CN111190155B - Auxiliary driving system capable of inhibiting radar close-range harmonic wave - Google Patents
Auxiliary driving system capable of inhibiting radar close-range harmonic wave Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/021—Auxiliary means for detecting or identifying radar signals or the like, e.g. radar jamming signals
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
- G01S7/415—Identification of targets based on measurements of movement associated with the target
Abstract
The invention provides an auxiliary driving system capable of restraining radar close-range harmonic waves, which comprises: at least one transceiver (Tx/Rx) configured to radiate a transmitted radar signal and to receive an echo signal of the transmitted radar signal; the original data processing unit is used for preprocessing a transceiver (Tx/Rx) transmitting/receiving signal; the target point extraction module extracts the original target point information of interest; the harmonic calculation module is used for calculating the harmonic order and the harmonic amplitude of the original target point in the scene; the comparison module is used for comparing the original target point information acquired by the target point extraction module with the harmonic parts of the target points and the information of the corresponding harmonic order numbers acquired by the harmonic calculation module, and judging whether the amplitude of a specific position in the original target point corresponds to the harmonic part acquired by the target point position harmonic calculation module or not; the moving object judging module is used for judging whether the moving object exists in the non-corresponding part. The invention can not only inhibit harmonic wave, but also completely reserve key points of other important objects such as pedestrians, bicycles and the like which are closer to the vehicle in the view field.
Description
Technical Field
The invention relates to the technical field of automobile electronics, in particular to an auxiliary driving system capable of inhibiting radar close-range harmonic waves.
Background
The driving assistance technology is now mature, and the advanced driving assistance system (ADVANCED DRIVER ASSISTANCE SYSTEM, ADAS) and the commonly used sensing devices in the unmanned driving system are visual sensing devices, ultrasonic radars and laser radars according to the current technology.
When detecting an obstacle, for example, a vehicle passing through the millimeter wave radar transversely is detected, the millimeter wave radar emits waves for many times through the transmitter to meet the vehicle passing through the millimeter wave radar transversely, and the millimeter wave radar is subjected to multiple reflections in a low-speed scene, and the multiple reflections can cause multiple harmonics to be generated. Harmonics are a disturbing factor, which is generated by reflection. The harmonic wave is reflected on a Doppler spectrogram formed after two-dimensional FFT and is expressed as one or a plurality of reflection points, but the system cannot distinguish the reflection points. And (3) detecting algorithm errors subsequently, preprocessing the point cloud data detected by the millimeter waves by the system, wherein the preprocessing comprises restraining the harmonic waves, namely removing a plurality of reflection points of the target point through an algorithm. Although the millimeter wave harmonic suppression preprocessing reduces the probability of occurrence of the virtual scene problem, the millimeter wave point cloud data processing can remove key points of other important objects as reflection points in an actual scene because of the error of a harmonic suppression algorithm. For example, when a pedestrian or a bicycle passes just beside a vehicle passing laterally, and the pedestrian or the bicycle needs to be monitored as an important object in the field of view, the target point representing the pedestrian or the bicycle is erroneously removed when passing through the harmonic suppression algorithm. Therefore, pedestrians or bicycles cannot be obtained by using the partial point cloud data as the original data to perform a clustering algorithm in the later period. Therefore, how to restrain harmonics and completely keep key points of important objects such as pedestrians, bicycles and the like which are close to vehicles in a view field, namely removing 'virtual view' points and keeping 'ghost' points, becomes a problem to be solved urgently.
Disclosure of Invention
In order to solve the above and other potential technical problems, the invention provides an auxiliary driving system capable of suppressing radar close-range harmonics, which solves the problem that millimeter wave harmonic suppression preprocessing reduces the occurrence probability of a virtual scene problem, but the millimeter wave point cloud data processing can remove key points of other important objects as reflection points in an actual scene due to a harmonic suppression algorithm.
An auxiliary driving system capable of suppressing radar close-range harmonics, comprising:
At least one transceiver (Tx/Rx) configured to radiate a transmitted radar signal and to receive an echo signal of the transmitted radar signal;
A raw data processing unit for preprocessing transceiver (Tx/Rx) transmit/receive signals to obtain raw target point information including, but not limited to, target speed, target distance, azimuth and elevation;
The target point extraction module is used for extracting the original target point information of interest;
The harmonic wave calculation module is used for calculating the harmonic wave number and the harmonic wave amplitude of the original target point in the scene, and separating harmonic wave parts of each target point and corresponding harmonic wave numbers from the original target point information;
and the moving object judging module is used for judging whether a moving object exists in the non-corresponding part or not when the amplitude of the specific position in the original target point acquired by the comparison module is not corresponding to the harmonic part acquired by the target point position harmonic calculation module.
Further, the harmonic calculation module comprises a harmonic order judgment module and a harmonic amplitude calculation module.
Further, the harmonic order judgment module is used for judging the order of the harmonic generated by each target point in the original target point information and the position of each order harmonic.
Further, the harmonic amplitude calculation module is used for calculating the amplitude of the harmonic to which each level harmonic position belongs.
Further, the harmonic correlation module is used for acquiring the correlation between the harmonic order number judgment module and the harmonic amplitude calculation module.
Further, the association range obtained by the harmonic association module includes, but is not limited to, whether the target point generates a harmonic wave, the order of each target point generating the harmonic wave, the harmonic amplitude corresponding to each harmonic order generated by each target point, and the association relation among each target point, the harmonic generated by the target point, the harmonic order and the corresponding amplitude of each harmonic order after the target point changes in the extended view range over time.
Further, when the amplitude of the specific position in the original target point does not correspond to the harmonic part obtained by the target point position harmonic calculation module, the comparison module analyzes the amplitude of the specific position in the original target point and the target point position harmonic part, and outputs the analysis result to the moving object judgment module.
Further, the system also comprises an analysis module, when the comparison module analyzes the amplitude of the specific position in the original target point and the harmonic part of the position of the target point, the analysis module takes the echo times of the target point and the amplitude of the target point as input data, and the input data is input into the scene multi-inverse model to obtain the harmonic order of each target point and the harmonic amplitude of each order.
Further, the scene multi-inverse model is used for representing the reflection relation of each reflector and reflection position of a specific scene, and the scene multi-inverse model can be an S-V multi-inverse model or a multi-inverse model obtained according to actual measurement of the specific scene and reflecting the reflection relation of the specific scene.
Further, the standard scene multi-inversion model extracts characteristic parameters according to the measured scene environment, and determines scene coverage and height.
Further, the S-V multi-inverse model is used for extracting characteristic parameters through a millimeter wave radar through a measuring environment to obtain a standard model, and the standard model is used for extracting a parking lot environment according to a measuring result, wherein the coverage range is 7 meters to 20 meters and is as high as 10Ghz.
For example, the office environment model has a measuring range of 3-28 m,2-8Ghz, and an outdoor measuring range of 5-17 meters, up to 3-6Ghz. The industrial model environment is extracted according to the measurement result, and the coverage range is 3-10Ghz and the distance is 2-8 meters.
Further, the scene multiple inverse model includes one or more standard scene multiple inverse models and/or a specific scene multiple inverse model.
Further, the specific scene multi-reflection model divides the scene into a standard area and a specific area, the standard area extracts characteristic parameters according to the measured scene environment, and the specific area is modeled according to the reflection relation of the reflector at the specific position.
Further, the system also comprises an evaluation module, wherein the evaluation module is used for evaluating the matching degree of the standard scene multi-inverse model and the specific scene multi-inverse model in the specific scene, and the evaluation module evaluates the basis including but not limited to path loss characteristics, amplitude distribution characteristics, clustering characteristics and reflection characteristic characteristics.
Further, the effect of the path loss characteristics on the evaluation by the evaluation module is that the implementation of this model on a computer of the path loss characteristics involves generating N correlated lognormal variables representing N different groups, and then applying the appropriate distances between the antennas around the path loss body. This can be done by generating N correlated normal variables, adding path loss, then converting from dB to linear scale, introducing appropriate variance and cross correlation coefficients, describing the distribution of the amplitude distribution for each group, without reproducing the covariance matrix C.
Further, after the parameterized channel is formed by the standard scene multiple inverse model, the standard scene multiple inverse model can be used for generating a set of impulse responses, and in turn, can also be used for testing the structural performance of different millimeter wave transceivers.
For example, typical indoor and outdoor environments, the general channel model and the fact model are compared with characteristics of path loss, amplitude distribution, clustering, etc., and differences between the channel model and the fact model are simulated and measured to correct the channel model.
Further, the system also comprises a correction channel model, wherein the correction channel model is used for correcting one or more of a standard scene multi-reflection model, a path loss characteristic, an amplitude distribution characteristic, a clustering characteristic and a reflection characteristic in a specific scene multi-reflection model.
An auxiliary driving method capable of suppressing radar close-range harmonic waves comprises the following steps:
S01: information transmission/reception: a transceiver (Tx/Rx) configured to radiate and receive an echo signal of a transmitted radar signal, pre-processing the transceiver (Tx/Rx) transmit/receive signal to obtain raw target point information including, but not limited to, target speed, target distance, azimuth and elevation;
S02: extracting interesting original target point information, calculating harmonic order numbers and harmonic amplitude values of the original target points in the scene, and separating harmonic parts and corresponding harmonic order numbers of all the target points from the original target point information;
S03: comparing the original target point information acquired by the target point extraction module with the harmonic wave parts of the target points and the information of the corresponding harmonic wave numbers acquired by the harmonic wave calculation module, and judging whether the amplitude of a specific position in the original target point corresponds to the harmonic wave part acquired by the target point position harmonic wave calculation module or not;
S04: when the amplitude of the specific position in the original target point acquired by the comparison module is not corresponding to the harmonic part acquired by the target point position harmonic calculation module, the moving object judgment module is used for judging whether a moving object exists in the non-corresponding part.
Further, the system also comprises a harmonic order judgment module, wherein the harmonic order judgment module is used for judging the order of the harmonic generated by each target point in the original target point information and the position of the harmonic of each order.
Further, the system also comprises a harmonic amplitude calculation module, wherein the harmonic amplitude calculation module is used for calculating the amplitude of the harmonic to which each level harmonic position belongs.
Further, the harmonic correlation module is used for acquiring the correlation between the harmonic order number judgment module and the harmonic amplitude calculation module.
Further, the association range obtained by the harmonic association module includes, but is not limited to, whether the target point generates a harmonic wave, the order of each target point generating the harmonic wave, the harmonic amplitude corresponding to each harmonic order generated by each target point, and the association relation among each target point, the harmonic generated by the target point, the harmonic order and the corresponding amplitude of each harmonic order after the target point changes in the extended view range over time.
Further, when the amplitude of the specific position in the original target point does not correspond to the harmonic part obtained by the target point position harmonic calculation module, the comparison module analyzes the amplitude of the specific position in the original target point and the target point position harmonic part, and outputs the analysis result to the moving object judgment module.
Further, the system also comprises an analysis module, when the comparison module analyzes the amplitude of the specific position in the original target point and the harmonic part of the position of the target point, the analysis module takes the echo times of the target point and the amplitude of the target point as input data, and the input data is input into the scene multi-inverse model to obtain the harmonic order of each target point and the harmonic amplitude of each order.
Further, the scene multi-inverse model is used for representing the reflection relation of each reflector and reflection position of a specific scene, and the scene multi-inverse model can be an S-V multi-inverse model or a multi-inverse model obtained according to actual measurement of the specific scene and reflecting the reflection relation of the specific scene.
Further, the standard scene multi-inversion model extracts characteristic parameters according to the measured scene environment, and determines scene coverage and height.
Further, the S-V multi-inverse model is used for extracting characteristic parameters through a millimeter wave radar through a measuring environment to obtain a standard model, and the standard model is used for extracting a parking lot environment according to a measuring result, wherein the coverage range is 7 meters to 20 meters and is as high as 10Ghz.
For example, the office environment model has a measuring range of 3-28 m,2-8Ghz, and an outdoor measuring range of 5-17 meters, up to 3-6Ghz. The industrial model environment is extracted according to the measurement result, and the coverage range is 3-10Ghz and the distance is 2-8 meters.
Further, the scene multiple inverse model includes one or more standard scene multiple inverse models and/or a specific scene multiple inverse model.
Further, the specific scene multi-reflection model divides the scene into a standard area and a specific area, the standard area extracts characteristic parameters according to the measured scene environment, and the specific area is modeled according to the reflection relation of the reflector at the specific position.
Further, the system also comprises an evaluation module, wherein the evaluation module is used for evaluating the matching degree of the standard scene multi-inverse model and the specific scene multi-inverse model in the specific scene, and the evaluation module evaluates the basis including but not limited to path loss characteristics, amplitude distribution characteristics, clustering characteristics and reflection characteristic characteristics.
Further, the effect of the path loss characteristics on the evaluation by the evaluation module is that the implementation of this model on a computer of the path loss characteristics involves generating N correlated lognormal variables representing N different groups, and then applying the appropriate distances between the antennas around the path loss body. This can be done by generating N correlated normal variables, adding path loss, then converting from dB to linear scale, introducing appropriate variance and cross correlation coefficients, describing the distribution of the amplitude distribution for each group, without reproducing the covariance matrix C.
Further, after the parameterized channel is formed by the standard scene multiple inverse model, the standard scene multiple inverse model can be used for generating a set of impulse responses, and in turn, can also be used for testing the structural performance of different millimeter wave transceivers.
For example, typical indoor and outdoor environments, the general channel model and the fact model are compared with characteristics of path loss, amplitude distribution, clustering, etc., and differences between the channel model and the fact model are simulated and measured to correct the channel model.
Further, the system also comprises a correction channel model, wherein the correction channel model is used for correcting one or more of a standard scene multi-reflection model, a path loss characteristic, an amplitude distribution characteristic, a clustering characteristic and a reflection characteristic in a specific scene multi-reflection model.
An application of an auxiliary driving system capable of suppressing radar close-range harmonic,
In the auxiliary driving system:
At least one transceiver is configured to radiate a transmit radar signal and to receive an echo signal of the transmit radar signal;
the original data processing unit is used for preprocessing a transceiver (Tx/Rx) transmitting/receiving signal to obtain original target point information including but not limited to target speed, target distance, azimuth angle and elevation angle;
the target point extraction module extracts the original target point information of interest;
The harmonic calculation module is used for calculating harmonic order and harmonic amplitude of an original target point in the scene, and separating harmonic parts of each target point and corresponding harmonic order from the original target point information;
The comparison module is used for comparing the original target point information acquired by the target point extraction module with the harmonic parts of the target points and the information of the corresponding harmonic order numbers acquired by the harmonic calculation module, and judging whether the amplitude of a specific position in the original target point corresponds to the harmonic part acquired by the target point position harmonic calculation module or not;
when the amplitude of the specific position in the original target point acquired by the comparison module does not correspond to the harmonic part acquired by the target point position harmonic calculation module, the moving object judgment module is used for judging whether a moving object exists in the non-corresponding part.
As described above, the present invention has the following advantageous effects:
When there is just a pedestrian, a bicycle, passing beside a laterally passing vehicle, which is to be monitored as an important object in the field of view, the target point characterizing the pedestrian or bicycle may be erroneously removed when passing the harmonic suppression algorithm. Therefore, pedestrians or bicycles cannot be obtained by using the partial point cloud data as the original data to perform a clustering algorithm in the later period. The method can not only inhibit harmonic waves (including multiple harmonic waves), but also completely reserve key points of other important objects such as pedestrians and bicycles which are close to the vehicle in the view field.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of an embodiment of the present invention.
Fig. 2 shows a schematic diagram of another embodiment.
Fig. 3 shows a schematic diagram of another embodiment.
Fig. 4 shows a schematic diagram of another embodiment.
Fig. 5 shows a schematic diagram of another embodiment.
Fig. 6 shows a schematic diagram of another embodiment.
Fig. 7 shows a slice in the intensity and distance dimensions in a three-dimensional FFT.
Fig. 8 shows a slice in the intensity and velocity dimensions in a three-dimensional FFT.
Fig. 9 shows a schematic diagram of a three-dimensional FFT.
Fig. 10 shows a schematic diagram of a two-dimensional FFT.
Fig. 11 shows a schematic diagram of a two-dimensional FFT.
Fig. 12 shows a schematic of a two-dimensional FFT.
FIG. 13 shows the reflection in an S-V multiple inverse model as an exponential decay and power diagram.
FIG. 14 is a schematic diagram of an S-V multi-inversion mode impulse response.
Fig. 15 shows the distance-Doppler spectrum obtained for the two-dimensional FFT of target n after K FMCW snapshots.
Detailed Description
Other advantages and effects of the present invention will become apparent to those skilled in the art from the following disclosure, which describes the embodiments of the present invention with reference to specific examples. The invention may be practiced or carried out in other embodiments that depart from the specific details, and the details of the present description may be modified or varied from the spirit and scope of the present invention. It should be noted that the following embodiments and features in the embodiments may be combined with each other without conflict.
It should be understood that the structures, proportions, sizes, etc. shown in the drawings are for illustration purposes only and should not be construed as limiting the invention to the extent that it can be practiced, since modifications, changes in the proportions, or otherwise, used in the practice of the invention, are not intended to be critical to the essential characteristics of the invention, but are intended to fall within the spirit and scope of the invention. Also, the terms such as "upper," "lower," "left," "right," "middle," and "a" and the like recited in the present specification are merely for descriptive purposes and are not intended to limit the scope of the invention, but are intended to provide relative positional changes or modifications without materially altering the technical context in which the invention may be practiced.
With reference to figures 1 to 15 of the drawings,
An auxiliary driving system capable of suppressing radar close-range harmonics, comprising:
At least one transceiver (Tx/Rx) configured to radiate a transmitted radar signal and to receive an echo signal of the transmitted radar signal;
A raw data processing unit for preprocessing transceiver (Tx/Rx) transmit/receive signals to obtain raw target point information including, but not limited to, target speed, target distance, azimuth and elevation;
The target point extraction module is used for extracting the original target point information of interest;
The harmonic wave calculation module is used for calculating the harmonic wave number and the harmonic wave amplitude of the original target point in the scene, and separating harmonic wave parts of each target point and corresponding harmonic wave numbers from the original target point information;
and the moving object judging module is used for judging whether a moving object exists in the non-corresponding part or not when the amplitude of the specific position in the original target point acquired by the comparison module is not corresponding to the harmonic part acquired by the target point position harmonic calculation module.
Further, the harmonic calculation module comprises a harmonic order judgment module and a harmonic amplitude calculation module.
Further, the harmonic order judgment module is used for judging the order of the harmonic generated by each target point in the original target point information and the position of each order harmonic.
Further, the harmonic amplitude calculation module is used for calculating the amplitude of the harmonic to which each level harmonic position belongs.
Further, the harmonic correlation module is used for acquiring the correlation between the harmonic order number judgment module and the harmonic amplitude calculation module.
Further, the association range obtained by the harmonic association module includes, but is not limited to, whether the target point generates a harmonic wave, the order of each target point generating the harmonic wave, the harmonic amplitude corresponding to each harmonic order generated by each target point, and the association relation among each target point, the harmonic generated by the target point, the harmonic order and the corresponding amplitude of each harmonic order after the target point changes in the extended view range over time.
Further, when the amplitude of the specific position in the original target point does not correspond to the harmonic part obtained by the target point position harmonic calculation module, the comparison module analyzes the amplitude of the specific position in the original target point and the target point position harmonic part, and outputs the analysis result to the moving object judgment module.
Further, the system also comprises an analysis module, when the comparison module analyzes the amplitude of the specific position in the original target point and the harmonic part of the position of the target point, the analysis module takes the echo times of the target point and the amplitude of the target point as input data, and the input data is input into the scene multi-inverse model to obtain the harmonic order of each target point and the harmonic amplitude of each order.
Further, the scene multi-inverse model is used for representing the reflection relation of each reflector and reflection position of a specific scene, and the scene multi-inverse model can be an S-V multi-inverse model or a multi-inverse model obtained according to actual measurement of the specific scene and reflecting the reflection relation of the specific scene.
Further, the standard scene multi-inversion model extracts characteristic parameters according to the measured scene environment, and determines scene coverage and height.
Further, the S-V multi-inverse model is used for extracting characteristic parameters through a millimeter wave radar through a measuring environment to obtain a standard model, and the standard model is used for extracting a parking lot environment according to a measuring result, wherein the coverage range is 7 meters to 20 meters and is as high as 10Ghz.
For example, the office environment model has a measuring range of 3-28 m,2-8Ghz, and an outdoor measuring range of 5-17 meters, up to 3-6Ghz. The industrial model environment is extracted according to the measurement result, and the coverage range is 3-10Ghz and the distance is 2-8 meters.
Further, the scene multiple inverse model includes one or more standard scene multiple inverse models and/or a specific scene multiple inverse model.
Further, the specific scene multi-reflection model divides the scene into a standard area and a specific area, the standard area extracts characteristic parameters according to the measured scene environment, and the specific area is modeled according to the reflection relation of the reflector at the specific position.
Further, the system also comprises an evaluation module, wherein the evaluation module is used for evaluating the matching degree of the standard scene multi-inverse model and the specific scene multi-inverse model in the specific scene, and the evaluation module evaluates the basis including but not limited to path loss characteristics, amplitude distribution characteristics, clustering characteristics and reflection characteristic characteristics.
Further, the effect of the path loss characteristics on the evaluation by the evaluation module is that the implementation of this model on a computer of the path loss characteristics involves generating N correlated lognormal variables representing N different groups, and then applying the appropriate distances between the antennas around the path loss body. This can be done by generating N correlated normal variables, adding path loss, then converting from dB to linear scale, introducing appropriate variance and cross correlation coefficients, describing the distribution of the amplitude distribution for each group, without reproducing the covariance matrix C.
Further, after the parameterized channel is formed by the standard scene multiple inverse model, the standard scene multiple inverse model can be used for generating a set of impulse responses, and in turn, can also be used for testing the structural performance of different millimeter wave transceivers.
For example, typical indoor and outdoor environments, the general channel model and the fact model are compared with characteristics of path loss, amplitude distribution, clustering, etc., and differences between the channel model and the fact model are simulated and measured to correct the channel model.
Further, the system also comprises a correction channel model, wherein the correction channel model is used for correcting one or more of a standard scene multi-reflection model, a path loss characteristic, an amplitude distribution characteristic, a clustering characteristic and a reflection characteristic in a specific scene multi-reflection model.
An auxiliary driving method capable of suppressing radar close-range harmonic waves comprises the following steps:
S01: information transmission/reception: a transceiver (Tx/Rx) configured to radiate and receive an echo signal of a transmitted radar signal, pre-processing the transceiver (Tx/Rx) transmit/receive signal to obtain raw target point information including, but not limited to, target speed, target distance, azimuth and elevation;
S02: extracting interesting original target point information, calculating harmonic order numbers and harmonic amplitude values of the original target points in the scene, and separating harmonic parts and corresponding harmonic order numbers of all the target points from the original target point information;
S03: comparing the original target point information acquired by the target point extraction module with the harmonic wave parts of the target points and the information of the corresponding harmonic wave numbers acquired by the harmonic wave calculation module, and judging whether the amplitude of a specific position in the original target point corresponds to the harmonic wave part acquired by the target point position harmonic wave calculation module or not;
S04: when the amplitude of the specific position in the original target point acquired by the comparison module is not corresponding to the harmonic part acquired by the target point position harmonic calculation module, the moving object judgment module is used for judging whether a moving object exists in the non-corresponding part.
Further, the system also comprises a harmonic order judgment module, wherein the harmonic order judgment module is used for judging the order of the harmonic generated by each target point in the original target point information and the position of the harmonic of each order.
Further, the system also comprises a harmonic amplitude calculation module, wherein the harmonic amplitude calculation module is used for calculating the amplitude of the harmonic to which each level harmonic position belongs.
Further, the harmonic correlation module is used for acquiring the correlation between the harmonic order number judgment module and the harmonic amplitude calculation module.
Further, the association range obtained by the harmonic association module includes, but is not limited to, whether the target point generates a harmonic wave, the order of each target point generating the harmonic wave, the harmonic amplitude corresponding to each harmonic order generated by each target point, and the association relation among each target point, the harmonic generated by the target point, the harmonic order and the corresponding amplitude of each harmonic order after the target point changes in the extended view range over time.
Further, when the amplitude of the specific position in the original target point does not correspond to the harmonic part obtained by the target point position harmonic calculation module, the comparison module analyzes the amplitude of the specific position in the original target point and the target point position harmonic part, and outputs the analysis result to the moving object judgment module.
Further, the system also comprises an analysis module, when the comparison module analyzes the amplitude of the specific position in the original target point and the harmonic part of the position of the target point, the analysis module takes the echo times of the target point and the amplitude of the target point as input data, and the input data is input into the scene multi-inverse model to obtain the harmonic order of each target point and the harmonic amplitude of each order.
Further, the scene multi-inverse model is used for representing the reflection relation of each reflector and reflection position of a specific scene, and the scene multi-inverse model can be an S-V multi-inverse model or a multi-inverse model obtained according to actual measurement of the specific scene and reflecting the reflection relation of the specific scene.
Further, the standard scene multi-inversion model extracts characteristic parameters according to the measured scene environment, and determines scene coverage and height.
Further, the S-V multi-inverse model is used for extracting characteristic parameters through a millimeter wave radar through a measuring environment to obtain a standard model, and the standard model is used for extracting a parking lot environment according to a measuring result, wherein the coverage range is 7 meters to 20 meters and is as high as 10Ghz.
For example, the office environment model has a measuring range of 3-28 m,2-8Ghz, and an outdoor measuring range of 5-17 meters, up to 3-6Ghz. The industrial model environment is extracted according to the measurement result, and the coverage range is 3-10Ghz and the distance is 2-8 meters.
Further, the scene multiple inverse model includes one or more standard scene multiple inverse models and/or a specific scene multiple inverse model.
Further, the specific scene multi-reflection model divides the scene into a standard area and a specific area, the standard area extracts characteristic parameters according to the measured scene environment, and the specific area is modeled according to the reflection relation of the reflector at the specific position.
Further, the system also comprises an evaluation module, wherein the evaluation module is used for evaluating the matching degree of the standard scene multi-inverse model and the specific scene multi-inverse model in the specific scene, and the evaluation module evaluates the basis including but not limited to path loss characteristics, amplitude distribution characteristics, clustering characteristics and reflection characteristic characteristics.
Further, the effect of the path loss characteristics on the evaluation by the evaluation module is that the implementation of this model on a computer of the path loss characteristics involves generating N correlated lognormal variables representing N different groups, and then applying the appropriate distances between the antennas around the path loss body. This can be done by generating N correlated normal variables, adding path loss, then converting from dB to linear scale, introducing appropriate variance and cross correlation coefficients, describing the distribution of the amplitude distribution for each group, without reproducing the covariance matrix C.
Further, after the parameterized channel is formed by the standard scene multiple inverse model, the standard scene multiple inverse model can be used for generating a set of impulse responses, and in turn, can also be used for testing the structural performance of different millimeter wave transceivers.
For example, typical indoor and outdoor environments, the general channel model and the fact model are compared with characteristics of path loss, amplitude distribution, clustering, etc., and differences between the channel model and the fact model are simulated and measured to correct the channel model.
Further, the system also comprises a correction channel model, wherein the correction channel model is used for correcting one or more of a standard scene multi-reflection model, a path loss characteristic, an amplitude distribution characteristic, a clustering characteristic and a reflection characteristic in a specific scene multi-reflection model.
An application of an auxiliary driving system capable of suppressing radar close-range harmonic,
In the auxiliary driving system:
At least one transceiver is configured to radiate a transmit radar signal and to receive an echo signal of the transmit radar signal;
the original data processing unit is used for preprocessing a transceiver (Tx/Rx) transmitting/receiving signal to obtain original target point information including but not limited to target speed, target distance, azimuth angle and elevation angle;
the target point extraction module extracts the original target point information of interest;
The harmonic calculation module is used for calculating harmonic order and harmonic amplitude of an original target point in the scene, and separating harmonic parts of each target point and corresponding harmonic order from the original target point information;
when the amplitude of the specific position in the original target point acquired by the comparison module does not correspond to the harmonic part acquired by the target point position harmonic calculation module, the moving object judgment module is used for judging whether a moving object exists in the non-corresponding part.
A computer storage medium for storing a software program corresponding to the above-described auxiliary driving method capable of suppressing the radar close range harmonics and/or an auxiliary driving system capable of suppressing the radar close range harmonics.
In the case of the 77GHz millimeter wave radar in ADAS high-speed scenes and low-speed scenes such as automatic parking application, radar emission signals are on strong detection targets (strong RCS), if vehicles, guardrails, metal roadblocks in parking lots and the like often form multiple reflections, since the automobile radar adopts FMCW CHIRP-sequence (sequence of frequency modulation continuous waves) waveform design, the mathematical expression form of the emission signals is as follows
Where a Tx denotes the strength of the transmitted signal, f c0 denotes the carrier frequency,The initial phase is represented, μ represents a signal scan time factor, the signal scan time factor is related to a bandwidth B and a scan duration Ts, where μ=b/Ts, at time t, a transmitted signal is propagated n times to obtain a strong detection target, and the signal passes through a channel h (t), and the receiver receives a virtual scene generated by repeated reflection, where the mathematical relationship between the propagation of the two signals and the difference frequency signal is:
The convolution operation and the channel response function operation variable h (t) are respectively arranged in the brackets, the sigma Rcs,n(t) detects the target RCS and the low-pass filter function lp (t), the target RCS and the low-pass filter function lp (t) are applied to the formula (2), and the converted difference frequency signal is written:
Where An represents the strength of the signal, τ n represents the round trip period of target n, one radar snapshot consists of K scans for a period of several milliseconds, where the round trip time of the signal of target n can be expressed as:
τn＝τ0+vD,n·K·TPRI (4)
Where τ 0, VD, n, K and TPRI are the response of the initial round trip period, doppler velocity, number of scans, scan and pulse time interval, respectively. Thus, the difference frequency signal of the target n is expressed in the frequency domain, and ω (t) represents a window function as in equation (2). Between the doppler frequency ranges fR to fD. The echo signal of a specific reflection point (tau n) of an object can be obtained by combining the multiple reflection model of [ S-V ] with the consideration that the echo signal can comprise multiple reflections in a complex scene, the whole echo signal representing the object n can be expressed as
Wherein the method comprises the steps ofA signal section representing the target point; t1, τk1 represents the arrival time of the 1st reflection node and the arrival time of the kth irradiation from node 1.βkl is the corresponding exponentially decaying signal gain. The model obtained by the formula (6) can directly calculate the time-frequency response of 1 time, 2 times, … times and n times of harmonic waves according to the target signal of the first part. Where the signal gain may be calibrated using channel measurement, such as Molisch, the calibration may be by scene classification, such as parking lot, outdoors, etc.
A specific first harmonic suppression method is given, as shown in equation (5), which can suppress multiple harmonics within a complex environment and is not limited to the scheme shown in fig. 15.
As shown in the figure, the distance-Doppler spectrum obtained by the two-dimensional FFT of the target n after K FMCW snapshots, the 1 st harmonic and the target m are located at the same position, according to the formula (6) and the calibration scheme, all harmonic signals (including amplitude and phase) of the target n can be calculated according to the target point signal part (Line-of-Sight) of the target n, so that the position of the whole target n harmonic interference (such as the interference of the target m and primary harmonic) can be suppressed through simple complex subtraction, and the correct amplitude and phase correspondence of the target m can be obtained.
As shown in fig. 1, in an embodiment, the current vehicle travels directly above, and there is a vehicle traveling laterally in front, and when the millimeter wave radar detects the target point, the position behind the target point, which is equidistant from the direction of the extension line, generates a first harmonic, as shown by a broken line, on the spectrogram, which is the same as the position on the spectrogram, as the target point, which is the pedestrian directly in front of the current vehicle, represented by a circle. The ordinate indicates the Doppler velocity, the abscissa indicates the relative distance, the point near the origin is represented by a triangle symbol, the point (LOS 1) at the closest position to the current vehicle and located on the lateral vehicle is represented by a cross symbol on the point, the point (LOS 2) at the slightly distant position from the current vehicle and located on the lateral vehicle is represented by a triangle symbol point located on the extension line of the point (LOS 1) and located at the same position as the point (LOS 1) at which the distance from the sight-providing vehicle is equal to the point (LOS 1) at the first virtual scene harmonic point, and the circle at the same position as the first virtual scene harmonic point represents the pedestrian. The cross sign on the extension line of the target point (LOS 2) and at the same position as the distance between the target point (LOS 2) and the vehicle providing the visual field is the primary virtual scene harmonic point of the target point (LOS 2), and the circle at the same position of the primary virtual scene harmonic point represents the pedestrian.
As shown in fig. 2, in an embodiment, when the current vehicle travels directly above and a vehicle traveling longitudinally in opposite directions is provided in front of the current vehicle, when the millimeter wave radar detects the target point, the position of the first harmonic wave generated in the direction of the equal distance extension line behind the target point is shown by a broken line due to multiple reflections of the wave, the position of the first harmonic wave on the spectrogram is the same as the position of the pillar immediately in front of the current vehicle, the pillar is shown by a circle, and the position on the spectrogram is the same. The ordinate indicates the doppler velocity, the abscissa indicates the relative distance, and the position near the origin is the position where the pillar is stationary at the doppler velocity when the vehicle providing the field of view is traveling at a very slow velocity, but it can be seen that there are two pulses in the distance direction, the first pulse being the target point (LOS 1) obtained on the longitudinal vehicle body, the second pulse being the first harmonic point of the first pulse, where the real target point (LOS 2) formed by the pillar is also included, and the portion of the second pulse related to the first pulse can be removed by using the method, leaving the real target point portion formed by the pillar.
As shown in fig. 3, in the middle is a vehicle that provides a view, which is ready to come out of the parking space, when the vehicle speed is very slow. There is a stop on both the left and right sides of the vehicle that provides a view. VT1 is a vehicle running from left to right, but because there is a stop between VT1 and the line of sight-providing vehicle, the virtual scene of the stop is exactly at the position of the vehicle running from left to right, when represented on the spectrogram, the first harmonic point of the stop is exactly at the similar position to the target point detected by the vehicle running from left to right, so when the first harmonic point is processed, the amplitude parameter of the first harmonic point is obtained based on the parking lot model constructed by the S-V multi-inverse model according to the method, and the position of the first harmonic on the spectrogram is calculated. Thereby stripping the first harmonic from detection from the vehicle target point traveling from the left side to the right. The vehicle providing the visual field can sense the relative speed and distance relation of the vehicle, and a basis is provided for autonomous judgment of the vehicle. In addition, since the distance between the pedestrian (circle representation) located beside the VT1 vehicle and the sight-seeing vehicle is the same, the position of the target point characterizing the pedestrian in the spectrogram is similar to the position of the vehicle traveling from the left side to the right side. The moving direction of the pedestrian is different from that of the VT1 vehicle, and the pedestrian and the vehicle can be separated by combining the environment information, the clustering algorithm and the dynamic and static separation algorithm through the S-V multi-inverse model.
VT2 is a vehicle traveling at a low speed from left to right, and when the vehicle just passes the position shown in the figure for 20 seconds, the virtual scene position formed by the primary reflection of the detection point of the left parking is similar to the position of the left pedestrian (represented by a circle) on the spectrogram. The algorithm of the scheme is needed to separate vehicles which run at a low speed from left to right from left to left.
As shown in fig. 4, when a wall is blocked at a turn on the left side of a vehicle, and a vehicle providing a view detects the wall, a virtual view of the wall is formed behind a real wall due to the phenomenon of multiple reflections. If a moving pedestrian or vehicle appears at the virtual scene position of the wall, then the problem of how to retain the real pedestrian and vehicle is also involved in removing the virtual scene wall, particularly when multiple reflections produce multiple virtual scenes, on the extension line providing the line of sight of the vehicle and the wall target point, the obstacles, moving objects, etc. appearing at the position where the distance between the sight of the vehicle and the wall target point is equal to the distance between the sight of the vehicle and the wall target point will overlap with the position of the harmonic on the spectrogram. The waveform functions of the subharmonics need to be found out respectively and stripped in turn.
As shown in fig. 5, when the sight-seeing vehicle is ready to park, the vehicle is parked on both the left and right sides of the target parking space, but the intermediate parking space is provided with a ground lock although not parked. When the sight-seeing vehicle is driven to the current position, the sight-seeing vehicle detects a target point of left parking, and the first harmonic position of the target point is easy to find. However, if the position of the ground lock is exactly at the first harmonic position of the left vehicle, after the first harmonic is filtered, the target point of the detected ground lock is also filtered out due to the position on the spectrogram being close to the first harmonic position. Providing a field of view the vehicle does not sense the ground lock, but plans to stop to the parking space, and collision risks can occur. The ground lock needs to be obtained according to the S-V model. And effectively identifying the function of the harmonic wave when filtering the virtual scene harmonic wave, and carrying out harmonic wave and wave stripping according to the identification function. In this way, the detection point of the ground lock can be reserved, and the ground lock can be detected in advance by the auxiliary vehicle.
As shown in fig. 6, when the sight-seeing vehicle is ready to park, the vehicle is parked on both left and right sides of the target parking space, and the left vehicle opens the door. When the vehicle is driven to the current position, the vehicle detects a left parking target point, the first harmonic position of the target point is easy to find, and after the first harmonic is filtered, the target point of the ground lock is also filtered due to the fact that the position on the spectrogram is close to the first harmonic position. Providing a view that the vehicle is not aware of the door and planning to stop to that parking space would present a risk of collision with the door.
As shown in fig. 7-12, are slice diagrams in the intensity and distance dimensions in a 3-dimensional FFT. Is a slice in the intensity and velocity dimensions in a 3-dimensional FFT. Is a schematic of a 3-dimensional FFT. The method is characterized in that a spectrogram diagram related to a target point is extracted from a three-dimensional FFT, a pulse with highest intensity can be seen to represent the target point, a pulse with second highest intensity at the side surface of the target point represents a first reflection point, a scattering point corresponding to the first reflection point is arranged at a position close to the first reflection point, and a second reflection point is arranged at a reverse position which is approximately equal to the distance between the target point and the first reflection point from the first reflection point. The total amount of the spectrum of the target object in the three-dimensional fourier transform spectrogram=the target object target point spectrum+the spectrum amount of the target object reflection point in the spectrum+the spectrum amount of the moving object (pedestrian, bicycle).
Preferably, in the S-V multi-reflection model, more than one harmonic may appear on the extension line of the line of parking and providing the vision vehicle on the left side, and when the second harmonic, the third harmonic and the fourth harmonic appear, the virtual view generated by the harmonics can affect the detection of the obstacle which is positioned on the same radius with the vision vehicle as the center of the circle and is positioned on the virtual view of the second harmonic, the virtual view of the third harmonic and the virtual view of the fourth harmonic. Particularly when the relative velocity vector of the vehicle providing vision and left hand parking is also the same as the relative velocity vector of the vehicle providing vision and obstacle. The wave functions of the subharmonics are found by the algorithm of the scheme, and the second harmonic virtual view and the real obstacle, the third harmonic virtual view and the real obstacle and the fourth harmonic virtual view and the obstacle which are in similar positions in the spectrogram are separated respectively.
A computer storage medium for storing a software program corresponding to the above-described millimeter wave radar-based driving assistance method and/or a millimeter wave radar-based driving assistance system.
The above embodiments are merely illustrative of the principles of the present invention and its effectiveness, and are not intended to limit the invention. Modifications and variations may be made to the above-described embodiments by those skilled in the art without departing from the spirit and scope of the invention. Accordingly, it is intended that all equivalent modifications and variations of the invention be covered by the claims of this invention, which are within the skill of those skilled in the art, be included within the spirit and scope of this invention.
Claims (9)
1. An auxiliary driving system capable of suppressing short-range harmonics of a radar, comprising:
At least one transceiver configured to radiate a transmit radar signal and to receive an echo signal of the transmit radar signal;
the device comprises an original data processing unit, a target speed detection unit, a target distance detection unit and a target elevation unit, wherein the original data processing unit is used for preprocessing a transceiver transmitting/receiving signal to obtain original target point information including a target speed, a target distance, an azimuth angle and an elevation angle;
The target point extraction module is used for extracting the original target point information of interest;
The harmonic calculation module is used for calculating harmonic order numbers and harmonic amplitude values of the original target points under the corresponding scene, and separating harmonic parts and the corresponding harmonic order numbers of the target points from the original target point information;
and the moving object judging module is used for judging whether a moving object exists in the non-corresponding part or not when the amplitude of the specific position in the original target point is not corresponding to the harmonic part acquired by the harmonic calculation module of the position of the corresponding target point.
2. The radar closely harmonic suppression driving assist system according to claim 1, wherein the harmonic calculation module includes a harmonic order judgment module and a harmonic amplitude calculation module.
3. The assistant driving system for suppressing a near-distance harmonic of a radar according to claim 2, wherein the harmonic order judgment module is configured to judge an order of a harmonic generated by each target point in the original target point information and a position of each order harmonic.
4. The driving assist system for suppressing radar near field harmonics of claim 3, wherein said harmonic magnitude calculation module is configured to calculate the magnitude of the harmonic to which each of the order harmonic locations belongs.
5. The radar closely spaced harmonic suppression driver assistance system of claim 4, further comprising a harmonic correlation module for obtaining a correlation between the harmonic order determination module and the harmonic magnitude calculation module.
6. The driving assistance system for suppressing a radar near harmonic according to claim 5, wherein the association range obtained by the harmonic association module includes an association relation between whether the target point generates a harmonic, an order of generating the harmonic by each target point, a harmonic amplitude corresponding to each harmonic order by each target point, and each target point, the target point generating the harmonic, the harmonic order, and a harmonic corresponding amplitude corresponding to each order after the target point changes over time in the field of view.
7. The driving assistance system for suppressing a radar near field harmonic according to claim 1, wherein the target point signal is represented by the following formula:
Where An represents the strength of the signal, τ 0 is the initial round trip period, τ n represents the round trip period of the target n, μ represents the signal scan time factor, T PRI represents the response of the scan and pulse time interval, V d,n represents the doppler velocity, K represents the number of scans, f c0 represents the carrier frequency, f R,n represents the function of range and round trip period, and f D,n represents the function of velocity and dry period.
8. The driving assist system for suppressing a radar near field harmonic according to claim 7, wherein the moving object judging module is configured to judge the expression of the non-corresponding portion S PD when the non-corresponding portion is:
Where τ l represents the first reflected node arrival time, τ kl represents the arrival time of the kth illumination from node l; β kl is the corresponding exponentially decaying signal gain, where An represents the signal strength, τ 0 is the initial round trip period, τ n represents the round trip period of the target n, μ represents the signal scan time factor, T PRI represents the response of the scan and pulse time interval, V d,n represents the doppler velocity, K represents the number of scans, f c0 represents the carrier frequency, f R,n represents the function of range and round trip period, and f D,n represents the function of velocity and dry period.
9. The driving assistance system for suppressing a radar near field harmonic according to claim 8, wherein the entire echo signal of the target n is expressed as:
Where An represents the strength of the signal, τ 0 is the initial round trip period, τ n represents the round trip period of the target n, μ represents the signal scan time factor, T PRI represents the response of the scan and pulse time interval, V d,n represents the doppler velocity, K represents the number of scans, f c0 represents the carrier frequency, μ represents the signal scan time factor, the signal scan time factor is related to the bandwidth B and scan duration Ts, f R,n represents a function of range and round trip period, f D,n represents a function of velocity and dry period, S nst represents n reflection point lines of sight; τ l represents the first reflection node arrival time, τ kl represents the arrival time of the kth illumination from node l; beta kl is the corresponding exponentially decaying signal gain.
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| US5528246A (en) * | 1994-06-30 | 1996-06-18 | Kustom Signals, Inc. | Traffic radar with digital signal processing |
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| CN110058219A (en) * | 2018-12-30 | 2019-07-26 | 上海微波技术研究所（中国电子科技集团公司第五十研究所） | MIMO harmonic radar |
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| US5528246A (en) * | 1994-06-30 | 1996-06-18 | Kustom Signals, Inc. | Traffic radar with digital signal processing |
| CN106970360A (en) * | 2017-05-25 | 2017-07-21 | 哈尔滨工业大学 | A kind of pathfinder multiple reflections false echo suppressing method |
| CN110058219A (en) * | 2018-12-30 | 2019-07-26 | 上海微波技术研究所（中国电子科技集团公司第五十研究所） | MIMO harmonic radar |
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