Method for determining at least one intrinsic velocity of at least one object in an environment of a motor vehicle, computer program product, computer-readable storage medium, and detection device

Abstract

The invention relates to a method for determining an intrinsic velocity of an object (16) in an environment, the method comprising the steps of: transmitting transmission signals and receiving reception signals (10) reflected from the object (16) over at least two time cycles; identifying a transmit antenna (4) and at least two receive antennas (5); generating a first amplitude map (17) of the environment for a first time step and generating a second amplitude map (17) for a second time step; identifying a first amplitude associated with the object (16) in the first amplitude map (17) and a second amplitude associated with the object (16) in the second amplitude map (17); determining a first position of the object (16) in the first amplitude map (17) and determining a second position of the object (16) in the second amplitude map (17); and determining the intrinsic velocity of the object (16) depending on the first position and the second position. The invention also relates to a computer program product, to a computer-readable storage medium, and to a detection device (2).

Description

[0001] Description

[0002] Method for determining at least one intrinsic velocity of at least one object in the vicinity of a motor vehicle, computer program product, computer-readable storage medium and detection device

[0003] The invention relates to a method for determining at least one inherent velocity of at least one object in the vicinity of a motor vehicle by means of a detection device of the motor vehicle according to the applicable claim 1. Furthermore, the invention relates to a corresponding computer program product, a computer-readable storage medium and a detection device.

[0004] For automated driving, the safest possible environmental perception is essential. This is achieved by capturing the environment using sensors such as radar, LiDAR, and cameras. A comprehensive 360-degree 3D mapping of the environment is particularly important, ensuring that all static and dynamic objects are detected. LiDAR plays a crucial role in redundant, robust environmental perception, as this sensor type can precisely measure distances and also be used for classification. However, these sensors are expensive and complex to design. 360-degree 3D environmental perception is especially problematic, as it requires either numerous smaller individual sensors, typically using many individual light sources and detector elements, or the installation of large sensors. Furthermore, LiDAR systems are susceptible to weather conditions such as rain, fog, or direct sunlight.

[0005] Radar sensors have been established in the mobile sector for years, reliably and consistently delivering data in all weather conditions. Even poor visibility conditions such as rain, fog, snow, dust, and darkness hardly affect their detection reliability. However, their resolution has been limited so far; commercially available radars currently in use have a resolution of approximately seven degrees. To meet the requirements for Levels 4 and 5 of automated driving with safe driving functions, radar sensors must deliver three-dimensional images with a fine resolution of 0.1 degrees and even finer, with high insensitivity to interference from their environment. This cannot be achieved with conventional radar technology, as the resolution of such systems is too low.

[0006] German patent DE 102019 103684 A1 describes a vehicle system, such as a multiple-input multiple-output (MIMO) radar system, for estimating a Doppler frequency shift and a method for its use. In one example, a modulated signal is mixed with an orthogonal code sequence and transmitted by a transmitting antenna array with a plurality of transmitting antennas. The signals reflect off a target object and are received by a receiving antenna array with a plurality of receiving antennas. Each of the received signals that likely contains a Doppler frequency shift is processed and mixed with a series of frequency shift hypotheses designed to compensate for the Doppler frequency shift and result in a series of correlation values.The frequency shift hypothesis with the highest correlation value is selected and used to correct the Doppler frequency shift in order to obtain more accurate target parameters, such as velocity.

[0007] DE 102017 110 063 A1 relates to a radar system for detecting the surroundings of a moving object, in particular a vehicle and / or a transport device, such as a crane, wherein the system is mounted or mountable on the moving object, wherein the radar system comprises at least two non-coherent radar modules with at least one transmitting and at least one receiving antenna, wherein the radar modules are arranged or can be arranged distributed on the moving object, wherein at least one evaluation unit is provided which is configured to process transmitting and receiving signals from the radar modules into modified measurement signals such that the modified measurement signals are coherent to each other.

[0008] The object of the present invention is to provide a method, a computer program product, a computer-readable storage medium and a detection device by means of which at least the intrinsic velocity of an object within the environment of a motor vehicle can be determined in an improved manner.

[0009] This problem is solved by a method, a computer program product, a computer-readable storage medium, and a recording device according to the independent claims. Advantageous embodiments are specified in the dependent claims.

[0010] One aspect of the invention relates to a method for determining at least one intrinsic velocity of at least one object in the vicinity of a motor vehicle by means of a detection device on the motor vehicle. The detection device is provided with a plurality of transmitting antennas and a plurality of receiving antennas on the motor vehicle. The transmitting antennas send signals into the vicinity, and the receiving antennas receive signals reflected from the at least one object over at least two time cycles. At least one transmitting antenna and at least two receiving antennas locally associated with the transmitting antennas are identified by the electronic processing unit as a function of the transmitted and received signals.The electronic computer generates a first amplitude map of the environment based on the received signals for a first magazine and a second amplitude map based on the received signals for a second magazine following the first. The amplitude map is generated specifically based on the identified transmitting antenna and its associated receiving antennas. The electronic computer then identifies a first amplitude associated with the object in the first amplitude map and a second amplitude associated with the object in the second amplitude map.The first position of the object in the first amplitude map is determined as a function of the first amplitude, and a second position of the object in the second amplitude map is determined as a function of the second amplitude using the electronic computing device. The object's velocity is then determined as a function of the first and second positions using the electronic computing device.

[0011] This makes it possible to reliably determine the vehicle's own speed using the detection device.

[0012] It is preferable that the transmitting and / or receiving antennas are provided as so-called EPIC chips (electronically-photonically co-integrated chips). These are specifically photonic radar systems for increasing resolution, which rely on the co-integration of electronic and photonic components in a single semiconductor. The generation of a so-called FMCW signal (frequency modulated continuous wave), as well as all signal processing and evaluation, is carried out by a central station. Each transmitting and receiving module is formed from the electronically-photonically co-integrated chip (EPIC). Silicon photonics technology is used for the co-integration. This enables the monolithic integration of photonic components, high-frequency electronics, and digital electronics together on a single chip.The technical innovation of such a system lies particularly in the transmission of gigahertz signals using an optical carrier signal in the terahertz frequency range. The central station generates an optical carrier frequency, specifically in the terahertz range. The signal to be transmitted is modulated onto this carrier frequency with, for example, one-eighth of the radar frequency and sent to the antenna chips via optical phase correction. The signal is then frequency-multiplied eightfold on the antenna chips, allowing the radiation to be emitted in its original form. Signal detection occurs in reverse. All data is processed at the central station.

[0013] The large-area distribution of the EPIC chips on the vehicle surface and the coherent signal processing of the individual antennas allow for a refinement of the resolution down to 0.1 degrees. This is achieved primarily through the use of a so-called spars array. However, this results in a less favorable contrast between the amplitude of the main and side lobes, leading to increased signal processing costs for target detection and potentially introducing ambiguities.

[0014] The application of algorithms from the Synthetic Aperture Radar (SAR) family enables the construction of large virtual apertures by synthesizing numerous individual radar measurements along the paths traveled by a moving radar sensor. After applying the reconstruction step (SAR algorithm), the constructed virtual aperture possesses the same properties as an equivalent physical aperture of the same dimensions. Essential for this construction step is adherence to the so-called Nyquist criterion, which, in the case of a synthetic aperture, relates to the spatial separation of successive individual measurements.

[0015] A As = -

[0016] 4 where As is the spatial distance between successive measurement positions and A is a wavelength of the carrier signal. A characteristic feature of applying SAR algorithms during the construction step is the orientation of the sensor perpendicular to the direction of movement of a carrier platform in order to cover the largest possible aperture.

[0017] Contrary to the conventional approach of creating a virtual aperture by moving a sensor, a virtual aperture can also be created by temporally synthesizing individual antennas arranged over a large area. A fundamental requirement for its applicability is coherent reception characteristics of the individual antennas and the Multiple Input Multiple Output (MIMO) principle. In particular, alternating transmission processes generate new virtual antennas whose positions cover the free space between the physically installed antennas, although their spatial distances from each other do not meet the Nyquist criterion.

[0018] Each virtual receiving antenna receives a signal after a transmission within one Ml MO cycle, but this signal is time-shifted relative to the transmitted signal. For the SAR algorithms to be applicable, the number of virtual receiving antennas is too small, so the missing signals must be compensated for using a model function with linear / non-linear prediction. The model signal, estimated based on the received signals, calculates a hypothetical received signal for those receiving antennas that would be required to meet the Nyquist criterion and thus for the SAR algorithms to be applicable.

[0019] To enable the applicability of SAR algorithms, compensation for interfering phase terms is advantageous before applying linear prediction. These phase terms arise from the propagation of the carrier system and the propagation of potential targets during a 1 M1 MO cycle. To compensate for these phase terms, both the range and range Doppler spectra are calculated based on the received signals from physical and virtual antennas. Using suitable detection algorithms, such as a CFAR threshold, potential targets are subsequently identified and their Doppler velocities calculated. Based on the determined Doppler velocities, a phase filter can be calculated that compensates for the Doppler phase term in each received signal. The starting point for applying the SAR algorithms is then a received signal without interfering phase terms.

[0020] Before reconstruction by the SAR algorithms, a model signal is determined for each distance gate with the same index, based on the received signals, using linear prediction. This model signal estimates the missing signal components required to satisfy the Nyquist equation. The result of this estimation forms the basis for the SAR reconstruction and corresponds to the data set of a fully populated antenna array.

[0021] According to the invention, based on an aperture of distributed radar antennas, ground velocity determination is provided by means of a group of spatially closely spaced receiving antennas within this aperture. Starting from the received signals of this antenna group, a virtual aperture signal is generated by linear projection over distance gates with the same index, and an amplitude map is determined by applying a backprojection algorithm.

[0022] The input data set consists of all received signals acquired within a transmission cycle. A transmission cycle comprises several consecutive MIMO cycles.

[0023] Within such a MIMO cycle, each transmitting antenna of the detection device emits a chirp that is received by all receiving antennas. Due to the time offset and the associated phase shifts, an antenna array of virtual antenna elements exists over the entire duration of a MIMO cycle.

[0024] First, all phase terms arising from the movement of the carrier platform or vehicle, or from the motion of all detected targets, can be compensated. Once these dynamic phase terms are removed, the received signal in a MIMO cycle comprises only phase terms attributable to the distance to potential targets. For determining ground speed, a group of spatially close receiving antennas is initially defined.

[0025] A transmitting antenna is then selected, whose transmit signal is received by this group of receiving antennas at constant time intervals (TMIMO).

[0026] In an optional step, those received signals from the range spectrum are identified that can be assigned to the previously defined transmitting antenna and the defined group of receiving antennas. Due to the preprocessing step for compensating the dynamic phase terms, the received signals consist exclusively of distance-dependent phase terms. To calculate an amplitude map by backprojection, compliance with the Nyquist criterion must be ensured. The arrangement of the receiving antennas can be so unfavorable that a reconstruction violating the Nyquist criterion would result in undesirable aliasing effects. To prevent this, a suitable model function is determined and calculated using linear prediction over all distance gates with the same index.

[0027] The estimated model function can be used to compensate for missing signal components due to a lack of receiving antennas. The result is a virtual aperture signal that satisfies the Nyquist criterion.

[0028] Starting with this virtual aperture signal, which represents a received signal from a comparable fully populated physical antenna, the amplitude map can be determined by backprojection in a further process step. This amplitude map compresses the received power in the azimuth and distance directions, so that a potential target has a large amplitude value.

[0029] If the described calculation steps are repeated for the next MIMO cycle, the position coordinates of potential targets change compared to the preceding MIMO cycle. This change in position is due to the intrinsic motion of these targets or the object itself. Since all velocity-related phase terms in the received signal have been compensated, the change in position corresponds to a new target distance and thus affects the corresponding phase term. The change in position coordinates is now visible within the amplitude map of the current MIMO cycle. If, in a further process step, the difference between the position coordinates of two consecutive MIMO cycles is calculated, the result is a direction vector that indicates the direction of motion of a target.

[0030] This direction vector indicates the direction of movement for each detected target within the amplitude map. Considering the relationship between the direction coordinates and the duration of a MIMO cycle (TMIMO) yields a motion vector. Calculating the magnitude of this motion vector gives the velocity, specifically the velocity over ground. The determined speed can then be transmitted to an environmental model, for example. Alternatively or additionally, control signals for a longitudinal acceleration device and / or lateral acceleration device of the vehicle can be generated based on the determined speed, in order to enable, for example, at least partially automated operation of the vehicle.

[0031] According to an advantageous embodiment, a distance spectrum is generated to identify the transmitting antenna and the associated receiving antennas. This distance spectrum is also referred to as a range spectrum. In particular, interfering phase terms can thus be compensated. Specifically, the range spectrum is calculated based on the received signals from physical and virtual antennas. These interfering phase terms can then be subsequently eliminated by the appropriate detection algorithms.

[0032] It is also advantageous to generate a distance Doppler spectrum to identify the transmitting antenna and the associated receiving antennas. This refers in particular to the range Doppler spectrum. This allows the corresponding phase terms to be compensated, resulting in an improved determination of the amplitude map.

[0033] In a further advantageous embodiment, it can be provided that a specific Doppler velocity in the distance spectrum is compensated. In particular, potential targets are subsequently identified and their Doppler velocities calculated. Based on a specific Doppler velocity, a phase filter can be calculated that compensates for the Doppler phase term in each received signal. This provides a starting point for the application of a SAR algorithm, thereby eliminating interfering phase terms.

[0034] It is also advantageous if the object's velocity can be determined based on a direction vector for the object in the amplitude map. In particular, the following formula can be used: The direction vector can be determined. The direction vector v rThis, in turn, also provides information about the vehicle's speed. Thus, it is possible to determine the vehicle's speed in a simple way.

[0035] Another advantageous embodiment involves taking the vehicle's own speed into account when determining the object's speed. For example, this allows for compensation of the vehicle's speed. This enables the compensation of corresponding phase terms, resulting in a more accurate determination of the object's speed. The vehicle's speed can be derived, for instance, from the vehicle's speedometer or navigation system. This allows for a highly precise determination of the object's speed.

[0036] Another advantageous design involves transmitting the object's determined velocity to an environmental model for the vehicle. This allows the vehicle to operate in a semi-automated or fully automated manner. Based on the object's determined velocity, the vehicle's longitudinal and / or lateral acceleration systems can then be adjusted accordingly, preventing collisions with the object at an early stage. Specifically, control signals for the vehicle's movement are generated based on the determined velocity. This velocity can then be further processed within an electronic vehicle guidance system. Precise determination of the object's velocity thus enables improved, at least semi-automated, operation of the vehicle.

[0037] In a further advantageous embodiment, it is provided that the individual velocities of at least two objects in the environment are determined essentially simultaneously. In particular, the individual velocities of a multitude of objects in the environment can be determined essentially simultaneously. Specifically, this allows for the determination of multiple objects and their positions within the amplitudes, and thus the determination of the individual velocities of the multitude of objects. This advantageously enables, for example, the implementation of semi-automated operation of a motor vehicle. The presented method is, in particular, a computer-implemented method.Therefore, a further aspect of the invention relates to a computer program product with program code means which, when the program code means are executed by the electronic computing device, cause it to perform a method according to the preceding aspect. Therefore, a further aspect of the invention also relates to a computer-readable storage medium containing the computer program product.

[0038] The invention further relates to a detection device for determining at least one inherent velocity of at least one object in the vicinity of a motor vehicle, comprising at least one transmitting antenna, at least two receiving antennas, and an electronic computing unit, wherein the detection device is configured to carry out a method according to the preceding aspect. In particular, the method is carried out by means of the detection device.

[0039] Furthermore, the invention also relates to a motor vehicle with a detection device according to the preceding aspect. The motor vehicle is at least partially or fully automated.

[0040] Advantageous embodiments of the process are to be regarded as advantageous embodiments of the computer program product, the computer-readable storage medium, the data acquisition device, and the motor vehicle. The data acquisition device and the motor vehicle possess tangible features to enable the corresponding process steps to be carried out.

[0041] An electronic vehicle control system, for example for partially automated operation, can be understood as an electronic system designed to drive a vehicle fully automatically or autonomously, in particular without requiring any intervention from a driver. The vehicle automatically performs all necessary functions, such as steering, braking, and / or acceleration maneuvers, monitoring and recording road traffic, and reacting accordingly. Specifically, the electronic vehicle control system can implement a fully automatic or fully autonomous driving mode of the motor vehicle according to Level 5 of the SAE J3016 classification. An electronic vehicle control system can also be understood as an advanced driver assistance system (ADAS), which supports the driver during partially automated or semi-autonomous driving.In particular, the electronic vehicle guidance system can implement a partially automated or semi-autonomous driving mode according to levels 1 to 4 of the SAE J3016 classification. Here and in the following, "SAE J3016" refers to the corresponding standard in the April 2021 version.

[0042] At least partially automated vehicle control can therefore include driving the vehicle in accordance with a fully automated or fully autonomous driving mode of Level 5 according to SAE J3016. At least partially automated vehicle control can also include driving the vehicle in accordance with a partially automated or semi-autonomous driving mode according to Levels 1 to 4 of SAE J3016.

[0043] The at least one control signal can be provided, for example, to one or more actuators of the motor vehicle, including, for example, one or more brake actuators and / or one or more steering actuators and / or one or more drive motors of the motor vehicle. The one or more actuators can influence the longitudinal and / or lateral steering of the motor vehicle in order to steer the motor vehicle at least partially automatically.

[0044] The assistance information can be output via a vehicle output device, such as a display and / or an audio output system and / or a haptic output system.

[0045] A computing unit / electronic computing device can be understood, in particular, as a data processing device containing a processing circuit. The computing unit can therefore process data to perform arithmetic operations. This may also include operations to perform indexed access to a data structure, such as a lookup table (LUT).

[0046] The computing unit may, in particular, contain one or more computers, one or more microcontrollers, and / or one or more integrated circuits, for example, one or more application-specific integrated circuits (ASICs), one or more field-programmable gate arrays (FPGAs), and / or one or more systems on a chip (SoCs). The computing unit may also contain one or more processors, for example, one or more microprocessors, one or more central processing units (CPUs), one or more graphics processing units (GPUs), and / or one or more signal processors, in particular one or more digital signal processors (DSPs). The computing unit may also include a physical or virtual array of computers or other units of the aforementioned type.

[0047] In various embodiments, the computing unit includes one or more hardware and / or software interfaces and / or one or more storage units.

[0048] A storage unit can be volatile data storage, for example as dynamic random access memory (DRAM) or static random access memory (SRAM), or as non-volatile data storage, for example as read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), flash memory or flash EEPROM, ferroelectric random access memory (FRAM), or magnetoresistive random access memory.It can be designed as MRAM (magnetoresistive random access memory) or as phase-change random access memory, PCRAM (phase-change random access memory).

[0049] For use cases or application situations that may arise during the procedure and are not explicitly described here, it may be provided that, according to the procedure, an error message and / or a request for user feedback is issued and / or a default setting and / or a predetermined initial state is set.

[0050] The invention also includes further developments of the computer program product, the detection device, and the motor vehicle according to the invention, which have features already described in connection with the further developments of the method according to the invention. For this reason, the corresponding further developments of the computer program product, the detection device, and the motor vehicle according to the invention are not described again here. The invention also includes combinations of the features of the described embodiments.

[0051] The following describes exemplary embodiments of the invention. This is illustrated by:

[0052] Fig. 1 shows a schematic top view of an embodiment of a motor vehicle with an embodiment of a detection device;

[0053] Fig. 2 shows a schematic block diagram according to one embodiment of a detection device;

[0054] Fig. 3 shows a schematic diagram of a received signal from a detection device;

[0055] Fig. 4 shows another schematic top view of an embodiment of a detection device;

[0056] Fig. 5 shows a schematic perspective view of an amplitude map;

[0057] Fig. 6 shows a schematic flowchart according to one embodiment of the method; and

[0058] Fig. 7 shows a schematic side view of an embodiment of the motor vehicle with an embodiment of the detection device.

[0059] The embodiments described below are preferred embodiments of the invention. In these embodiments, the described components each represent individual features of the invention that can be considered independently of one another. Each of these features further develops the invention independently and can therefore be considered part of the invention individually or in a combination other than that shown. Furthermore, the described embodiments can also be supplemented by other features of the invention already described.

[0060] In the figures, identical or functionally equivalent elements are designated with the same reference numerals. Fig. 1 shows a schematic top view of an embodiment of a motor vehicle 1. In the present embodiment, the motor vehicle 1 has a detection device 2. The detection device 2 has at least one electronic computing unit 3. Furthermore, the detection device 2 has at least one transmitting antenna 4 and two receiving antennas 5.

[0061] In the present embodiment, it is shown in particular that virtual antennas 6, which are in particular temporally synthetic antennas, can be formed by the appropriate distribution of the transmitting antennas 4 and the receiving antennas 5.

[0062] In particular, Fig. 1 shows the creation of a virtual aperture by successive transmission processes from different transmitting antennas 4.

[0063] Fig. 2 shows a schematic block diagram according to an embodiment of the detection device 2. In the present embodiment, the electronic computing unit 3, a transmitting antenna 4, and a receiving antenna 5 are shown in more detail. It is shown in particular that the method can be provided via so-called EPIC chips. The electronic computing unit 3 is shown on the left, and the transmitter level, specifically formed by the transmitting antennas 4 and the receiving antennas 5, is shown on the right. The electronically photonic integrated circuits (EPICs) and their components are specifically designated by reference numeral 7. Here, a grid coupler and photodiode for a transmitter, as well as two grid couplers, a photodiode, and a modulator for the receiver, are shown. The electronic components are designated by reference numeral 8.Furthermore, optical components 9, in particular optical fibers, are shown accordingly.

[0064] Figure 2 shows in particular that a central station, especially in the form of the electronic computing unit 3, generates an optical carrier frequency. This frequency is modulated, for example, with one-eighth of the radar frequency and transmitted via optical fiber to the antenna chips. The frequency is then multiplied eightfold on the antenna chips, enabling them to emit radiation. Signal detection occurs in reverse. All data is processed again by the electronic computing unit 3.

[0065] Fig. 3 again shows the course of a received signal 10, in particular at different distance ranges 11, 12, 13. It is shown in particular that received signals from the physical antennas, especially the receiving antenna 5, can be represented, as well as received signals 10 generated by the virtual antenna 6. Furthermore, regions 14 are shown that were predicted using a corresponding algorithm.

[0066] In particular, Fig. 3 shows that each virtual antenna 6 also receives a received signal 10 during a transmission process within one M1 MO cycle, which is time-shifted relative to the transmitted signal. However, the number of virtual receiving antennas 6 is too small for the application of a so-called SAR algorithm, so the missing received signals 10 must be compensated for by a model function using linear / non-linear prediction, which is represented here by reference numeral 14. The model signal, estimated based on the received signals 10, calculates a hypothetical received signal for each receiving antenna that would be required to meet the Nyquist criterion and thus for the applicability of the SAR algorithms. In particular, Fig. 3 thus describes the estimation of missing signal components by linear prediction to create a fully populated aperture to fulfill the Nyquist criterion.

[0067] Fig. 4 shows a schematic top view of an embodiment of the motor vehicle 1 with the detection device 2. It is shown in particular that locally related receiving antennas 5, represented here by a group 15, can be assigned to a corresponding transmitting antenna 4. This is necessary in order to determine the speed of an object 16 (Fig. 5).

[0068] Fig. 5 shows a schematic view of a so-called amplitude map 17 at three different times with the object 16.

[0069] Fig. 6 shows a schematic flowchart according to one embodiment of the method. In a first step S1, transmit signals are transmitted over several MIMO cycles. In a second step S2, a virtual antenna array is created over each MIMO cycle. In the third step S3, range spectra of the received beat signals of all virtual antennas 6 are determined. In the fourth step S4, a range Doppler spectrum is calculated. In the fifth step S5, all range Doppler targets, in particular the objects 16, are detected. In the sixth step S6, the detected Doppler velocities are compensated from the range spectra or the beat signals. In the seventh step S7, a transmit antenna 4 with a constant transmit cycle over all MIMO cycles is identified.In the eighth step S8, the group of 15 spatially closely located receiving antennas 5 is identified, and previously calculated range spectra are assigned. In the ninth step S9, a model function is applied by linear prediction to the received signals 10 of this group 15 to calculate a model-based virtual aperture signal for each distance gate.

[0070] In step ten, S10, the amplitude map 17 is reconstructed from the virtual aperture signal of this group 15 by backprojection. In step eleven, S11, steps S9 and S10 are repeated to calculate at least two consecutive amplitude maps 17. In step twelve, S12, the target coordinates of at least two consecutive amplitude maps 17 are subtracted to determine a direction vector, and in step thirteen, S13, the coordinates of the direction vector are divided by the MIMO cycle time by the ground velocity calculation.

[0071] In particular, Fig. 6 describes the approach on an aperture with distributed radar antennas for determining ground velocity using the group 15 spatially close receiving antennas 5 within this aperture. All received signals 10 acquired within a transmission cycle serve as the input data set for the steps described below. A transmission cycle consists of several successive MIMO cycles.

[0072] Within such a MIMO cycle, each transmitting antenna 4 of the detection device 2 emits a chirp, which is received by all receiving antennas 5. Due to the time offset and the associated phase shift, an antenna array of virtual antenna elements is created over the entire duration of a MIMO cycle. First, all phase terms arising from the intrinsic motion of the carrier platform or the motion state of all detected targets are compensated. Once these dynamic phase terms are removed, the received signal 10 of a MIMO cycle now comprises only phase terms attributable to the distance to potential targets or objects 16. For determining the ground velocity, a group 15 of spatially close receiving antennas 5 is initially defined.Subsequently, a transmitting antenna 4 is defined, whose transmit signal is received by this group of receiving antennas at a constant time interval TMIMO.

[0073] In the next step, those received signals 10 from the range spectrum are determined that can be assigned to the previously defined transmitting antenna 4 and the defined group 15 of receiving antennas 5. Due to the preprocessing step for compensating the dynamic phase terms, the received signals 10 consist exclusively of distance-dependent phase terms. To now calculate an amplitude map 17 by backprojection, compliance with the Nyquist criterion must be ensured. The arrangement of the receiving antennas 5 can be so unfavorable that a reconstruction in violation of the Nyquist criterion would result in undesired aliasing effects. To prevent these, a suitable model function is determined and calculated using linear prediction over all distance gates 11, 12, 13 with the same index.

[0074] The estimated model function can be used to compensate for missing signal components due to the lack of receiving antennas. The result is a virtual aperture signal that satisfies the Nyquist criterion.

[0075] Starting from the virtual aperture signal, which represents a received signal 10 of a comparable fully populated physical antenna, an amplitude map 17 can be calculated by backprojection in a further process step. This amplitude map 17 compresses the received power in the azimuth and distance directions, so that a potential target, or object 16, has a large amplitude value.

[0076] If the described calculation steps are repeated for the next MIMO cycle, the position coordinates of possible objects 16 change compared to the preceding MIMO cycle. This change in position is due to the object's own motion. Since all velocity-related phase terms in the received signal 10 have been compensated, the change in position corresponds to a new target distance and thus influences the corresponding phase term. The change in position coordinates is now visible within the amplitude map 17 of the current MIMO cycle. If, in a further process step, the difference between the position coordinates of two successive MIMO cycles is calculated, the result is a direction vector that indicates the direction of motion of a target.

[0077] This direction vector indicates the direction of movement for each detected target within the amplitude map 17. Considering the relationship between the direction coordinates and the duration of a MIMO cycle (TMIMO) yields a motion vector. Calculating the magnitude of this motion vector gives the ground velocity: -» il v r r l = - T l MIMO v = I ||2

[0078] In particular, many elementary antennas are interconnected to form a single array. Conventional or electro-photonic radar circuits are connected to the corresponding antennas. Data transmission is then carried out to the electronic computing unit 3 for signal processing of the received data and for controlling the data to be transmitted, especially for beamforming and waveforming. The virtual antennas 6 are deployed over the M1 MO cycle. The range spectrum and the range Doppler spectrum are calculated. All Doppler targets are then detected. The detected Doppler velocities are compensated from the range spectrum or the beat signal. The transmitting antenna 4 is then set at a constant time interval from the subsequent M1 MO cycle. Spatially close receiving antennas 5 are then grouped.The application of the linear and non-linear prediction method to the Doppler-compensated range spectrum or the beat signal of this group 15 is performed to calculate a virtual aperture signal via index-matched range gates or samples. The transmitting antenna 4 with a time-constant transmit cycle in successive MIMO cycles is then selected. The amplitude map 17 is calculated by backprojection after each MIMO cycle, and the spatial displacement and rotation of the reflections in successive amplitude maps 17 are determined. The direction vector is then determined by subtracting the position coordinates of identical reflections from successive amplitude maps 17. The velocity vector of the reflection is then determined by dividing the x and y components of the direction vectors by the transmit cycle. The entire process can then be applied to an environmental model.

[0079] The proposed method offers the particular advantage of increased diagnostic capability and reliability in the event of a fault. Furthermore, it enables compensation for the failure of individual elements and enhances system robustness. Additionally, the antenna array can be reconfigured after the failure of individual element antennas, eliminating the need to replace the entire array. The method also allows for improved resolution and accuracy, as well as an increase in the detection range. Moreover, it enables the cost-effective manufacturing of large-area antenna arrays and the refinement of the overall system's resolution. Finally, it allows for the measurement of ground velocity under a wider range of target scenarios. A calibration method can also be employed. This method is applicable to LiDAR, camera, and satellite communication systems.

[0080] Fig. 7 shows a schematic side view of an embodiment of a motor vehicle 1 with an embodiment of a detection device 2. In particular, a plurality of antenna elements 4, 5 are shown. In the present embodiment, it is shown that these can be arranged, for example, on the side of the motor vehicle 1. It is also shown that they can be arranged, for example, in a window element of the motor vehicle 1. It is, of course, also possible that the antenna elements 4, 5 can be located at the rear of the motor vehicle 1, at the front of the motor vehicle 1, for example, on a windshield of the motor vehicle 1, as well as on the roof of the motor vehicle 1. This list is purely exemplary and by no means exhaustive. The antenna elements 4, 5 can also be arranged at other, unlisted locations on the motor vehicle 1.

[0081] Reference symbol list

[0082] Motor vehicle detection device, electronic computing device, transmitting antenna, receiving antenna, virtual antenna, photonic component, electronic component, optical component, received signal, range range, range range, range range, model function, group, object

[0083] Amplitude map Steps of the procedure

Claims

Patent claims 1. Method for determining at least one velocity of at least one object (16) in the vicinity of a motor vehicle (1) by means of a detection device (2) of the motor vehicle (1), comprising the steps: - Providing the detection device (2) with a plurality of transmitting antennas (4) on the motor vehicle (1) and a plurality of receiving antennas (5) on the motor vehicle (1); - Emitting signals into the environment using the transmitting antennas (4) and receiving received signals (10) reflected from the at least one object (16) using the receiving antennas (5) for at least two time cycles; - Identifying at least one transmitting antenna (4) and at least two receiving antennas (5) locally assigned to the transmitting antenna (4) depending on the transmitting signals and the receiving signals (10) by means of an electronic computing device (3) of the detection device (2); - Generating a first amplitude map (17) of the environment depending on the received signals (10) for a first magazine and generating a second amplitude map (17) of the environment depending on the received signals (10) for a second magazine following the first magazine using the electronic computing device (3); - Identifying a first amplitude assigned to the object (16) in the first amplitude map (17) and identifying a second amplitude assigned to the object (16) in the second amplitude map (17) using the electronic computing device (3); - Determining a first position of the object (16) in the first amplitude map (17) as a function of the first amplitude and determining a second position of the object (16) in the second amplitude map (17) as a function of the second amplitude using the electronic computing device (3); and - Determining the proper velocity of the object (16) as a function of the first position and the second position using the electronic computing device (3).

2. The method according to claim 1, characterized in that A distance spectrum is generated to identify the transmitting antenna (4) and the associated receiving antennas (5).

3. Method according to claim 1 or 2, characterized in that a distance Doppler spectrum is generated to identify the transmitting antenna (4) and the associated receiving antennas (5).

4. Method according to one of claims 2 or 3, characterized in that a certain Doppler velocity in the distance spectrum is compensated.

5. Method according to one of the preceding claims, characterized in that the proper velocity of the object (16) is determined on the basis of a determination of a direction vector for the object (16) in the amplitude maps (17).

6. Method according to one of the preceding claims, characterized in that the speed of the motor vehicle (1) is taken into account when determining the speed of the object (16).

7. Method according to one of the preceding claims, characterized in that the determined intrinsic velocity of the object is transferred to an environment model for the motor vehicle (1).

8. Method according to one of the preceding claims, characterized in that a respective proper velocity is determined for at least two objects (16) in the environment essentially simultaneously.

9. Computer program product comprising program code means which cause an electronic computing device (3) to perform a method according to one of claims 1 to 8 when the program code means are executed by the electronic computing device (3).

0. Detection device (2) for determining at least one velocity of at least one object (16) in the vicinity of a motor vehicle (1), comprising at least one transmitting antenna (4), at least two receiving antennas (5) and an electronic computing device (3), wherein the detection device (2) is configured to perform a method according to one of claims 1 to 8.

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