frequency shift f and / or range of r measurements), where k represents the index of the selected Doppler range bin (k is an index between 1 and K, where K represents the number of selected Doppler range bins). The receive antenna response X kcan be represented or expressed in any number of suitable forms, including a multidimensional data structure, such as a 2D matrix or array with N × M elements, where N is the number of transmitting antennas and M is the number of receiving antennas. Using the in Fig. The vehicle radar system 10 shown in section 1 serves as an illustrative example of the receiving antenna response X. k This can be expressed as a multidimensional matrix or array with NxM = 3×4 = 12 virtual antenna elements, where each element is provided with position information in the form of azimuth and altitude information. In such an example, it would be unnecessary for the elements to also contain Doppler frequency shift f and / or range r information, since this position information would already be known from the selected Doppler range bin. It should be noted that the receive antenna response X kcan be represented or expressed in any number of other formats and is not limited to the exemplary matrix or arrangement described above (e.g., position information can be used in the form of Cartesian, polar and / or other coordinates instead of spherical coordinates).

[0038] In one scenario, only a single Doppler range bin can be selected (K = 1), indicating that only a single target object is within the radar's field of view. In other embodiments, a plurality of Doppler range bins can be selected, indicating that a plurality of target objects are within the radar's field of view. In such a case, multiple receive antenna responses X would be used. k received, including a first receiving antenna response X1, a second receiving antenna response X2, and so on. Each of these receiving antenna responses X kThis can therefore include azimuth, elevation, and / or other information about N×M virtual antenna elements. After the receiving antenna response X k Once the result has been received, the procedure continues with step 250 (200).

[0039] In some embodiments, each iteration of steps 210 to 248 corresponds to a single receive radar frame j, such that multiple repetitions or cycles of this group of steps result in multiple receive antenna responses X k,j lead to, where k represents the Doppler range bin and j the receive radar frame or cycle. Thus, the sequence of steps can be executed J times 210 to 248 times to obtain at least J number of receive antenna responses X. k,jto obtain, each of which is associated with a specific target object, as observed in a single receive radar frame. Furthermore, for each receive radar frame j (during each iteration of steps 210 to 248), the number of target objects K can vary, but information about at least one target object for each iteration of steps 210 to 248 is used to determine a receive antenna response X. k,j to generate. And the variable K j can represent the number of target objects for a given receiving radar frame j.

[0040] In one embodiment, steps 250 to 270 can be executed after a single iteration of steps 210 to 248 (J = 1). Alternatively, in other embodiments, steps 250 to 270 can be executed after a specific number of iterations of steps 210 to 248, such as after 10 iterations (J = 10), where at least 10 receive antenna responses X are recorded. k,jare obtained (i.e., 10 receive radar frames, each containing a receive antenna response for one or more target objects (K > 1)). In such an embodiment, the method can continue to return to step 210 until the required number of frames or cycles is reached, at which point the method can proceed to step 250. The number of frames, cycles, and / or iterations can vary and may depend on the current operating state and / or environment of the vehicle 100 or the vehicle radar system 10, or on the value of the uncertainty metrics derived from previous receive radar frames, to name just a few possibilities. Furthermore, when using the iterative cost optimization search process described herein, the number of iterations may not be known until the stopping conditions are met, which, for example, may occur during or after the last iteration in many cases.

[0041] In step 250, the procedure searches for new calibration matrices Ĉ1 and Ĉ2. These matrices may inherently be similar to the hypothetical calibration matrices C1 and C2, but may differ in that the new calibration matrices Ĉ1 and Ĉ2 are the matrices being sought, while the hypothetical calibration matrices C1 and C2 are those already known at the beginning of the procedure or those evaluated during the calibration search process. The number of calibration matrices sought can be equal to the number of dimensions used to form the beamforming images, which in the aforementioned example are two (e.g., a calibration matrix C1 for azimuth and a calibration matrix C2 for height). Thus, for example, a new calibration matrix C1 for azimuth and a new calibration matrix Ĉ2 for height can be sought.Various different search or optimization techniques can be used to obtain accurate and novel calibration matrices Ĉ1 and Ĉ2. For example, an iterative cost optimization search process implementing gradient descent techniques can be used, or a predefined calibration hypothesis testing procedure can be employed. Both processes generally involve searching for the hypothetical calibration matrix sets {C. 1,p , C 2,p}, which are resolved to the lowest uncertainty value or a sharper beamforming image, which is a function of the beamforming image and the hypothetical calibration matrices C 1,p , C 2,p is, where p is the index of the hypothetical calibration matrix sets. The hypothetical calibration matrices C 1,p , C 2,pThe matrices corresponding to the lowest uncertainty value can be selected or obtained as a result of the calibration search process (as described in steps 252-258), which is explained below. Based on the selected hypothetical calibration matrices C 1,p , C 2,p New calibration matrices Ĉ1 and Ĉ2 will be obtained, which can then be used for better calibration of the vehicle radar system 10.

[0042] As mentioned earlier, step 250 may involve the use of an iterative search process for cost optimization (e.g., a process implementing gradient descent techniques) or a predefined calibration hypothesis testing procedure. Both processes involve obtaining hypothetical calibration matrices C. 1,p , C 2,p (Step 252), Obtaining a beamforming image Y k,j,p using the new hypothesized calibration matrices C 1,p , C 2,p(Step 254), Derivation of an uncertainty metric(s) g k,j,p for the hypothetical calibration matrices C 1,p , C 2,p (Step 256) and selecting hypothetical calibration matrices C 1,p , C 2,p , which have the best beamforming image resolution (e.g., the lowest uncertainty metric g) k,j,p ) (Step 258). The iterative cost optimization search process can iteratively determine the next hypothetical calibration matrices C. 1,p , C 2,p derive, while the predefined calibration hypothesis test procedure can retrieve predefined hypothetical calibration matrices from memory. In general, however, the calibration search procedure (e.g., steps 252 to 256) P can be performed multiple times to derive uncertainty metrics. k,j,p for all hypothetical calibration matrix sets {C 1,p , C 2,p} to derive, (where p is the index of the iteration of steps 252-256 such that p is the hypothetical calibration matrix set {C 1,p , C 2,p} indicates which is being tested). Thus, steps 252-256 lead to an uncertainty metric g. k,j,p for each beam shaping pattern Y k,j,p , which, using the set of hypothesized calibration matrix sets {C 1,p , C 2,p} as well as the receiving antenna responses X k,j is obtained. Then, after the P iterations of steps 252-256, hypothetical calibration matrices C are obtained. 1,p , C 2,p based on the uncertainty metrics g k,j,p selected and used to obtain new calibration matrices Ĉ1 and Ĉ2 (step 260). In some embodiments, the hypothetical calibration matrices C 1,p , C 2,p , which has the lowest uncertainty metric g k,j,pare assigned when the new calibration matrices Ĉ1 and Ĉ2 are selected, and in further embodiments, a weighting function is used to assign the new calibration matrices Ĉ1 and Ĉ2 based on the hypothesized calibration matrices C. 1,p , C 2,p to obtain the lowest uncertainty metric g k,j,p are assigned.

[0043] With reference to Fig. Figure 3 shows a detailed flowchart illustrating various inputs and outputs in one embodiment of the calibration search process. The reference numerals correspond to those in Fig. 2, so that the following description of steps 250 to 270 of procedure 200 with reference to the two Fig. 2 and Fig. 3 is carried out.

[0044] In step 252, an initial or first set of hypothesized calibration matrices C is calculated. 1,1 and C 2,1obtained in one of several possible ways. As explained in more detail, step 252 is likely to be performed multiple times (i.e., several iterations during procedure 200). In the initial or first iteration of step 252, the procedure may obtain an initial set of hypothetical calibration matrices C. 1,1 and C 2,1 obtained by retrieving them from the memory of the vehicle radar system 10 or elsewhere; in subsequent iterations of step 252, the procedure for obtaining hypothetical calibration matrices is likely to be different. For example, the first set of hypothetical calibration matrices C 1,1 and C 2,1 These could be predetermined calibration matrices with standard calibration information stored in the memory of the vehicle radar system 10. In another example, the first set of hypothetical calibration matrices could be C 1,1 and C 2,1These could be previously stored calibration matrices, possibly those derived from calibration information generated during an earlier iteration of procedure 200. In any case, the initial hypothetical calibration matrices C 1,1 and C 2,1 obtained and as the first entries in a hypothetical calibration matrix set {C 1,p , C 2,p} are stored, where p represents the number of iterations or cycles of the procedure. It should be noted that the term “hypothetical calibration matrix set {C 1,p , C 2,p}“ interchangeable with the term “multiple hypothetical calibration matrices” used C 1,1 - C 1,p and C 2,1 - C 2,p. is used. Each of these matrices contains calibration information relating to the vehicle radar system (e.g., these matrices may contain calibration information in the form of altitude and / or azimuth calibration data for attempting to correct signals from the receiving antenna array, but calibration information may include any data relating to the attempted calibration of the vehicle radar system).

[0045] In subsequent iterations of steps 252-256, new hypothetical calibration matrices can be obtained by using a cost optimization technique with an uncertainty metric cost function g(Y). k,j,p , C 1,p , C 2,p ) is carried out as explained. In one embodiment, an iterative calibration search process implementing gradient descent techniques can be used. For example, gradient descent techniques can be performed using the hypothetical calibration matrices C 1,p , C2,p the present iteration p together with the uncertainty metric cost function g(Y k,j,p , C 1,p , C 2,p ) are performed to generate hypothetical calibration matrices C 1,p+1 , C 2,p+1 to obtain values ​​that will be used in the next iteration (p+1), if any. When using gradient descent (or another cost optimization technique), the number of iterations P may depend on how quickly an optimized local minimum value (e.g., uncertainty metric) is obtained or when certain halting conditions are met, as explained in more detail below.

[0046] In other embodiments, a predefined calibration hypothesis test procedure can be used to test a variety of uncertainty metrics. 1,1,1 up to g K,J,P for each of the P number of hypothetical calibration matrices C 1,p , C 2,pand to obtain each of the target objects (or antenna receive responses) k from each receive radar frame j. In this embodiment, the P number of hypothetical calibration matrix sets {C 1,1 , C 2,1} - {C 1,P , C 2,P} stored in the memory of the vehicle radar system 10 or elsewhere and can then be retrieved from memory when step 252 is reached. When performing the specified calibration hypothesis test procedure, step 252 would involve retrieving the hypothetical calibration matrix set {C 1,p , C 2,p} for the present iteration p. At the end of step 252, a hypothetical calibration matrix set {C 1,p , C 2,p} generated, identified and / or otherwise obtained. The procedure then continues at step 254.

[0047] In step 254, a beam shaping procedure is performed to obtain a beam shaping image. The beam shaping procedure may involve numerous substeps, including the multiplication of a hypothetical calibration matrix set {C 1,p , C 2,p} with a receiving antenna response X k,j , to achieve a calibrated array response Z k,j,p (Substep 254-1) to obtain, obtaining beam-shaping matrices F1 and F2 (substep 254-2), and calculating a beam-shaping pattern Y k,j,p (Substep 254-3). As mentioned earlier, the procedure can be performed for more than one receiving radar frame j, each of which can contain one or more target objects k. In this case, the hypothetical calibration matrix {C 1,p , C 2,p} with each of the receiving antenna responses X 1,1 up to X K,J multiplied to create a variety of calibrated array reactions Z 1,1,1 to Z K,J,Pto obtain, as in block 254-1 of Fig. 3 shown. Each calibrated array response Z k,j,p represents a receiving antenna response X k,j which, using a hypothetical calibration matrix C 1,p , C 2,p was calibrated. The first hypothetical calibration matrix C 1,p can represent calibration values ​​for azimuth information and the second hypothetical calibration matrix C 2,p It can represent calibration values ​​for altitude information. As mentioned above, although Procedure 200 provides an example of calibrating the vehicle radar system 10 for two dimensions (azimuth and altitude) and subsequently for obtaining two-dimensional information about target objects, the procedure is not limited to two dimensions. For example, the procedure can be adapted to calibrate for one or three dimensions and to obtain one- or three-dimensional information.

[0048] In step 254-1, one or more calibrated array reactions Z are selected. 1,1p to Z K,J,p for each iteration p. In one embodiment, each of the receiving antenna responses X is k,j with the hypothetical calibration matrices C 1,p , C 2,p multiplied to obtain a calibrated array response Z k,j,p to obtain using the following equation: Zk,j,p=C1,pXk,jC2,p

[0049] Equation 1 can be performed for each target (represented by the index combination k,j, where k is the index for the target of the j-th receive radar frame (or the iteration of steps 210 to 240)) to obtain at least J calibrated array responses Z 1,1,p to Z K,J,p (since K ≥ 1). After the calibrated array reactions Z 1,1,p to Z K,J,p Once the beam shaping images have been obtained, they can be obtained.

[0050] In step 254-2, the beam-shaping matrices F1 and F2 are obtained. In one embodiment, F1 can be a beam-shaping matrix applied to the vertical antenna elements of the altitude information array, and F2 can be a beam-shaping matrix applied to the vertical antenna elements of the azimuth information array. In some embodiments, the beam-shaping matrices can be stored in the memory of the vehicle radar system 10, such as in the radar control module 16. After obtaining the beam-shaping matrices F1 and F2, the method can proceed to step 254-3, where beam-shaping images are calculated.

[0051] In step 254-3, a beamforming image can be obtained for each calibrated array response for the current iteration. The beamforming image Y k,j,pThus, represents the beamforming pattern for a given target object k of the j-th receiving radar frame (or a given Doppler range bin k of the j-th receiving radar frame), and p corresponds to the index of the hypothetical calibration matrices C. 1,p , where C 2,p The beamforming pattern Y is checked during the present iteration of steps 252-256. k,j,p can include two-dimensional information, including azimuth and height information, where in such a case the beamforming image is called the 2D beamforming image Y k,j,p can be described. As mentioned above, in other embodiments the calibration and / or beam shaping process can include information of a single dimension, in this case a 1D beam shaping image Y. k,j,p can be obtained.

[0052] In one embodiment, the 2D beam shaping images Y 1,1,p up to Y K,J,pobtained using the following equation: Yk,j,p=F1 Zk,j,p F2 where F1 is a beamforming matrix applied to the vertical antenna elements of the 2D array for height information (hereinafter referred to as the height beamforming matrix F1), F2 is the beamforming matrix applied to the horizontal elements for azimuth resolution (hereinafter referred to as the azimuth beamforming matrix), Y k,j,p is the 2D beamforming pattern for the receiving antenna response X k,j , which uses the hypothetical calibration matrices C 1,p , C 2,p calibrated (where k is the index of the target object within the receiving radar frame j, and p is the index of the hypothetical calibration matrices C). 1,p , C 2,p (is the one being tested). For example, in a 2D planar, uniformly spaced array, the calibrated array response Z can be k,j,pLet A contain the number of rows and B the number of columns, such that each matrix element represents a virtual antenna element. Thus, in this example, the height beam shaping matrix F1 can be the Bartlett beam shaping matrix, which maps to the columns of virtual antennas in the 2D array of Z. k,j,p is applied. Similarly, the azimuth beamforming matrix F2 can be the Bartlett beamforming matrix applied to the rows of virtual antenna elements in the 2D array of Z. k,j,p is applied.

[0053] In the case of a one-dimensional beam shaping pattern (e.g., where A = 1 or B = 1), the dot product of the height beam shaping matrix F1 and the first calibration matrix C yields 1,p A full scale value of 1 (e.g., is eliminated) leaves only the azimuth beam shaper matrix F2 and the second calibration matrix C. 2,p What remains is therefore only azimuth information that is incorporated into the 1D beamforming image Y. k,j,precorded. In such a case, the remaining steps of procedure 200 can be used to improve the second calibration matrix C. 2,p They can be used. After obtaining the beam shaping images, procedure 200 continues with step 256.

[0054] In step 256, uncertainty metrics are obtained. As in Fig. As shown in Figure 3, step 256 can comprise three sub-steps: 256-1, 256-2, and 256-3. In sub-step 256-1, an uncertainty metric cost function g(Y) is defined. k,j,p , C 1,p , C 2,p ) on each beamforming image Y k,j,p applied, each of which corresponds to a hypothetical calibration matrix set {C 1,p , C 2,p} corresponds. After calculating each of these values ​​(substep 256-1), an overall uncertainty metric g can be obtained. p for the hypothetical calibration matrix set {C 1,p , C 2,p} can be derived (substep 256-2). Afterwards, as shown in substep 256-3, the procedure can return to step 252 if a further iteration is desired, for example if the holding conditions of the iterative cost optimization search process are not met; otherwise, the procedure can proceed to step 258.

[0055] In substep 256-1, the uncertainty metrics can be replaced by g k,j,p and can be represented using the uncertainty metric cost function g(Y) k,j,p , C 1,p , C 2,p ) can be determined. The uncertainty metric cost function g(Y) k,j,p , C 1,p , C 2,p ) can be various types of radar uncertainty algorithms or functions that affect the uncertainty of the beamforming image Y k,j,pdetermine. For example, edge detection techniques can be applied to determine the sharpness of the beamforming image around an edge. Larger intensity changes around an edge may indicate a sharper image than those with smaller intensity changes. In some embodiments, a high-pass filter can be used to determine the sharpness or blurriness of the beamforming image Y. k,j,p to determine. It should be noted that the uncertainty metric(s) may include all information generally representative of the quality or resolution of the beamforming image, including, but not limited to, the blurriness, sharpness, and / or resolution of the beamforming image. Various other techniques known to those skilled in the art may be used to determine the resolution of the beamforming image Y. k,j,p be used.

[0056] In substep 256-2, an overall uncertainty metric g can be calculated. p for the hypothetical calibration matrix set {C 1,p , C 2,p} can be calculated. For example, the uncertainty metrics for a specific iteration p can be summed to obtain an overall uncertainty metric g. p to obtain. Thus, where p = 1, the uncertainty metrics g can be obtained. 1,1,1 up to g K,1,1 These values ​​are summed to obtain the overall uncertainty metric g1. This can be done for any set of hypothetical calibration matrices C. 1,p , C 2,p to determine the P total uncertainty metrics g1 to g P to obtain. For example, the following equation can be used to obtain the total uncertainty metric g. p To obtain the following for a given iteration p: gp=∑j=1J∑k=1Kjgk,j,p where g p the total uncertainty metric for the hypothetical calibration matrix set {C 1,p , C 2,p} is, J is the number of received radar frames (with j as the index), and K j The number of target objects (or Doppler range bins) for the receiving radar frame j is (with k as the index). However, as explained below, other embodiments cannot omit the step of calculating an overall uncertainty metric g. p include, but instead the uncertainty metrics g k,j,p use to determine which hypothetical calibration matrix set is available for selection.

[0057] In substep 256-3, the procedure 200 can determine whether to perform another iteration of the calibration search process. In an embodiment where a cost optimization technique, such as gradient descent, is used, the procedure 200 can return to step 252 if the halt conditions are satisfied or after a predetermined number of iterations have been performed. For example, the halt conditions may include cases where the gradient used to determine the uncertainty metric g k,j,p surrounds, is flat (or at a local minimum, as determined by comparing the gradient with a gradient stop threshold) or in which a previous iteration has resulted in a lower uncertainty metric (e.g. g k,j,p-1 ) than the uncertainty metric g k,j,pof the current iteration p. When using the predefined calibration hypothesis verification process, it can be determined that procedure 200 should return to step 252 if one or more predefined, hypothetical calibration matrix sets still need to be checked, which determine the receiving antenna responses X k,j use.

[0058] If it is determined that procedure 200 returns to step 252, then a next set of hypothetical calibration matrices C is used. 1,p+1 , C 2,p+1 for use in the next iteration of steps 254 and 256. For example, the uncertainty metric cost function g(Y) can be obtained. k,j,p , C 1,p , C 2,p ) can be used with gradient descent techniques to find the next hypothetical calibration matrix set {C 1,p+1 , C 2,p+1} to obtain. In one embodiment, the following function can be used to obtain the next hypothetical calibration matrix set {C 1,p+1 , C2,p+1} to obtain: C1,p+1=C1,p−γ1∇1g(Yk,j,p,C1,p,C2,p) C2,p+1=C2,p−γ2∇2g(Yk,j,p,C1,p,C2,p) where γ1, γ2 are the step sizes, ∇1g(Y k,j,p , C 1,p , C 2,p ) the gradient uncertainty metric with respect to C 1,p are and ∇2g(Y k,j,p, C 1,p , C 2,p ) the gradient uncertainty metric with respect to C 2,p is. Thus, the next hypothetical calibration matrix set {C 1,p+1 , C 2,p+1} obtained for the purposes of step 252.

[0059] In other embodiments, such as when using the predefined calibration hypothesis verification process, the subsequent execution of step 252 can obtain the next calibration set of hypothetical calibration matrices C. 1,p+1 , C 2,p+1by simply calling up the next set of calibration hypotheses to be tested from memory, such as from the memory of the vehicle radar system 10 or from the memory of another VSM of the vehicle 100 (e.g., a storage device of the on-board computer 130). Thus, steps 252 to 256 are executed P times until an uncertainty metric g k,j,p For each combination, the following is obtained: hypothetical calibration matrices C 1,p , C 2,p as well as each of the receiving antenna responses X k,j (e.g., assuming that K = 1 for each received radar frame j, then a total of JxP iterations are performed for steps 252 to 256). After obtaining all uncertainty metrics g 1,1,1 up to g K,J,P The procedure can be continued with step 258.

[0060] In step 258, the hypothetical calibration matrices C are 1,p , C 2,pselected. In one embodiment, this can be the selection of the hypothetical calibration matrix set {C 1,p , C 2,p} include the one with the lowest total uncertainty metric g p is connected. In another embodiment, this can be the selection of the hypothetical calibration matrix set {C 1,p , C 2,p} include which of the lowest individual total uncertainty metric g k,j,p is assigned. In both cases, the procedure generally attempts to assign the hypothetical calibration matrix {C 1,p , C 2,p} to select the one that produces the sharpest or highest-quality image (i.e., the image with the least blur or the greatest sharpness). The selected calibration matrix set {C 1,p , C 2,p} and / or the uncertainty metrics (e.g. g) p , g k,j,p) can be stored in memory, such as in the memory of the vehicle radar system 10 or in the memory of another VSM of the vehicle 100 (e.g., a storage device of the on-board computer 130). The procedure 200 then continues with step 260.

[0061] In step 260, the selected calibration matrices C can be 1,p , C 2,p They can be stored or used to update the first calibration matrices C1 and C2. In one embodiment, the selected calibration matrices C 1,p , C 2,p simply the initial calibration matrices C1 and C2 (thus the selected calibration matrices C 1,p , C 2,p the new calibration matrices Ĉ1 and Ĉ2). In a further embodiment, the selected calibration matrices C 1,p , C 2,p weighted (e.g., multiplied by a weighting factor) based on how many receiving antenna responses X k,jare obtained based on the number of received radar frames and / or based on how often the procedure 200 was previously used to update the calibration matrices. Furthermore, in some embodiments, the selected calibration matrices C 1,p , C 2,p and / or the initial calibration matrices C1 and C2 are assigned a confidence value, with the initial calibration matrices C1 and C2 being updated based on the confidence values ​​assigned to the respective matrix sets. Using these weighting techniques, the selected calibration matrices C 1,p , C 2,p The initial calibration matrices C1 and C2 are used to obtain the new calibration matrices Ĉ1 and Ĉ2. The new calibration matrices Ĉ1 and Ĉ2 can then be stored in the memory of the vehicle radar system 10 or in the memory of another VSM of the vehicle 100. Procedure 200 continues with step 270.

[0062] In step 270, the new calibration matrices Ĉ1 and Ĉ2 are then applied to the receive antenna responses X to calibrate one or more of the receive antennas. In one embodiment, the vehicle radar system 10 can use the new calibration matrices Ĉ1 and Ĉ2 to obtain beamforming images that correspond to the receive antenna responses X. k,j These correspond to the calibration matrices used in the calibration search process. In further embodiments, the new calibration matrices Ĉ1 and Ĉ2 are then applied to future (or other) receiving antenna responses X to obtain new beamforming images Ŷ. In any case, the new beamforming images Ŷ can then be used to obtain a target parameter, such as the target's altitude or azimuth, as well as other information.

[0063] In step 280, target parameters relating to a target object can be calculated using the beamforming image Ŷ. Various Doppler frequency shift equations, as well as various other pieces of information, can be used to determine the altitude or azimuth of the target object using the beamforming image Ŷ. For example, angle of incidence information, target object velocity, range, and / or other spatial information can be determined by processing received signals at the radar system 10 using the novel calibration matrices Ĉ1 and Ĉ2 and / or the beamforming image Ŷ. In one embodiment, MIMO angular resolution techniques can be used to determine the angle between the radar system 10 and the target object 18.

[0064] Once the altitude or azimuth (or other information) of the target object has been calculated, it can be sent to other vehicle system modules (VSMs), such as the ECU 120, the on-board computer 130, and / or other VSMs 140. In addition, speed and range information, along with the target object's azimuth and altitude, can be sent to other VSMs. This information can be used for various vehicle operations, such as notifying a driver or occupants and / or executing various semi-autonomous or fully autonomous vehicle functions. The procedure 200 can terminate at this point or be looped back for further execution.

[0065] As used in this specification and the claims, the terms "for example," "e.g.," "such as," "as," and "equal / similar," as well as the verbs "comprise," "have," "include," and their other verb forms, when used in conjunction with a list of one or more components or other elements, are each to be interpreted as open-ended, meaning that the list may not be considered an exception to other, additional components or elements. Other terms are to be interpreted in their broadest reasonable sense unless used in a context that requires a different interpretation. Additionally, the expression "and / or" is understood as an inclusive OR. As an example, the expression "A, B, and / or C" includes: "A"; "B"; "C"; "A and B"; "A and C"; "B and C"; and "A, B, and C."

Claims

[1] Method for calibrating a vehicle radar system, wherein the vehicle radar system comprises a transmitting antenna arrangement with a plurality of transmitting antennas and a receiving antenna arrangement with a plurality of receiving antennas, the method comprising the following steps: Transmitting a large number of signals using the transmitting antenna array; Receive a multitude of signals with the receiving antenna arrangement; Receiving a multitude of antenna responses based on the multitude of received signals, each of the antenna responses containing positional information regarding a target object; Applying a multitude of hypothetical calibration matrices to each of the multitude of receiving antenna responses to obtain a multitude of calibrated array responses, each of the multitude of hypothetical calibration matrices incorporating calibration information relating to the vehicle radar system; Applying at least one beam-shaping matrix to each of the multitude of calibrated array reactions to obtain a multitude of beam-shaping images; Deriving at least one uncertainty metric for each of the plurality of beamforming images, wherein each of the plurality of uncertainty metrics is representative of a beamforming image resolution; Selecting at least one of the multitude of hypothetical calibration matrices based on the multitude of uncertainty metrics, wherein the selected hypothetical calibration matrix is ​​associated with the uncertainty metric of the one with the best beamforming image resolution; and Using the selected hypothetical calibration matrix to calibrate the vehicle radar system. [2] The method of claim 1, further comprising the following steps: Generating a modulated signal US1 with a transmitter; Mixing the modulated signal MS1 with a code sequence C1 to C N to produce the multitude of transmitted signals Tx1 to Tx N , where the code sequence C1 to C N includes at least one separate code for each of the plurality of transmitting antennas in the transmitting antenna arrangement, wherein the mixing step is performed before the transmitting step; and Separation of the multitude of received signals Rx1 to Rx M with the code sequence C1 to C N to generate a large number of separate received signals S 1,1 to S M,N , where the separate received signals S 1,1 to S M,N at least N number of separate signals for each of the plurality of receiving antennas in the receiving antenna arrangement, wherein the demixing step is performed after the receiving step; wherein the step of receiving further includes receiving a multitude of receiving antenna responses X 1,1 up to XK,J based on the multitude of separate received signals S 1,1 to S M,N includes, which in turn are based on the multitude of received signals Rx1 to Rx M based. [3] The method of claim 1, wherein the receiving step further comprises performing a pre-beam shaping process which separates the plurality of received signals Rx1 to Rx M into a multitude of separate received signals S 1,1 to S M,N includes, and wherein the step of disconnecting is carried out from a receiver that is part of the vehicle radar system. [4] Method according to claim 3, wherein the receiving step further comprises performing a pre-beam shaping process which filters the separated received signals S 1,1 to S M,Nbased on a Doppler frequency shift f and / or a range r and is assigned to the filtered signals of one or more Doppler range bins. [5] Method according to claim 4, wherein the receiving step further comprises performing a pre-beam shaping process which includes determining whether one or more Doppler range bins adequately indicate the presence of a target object and, if so, using the Doppler range bin to select the plurality of receiving antenna responses X 1,1 up to X K,J to obtain. [6] Method according to claim 1, wherein the first application step further comprises retrieving a first set of hypothetical calibration matrices C 1,1 and C 2,1 from the electronic memory in the vehicle radar system using the first set of hypothetical calibration matrices C 1,1 and C 2,1includes one or more subsequent sets of hypothetical calibration matrices C 1,p and C 2,p to generate, and use the following sets of hypothetical calibration matrices C 1,p and C 2,p , to the multitude of hypothetical calibration matrices C 1,1 up to C 1,p and C 2,1 up to C 2,p to generate responses to the multitude of receiving antenna reactions X 1,1 up to X K,J be applied. [7] Method according to claim 1, wherein the first application step further comprises the application of each of the plurality of receiving antenna responses X 1,1 up to X K,J to each of the multitude of hypothetical calibration matrices C 1,1 up to C 1,p and C 2,1 up to C 2,p includes a variety of calibrated array reactions Z 1,1,1 to Z k,j,pto obtain, whereby the first application step is performed by the vehicle radar system according to the following equation: Zk,j,p=C1,pXk,jC2,p where the hypothetical calibration matrices C 1,1 up to C 1,p Altitude calibration information includes the hypothetical calibration matrices C 2,1 up to C 2,p Azimuth calibration information is included, k represents a Doppler range bin, j represents a receive radar frame, and p represents a calibration iteration. [8] Method according to claim 1, wherein the second application step further comprises applying the first beam shaper matrix F1 and the second beam shaper matrix F2 to each of the plurality of calibrated array reactions Z 1,1,1 to Z K,J,P includes a variety of beam shaping images Y 1,1,1 up to Y K,J,P , to obtain, and the second application step is performed by the vehicle radar system according to the following equation: Yk,j,p=F1 Zk,j,p F2 where the first beamformer matrix F1 contains height information regarding a target object, the second beamformer matrix F2 contains azimuth information regarding the target object, k represents a Doppler range bin, j represents a receive radar frame, and p represents a calibration iteration. [9] Method according to claim 1, wherein the vehicle radar system continues to derive a plurality of uncertainty metrics until at least one holding condition is satisfied, wherein the at least one holding condition is selected from a plurality of holding conditions, which includes: a condition if a gradient associated with an uncertainty metric is flat, a condition if a gradient associated with an uncertainty metric is at a local minimum, or a condition if a previous iteration has resulted in an uncertainty metric with better beamforming image resolution. [10] The method of claim 1, wherein the derivation step further comprises calculating an overall uncertainty metric g p for a given set of hypothesized calibration matrices C 1,p , C 2,p includes the total uncertainty metric g p representative of an overall value for the given set of hypothetical calibration matrices C 1,p , C 2,p in relation to the beamforming image resolution, and wherein the selection step further comprises selecting the given set of hypothetical calibration matrices C 1,p , C 2,p includes which of the total uncertainty metric g p is assigned the best beamforming image resolution. [11] Method according to claim 1, wherein the application step further comprises obtaining a first new calibration matrix Ĉ1 and a second new calibration matrix Ĉ2 based on the selected hypothetical calibration matrix, calculating a new beamforming image Ŷ using the first new calibration matrix Ĉ1 and the second new calibration matrix Ĉ2, and determining one or more target parameters for a target object by evaluating the new beamforming image Ŷ, wherein the application step is performed by the vehicle radar system. [12] Vehicle radar system, wherein the vehicle radar system is mounted on a carrier vehicle and comprises the following: a transmitter; a transmitting antenna arrangement with a plurality of transmitting antennas coupled to the transmitter, wherein the transmitting antenna arrangement transmits a plurality of transmitting signals; a receiving antenna arrangement with a plurality of receiving antennas, wherein the receiving antenna arrangement receives a plurality of received signals; and a receiver coupled to the receiving antenna arrangement, in which the receiver is configured to: Receiving a multitude of antenna responses based on the multitude of received signals, each of the antenna responses containing positional information regarding a target object; Applying a multitude of hypothetical calibration matrices to each of the multitude of receiving antenna responses to obtain a multitude of calibrated array responses, each of the multitude of hypothetical calibration matrices incorporating calibration information relating to the vehicle radar system; Applying at least one beam-shaping matrix to each of the multitude of calibrated array reactions to obtain a multitude of beam-shaping images; Deriving at least one uncertainty metric for each of the plurality of beamforming images, wherein each of the plurality of uncertainty metrics is representative of a beamforming image resolution; Selecting at least one of the multitude of hypothetical calibration matrices based on the multitude of uncertainty metrics, wherein the selected hypothetical calibration matrix is ​​associated with the uncertainty metric of the one with the best beamforming image resolution; and Using the selected hypothetical calibration matrix to calibrate the vehicle radar system.

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