CN111175714A - Driving assistance method capable of suppressing short-range harmonic waves of radar and storage medium - Google Patents
Driving assistance method capable of suppressing short-range harmonic waves of radar and storage medium Download PDFInfo
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- G—PHYSICS
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- 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
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- G—PHYSICS
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- 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
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- G01S7/415—Identification of targets based on measurements of movement associated with the target
Abstract
The invention provides an assistant driving method capable of inhibiting radar short-range harmonic waves and a storage medium, comprising the following steps of: information transmission/reception: the transceiver (Tx/Rx) is configured to radiate a transmitting radar signal and receive an echo signal of the transmitting radar signal, pre-process the transceiver (Tx/Rx) transmit/receive signal, obtain raw target point information including, but not limited to, target speed, target distance, azimuth and elevation; extracting interesting original target point information, calculating the harmonic order and the harmonic amplitude of the original target point in the scene, and separating the harmonic part and the corresponding harmonic order of each target point from the original target point information; and when the amplitude of the specific position in the original target point 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. The invention can inhibit harmonic waves and completely reserve key points of important objects such as pedestrians, bicycles and the like close to vehicles in the field of view.
Description
Technical Field
The invention relates to the technical field of automotive electronics, in particular to an auxiliary driving method capable of inhibiting radar short-range harmonic waves and a storage medium.
Background
As the driving Assistance technology is becoming more mature, the conventional sensing devices in Advanced Driver Assistance Systems (ADAS) and unmanned driving systems are visual sensing devices, ultrasonic radars and laser radars.
When the millimeter wave radar detects an obstacle, for example, a transversely passing vehicle is detected, the millimeter wave radar passes through the transmitter and repeatedly reflects waves in a low-speed scene when the waves transmitted by the transmitter encounter the transversely passing vehicle, and multiple harmonics are generated due to the repeated reflection. Harmonics are a disturbing factor, which is generated by reflections. The harmonics appear on the doppler spectrogram formed after the two-dimensional FFT as one or several reflection points, but the system cannot distinguish these 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 harmonic suppression, namely removing a plurality of reflection points of the target point through an algorithm. Although the probability of the false scene problem is reduced by millimeter wave harmonic suppression preprocessing, the millimeter wave point cloud data processing can remove key points of other important objects as reflection points by mistake in an actual scene due to a harmonic suppression algorithm. For example, when a pedestrian or a bicycle just passes by a vehicle passing transversely, and the pedestrian or the bicycle is monitored as an important object in the visual field, the target point representing the pedestrian or the bicycle is mistakenly removed when passing through the harmonic suppression algorithm. Therefore, the pedestrian or bicycle cannot be obtained by using the partial point cloud data as the original data to perform the clustering algorithm at the later stage. Therefore, how to achieve the purposes of suppressing harmonic waves and completely retaining key points of important objects such as pedestrians, bicycles and the like close to vehicles in a view field, namely removing 'virtual scene' points and retaining '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 assistant driving method and a storage medium capable of suppressing radar short-range harmonics, which solve the problem that although millimeter wave harmonic suppression preprocessing reduces the probability of occurrence of false scene problems, the processing of millimeter wave point cloud data in an actual scene can remove key points of other important objects as reflection points by mistake due to a harmonic suppression algorithm.
A driving assistance system that can suppress radar close-range harmonics, comprising:
at least one transceiver (Tx/Rx) configured to radiate a transmit radar signal and to receive an echo signal of the transmit 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 velocity, target distance, azimuth and elevation;
a target point extraction module which extracts original target point information of interest;
the harmonic order calculation module is used for calculating the harmonic order and the harmonic amplitude of the original target point in the scene, and separating the harmonic part of each target point and the corresponding harmonic order 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 does not correspond 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 determination module is configured to determine an order of harmonic generated by each target point in the original target point information and a position of each order harmonic.
Further, the harmonic amplitude calculation module is configured to calculate amplitudes of the harmonics to which the harmonic positions of the respective orders belong.
And the harmonic correlation module is used for acquiring the correlation between the harmonic order 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 points generate harmonics, the order of the harmonics generated by each target point, the harmonic amplitude corresponding to each harmonic order generated by each target point, and the association relationship among the harmonics generated by each target point, the harmonic orders generated by the target points, and the amplitudes corresponding to the harmonics of each order after the target points change within the field range extending 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 harmonic part of the target point position, and outputs the analysis result to the moving object judgment module.
And 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 inputs the input data into the scene multi-inverse model to obtain the harmonic order and the harmonic amplitude of each order of each target point.
Further, the scene multiple inverse model is used for representing the reflection relation of each reflector and reflection position of a specific scene, and the scene multiple inverse model may be an S-V multiple inverse model or a multiple inverse model reflecting the reflection relation of the specific scene obtained according to actual measurement of the specific scene.
Further, the standard scene multi-inverse model extracts characteristic parameters according to the measured scene environment, and determines the scene coverage and height.
Furthermore, the S-V multi-inverse model extracts characteristic parameters by a millimeter wave radar through measuring the environment to obtain a standard model, the standard model extracts the environment of the parking lot according to the measurement result, and the coverage range is 7-20 m and is up to 10 Ghz.
For example, the measuring range of the office environment model is 3-28 m and 2-8Ghz, and the measuring range is 5-17 m and is as high as 3-6Ghz for outdoor environment model. The industrial model environment is extracted according to the measurement result, the coverage range is 3-10Ghz, and the distance is 2-8 meters.
Further, the scene multiple inverse model comprises one or more standard scene multiple inverse models and/or a specific scene multiple inverse model.
Furthermore, the scene is divided into a standard area and a specific area by the specific scene multi-inverse model, the standard area extracts characteristic parameters according to the measured scene environment, and modeling is specifically carried out according to the reflection relation of the reflectors at specific positions.
Further, an evaluation module is further included, 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 characteristic according to the characteristics including but not limited to path loss characteristics, amplitude distribution characteristics, clustering characteristics and reflection characteristics.
Further, the impact of the path loss characteristics on the evaluation module's evaluation is that implementing this model of the path loss characteristics on a computer involves generating N correlated lognormal variables representing N different groups, and then applying the appropriate path loss distance between the antennas around the body. This can be done by generating N related 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 of each group, without reproducing the covariance matrix C.
Further, after the multiple inverse models of the standard scene form a parameterized channel, the multiple inverse models can be used for generating a set of impulse responses, and in turn, used for testing the structural performance of different millimeter wave transceivers.
Such as typical indoor and outdoor environments, compares the generic channel model with the fact model by characteristics of path loss, amplitude distribution, clustering, etc., and simulates and measures differences between the channel model and the fact model to modify the channel model.
The system further comprises a correction channel model, wherein the correction channel model is used for correcting one or more of path loss characteristics, amplitude distribution characteristics, clustering characteristics and reflection characteristic characteristics in the standard scene multi-inverse model and the specific scene multi-inverse model.
An assistant driving method capable of suppressing radar short-distance harmonic waves comprises the following steps:
s01: information transmission/reception: the transceiver (Tx/Rx) is configured to radiate a transmitting radar signal and receive an echo signal of the transmitting radar signal, pre-process the transceiver (Tx/Rx) transmit/receive signal, obtain original target point information including, but not limited to, target speed, target distance, azimuth and elevation;
s02: extracting interesting original target point information, calculating the harmonic order and the harmonic amplitude of the original target point in the scene, and separating the harmonic part and the corresponding harmonic order of each target point from the original target point information;
s03: and 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 corresponding part.
The harmonic order judging module is used for judging the order of harmonic waves generated by each target point in the original target point information and the position of each order harmonic wave.
And further, the harmonic amplitude calculation module is used for calculating the amplitude of the harmonic belonging to each order harmonic position.
And the harmonic correlation module is used for acquiring the correlation between the harmonic order 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 points generate harmonics, the order of the harmonics generated by each target point, the harmonic amplitude corresponding to each harmonic order generated by each target point, and the association relationship among the harmonics generated by each target point, the harmonic orders generated by the target points, and the amplitudes corresponding to the harmonics of each order after the target points change within the field range extending 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 harmonic part of the target point position, and outputs the analysis result to the moving object judgment module.
And 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 inputs the input data into the scene multi-inverse model to obtain the harmonic order and the harmonic amplitude of each order of each target point.
Further, the scene multiple inverse model is used for representing the reflection relation of each reflector and reflection position of a specific scene, and the scene multiple inverse model may be an S-V multiple inverse model or a multiple inverse model reflecting the reflection relation of the specific scene obtained according to actual measurement of the specific scene.
Further, the standard scene multi-inverse model extracts characteristic parameters according to the measured scene environment, and determines the scene coverage and height.
Furthermore, the S-V multi-inverse model extracts characteristic parameters by a millimeter wave radar through measuring the environment to obtain a standard model, the standard model extracts the environment of the parking lot according to the measurement result, and the coverage range is 7-20 m and is up to 10 Ghz.
For example, the measuring range of the office environment model is 3-28 m and 2-8Ghz, and the measuring range is 5-17 m and is as high as 3-6Ghz for outdoor environment model. The industrial model environment is extracted according to the measurement result, the coverage range is 3-10Ghz, and the distance is 2-8 meters.
Further, the scene multiple inverse model comprises one or more standard scene multiple inverse models and/or a specific scene multiple inverse model.
Furthermore, the scene is divided into a standard area and a specific area by the specific scene multi-inverse model, the standard area extracts characteristic parameters according to the measured scene environment, and modeling is specifically carried out according to the reflection relation of the reflectors at specific positions.
Further, an evaluation module is further included, 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 characteristic according to the characteristics including but not limited to path loss characteristics, amplitude distribution characteristics, clustering characteristics and reflection characteristics.
Further, the impact of the path loss characteristics on the evaluation module's evaluation is that implementing this model of the path loss characteristics on a computer involves generating N correlated lognormal variables representing N different groups, and then applying the appropriate path loss distance between the antennas around the body. This can be done by generating N related 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 of each group, without reproducing the covariance matrix C.
Further, after the multiple inverse models of the standard scene form a parameterized channel, the multiple inverse models can be used for generating a set of impulse responses, and in turn, used for testing the structural performance of different millimeter wave transceivers.
Such as typical indoor and outdoor environments, compares the generic channel model with the fact model by characteristics of path loss, amplitude distribution, clustering, etc., and simulates and measures differences between the channel model and the fact model to modify the channel model.
The system further comprises a correction channel model, wherein the correction channel model is used for correcting one or more of path loss characteristics, amplitude distribution characteristics, clustering characteristics and reflection characteristic characteristics in the standard scene multi-inverse model and the specific scene multi-inverse model.
An application of a driving-assistant system capable of suppressing short-range harmonic waves of radar,
in a driving assistance system:
at least one transceiver 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 the transmitting/receiving signals of the transceiver (Tx/Rx) and obtaining original target point information including but not limited to target speed, target distance, azimuth angle and elevation angle;
the target point extracting module extracts interesting original target point information;
the harmonic calculation module is used for calculating the harmonic order and the harmonic amplitude of the original target point in the scene, and separating the harmonic part and the corresponding harmonic order of each target point from the information of the original target point;
the comparison module is used for comparing the original target point information acquired by the target point extraction module with the harmonic part of each target point acquired by the harmonic calculation module and the corresponding harmonic order information, 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;
and 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 is used for storing a software program corresponding to the driving assisting method capable of suppressing the short-distance harmonic waves of the radar and/or a driving assisting system capable of suppressing the short-distance harmonic waves of the radar.
As described above, the present invention has the following advantageous effects:
when a pedestrian or a bicycle just passes by the vehicle passing transversely and needs to be monitored as an important object in the visual field, the target point representing the pedestrian or the bicycle is mistakenly removed when passing through the harmonic suppression algorithm. Therefore, the pedestrian or the bicycle cannot be obtained by using the partial point cloud data as the original data to perform the clustering algorithm in the later period. By utilizing the method, not only can harmonic waves (including multiple harmonic waves) be inhibited, but also key points of other important objects such as pedestrians, bicycles and the like close to the vehicle in the field of view can be completely reserved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a schematic diagram of an embodiment of the present invention.
FIG. 2 is a schematic view of another embodiment.
FIG. 3 is a schematic diagram of another embodiment.
FIG. 4 is a schematic view of another embodiment.
FIG. 5 is a schematic view of another embodiment.
FIG. 6 is a schematic view of another embodiment.
Fig. 7 is shown as a slice in the intensity and distance dimensions in a three-dimensional FFT.
Fig. 8 is shown as 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 is a schematic diagram of a two-dimensional FFT.
Fig. 11 is a schematic diagram of a two-dimensional FFT.
Fig. 12 shows a schematic diagram of a two-dimensional FFT.
FIG. 13 is a graph showing the exponential decay and power of the reflection in the S-V multiple inverse model.
FIG. 14 is a diagram of the impulse response of the S-V multiple inverse model.
Fig. 15 shows the two-dimensional FFT-derived range-Doppler spectra after K FMCW snapshots for target n.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
It should be understood that the structures, ratios, sizes, and the like shown in the drawings and described in the specification are only used for matching with the disclosure of the specification, so as to be understood and read by those skilled in the art, and are not used to limit the conditions under which the present invention can be implemented, so that the present invention has no technical significance, and any structural modification, ratio relationship change, or size adjustment should still fall within the scope of the present invention without affecting the efficacy and the achievable purpose of the present invention. In addition, the terms "upper", "lower", "left", "right", "middle" and "one" used in the present specification are for clarity of description, and are not intended to limit the scope of the present invention, and the relative relationship between the terms and the terms is not to be construed as a scope of the present invention.
With reference to figures 1 to 15 of the drawings,
a driving assistance system that can suppress radar close-range harmonics, comprising:
at least one transceiver (Tx/Rx) configured to radiate a transmit radar signal and to receive an echo signal of the transmit 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 velocity, target distance, azimuth and elevation;
a target point extraction module which extracts original target point information of interest;
the harmonic order calculation module is used for calculating the harmonic order and the harmonic amplitude of the original target point in the scene, and separating the harmonic part of each target point and the corresponding harmonic order 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 does not correspond 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 determination module is configured to determine an order of harmonic generated by each target point in the original target point information and a position of each order harmonic.
Further, the harmonic amplitude calculation module is configured to calculate amplitudes of the harmonics to which the harmonic positions of the respective orders belong.
And the harmonic correlation module is used for acquiring the correlation between the harmonic order 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 points generate harmonics, the order of the harmonics generated by each target point, the harmonic amplitude corresponding to each harmonic order generated by each target point, and the association relationship among the harmonics generated by each target point, the harmonic orders generated by the target points, and the amplitudes corresponding to the harmonics of each order after the target points change within the field range extending 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 harmonic part of the target point position, and outputs the analysis result to the moving object judgment module.
And 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 inputs the input data into the scene multi-inverse model to obtain the harmonic order and the harmonic amplitude of each order of each target point.
Further, the scene multiple inverse model is used for representing the reflection relation of each reflector and reflection position of a specific scene, and the scene multiple inverse model may be an S-V multiple inverse model or a multiple inverse model reflecting the reflection relation of the specific scene obtained according to actual measurement of the specific scene.
Further, the standard scene multi-inverse model extracts characteristic parameters according to the measured scene environment, and determines the scene coverage and height.
Furthermore, the S-V multi-inverse model extracts characteristic parameters by a millimeter wave radar through measuring the environment to obtain a standard model, the standard model extracts the environment of the parking lot according to the measurement result, and the coverage range is 7-20 m and is up to 10 Ghz.
For example, the measuring range of the office environment model is 3-28 m and 2-8Ghz, and the measuring range is 5-17 m and is as high as 3-6Ghz for outdoor environment model. The industrial model environment is extracted according to the measurement result, the coverage range is 3-10Ghz, and the distance is 2-8 meters.
Further, the scene multiple inverse model comprises one or more standard scene multiple inverse models and/or a specific scene multiple inverse model.
Furthermore, the scene is divided into a standard area and a specific area by the specific scene multi-inverse model, the standard area extracts characteristic parameters according to the measured scene environment, and modeling is specifically carried out according to the reflection relation of the reflectors at specific positions.
Further, an evaluation module is further included, 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 characteristic according to the characteristics including but not limited to path loss characteristics, amplitude distribution characteristics, clustering characteristics and reflection characteristics.
Further, the impact of the path loss characteristics on the evaluation module's evaluation is that implementing this model of the path loss characteristics on a computer involves generating N correlated lognormal variables representing N different groups, and then applying the appropriate path loss distance between the antennas around the body. This can be done by generating N related 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 of each group, without reproducing the covariance matrix C.
Further, after the multiple inverse models of the standard scene form a parameterized channel, the multiple inverse models can be used for generating a set of impulse responses, and in turn, used for testing the structural performance of different millimeter wave transceivers.
Such as typical indoor and outdoor environments, compares the generic channel model with the fact model by characteristics of path loss, amplitude distribution, clustering, etc., and simulates and measures differences between the channel model and the fact model to modify the channel model.
The system further comprises a correction channel model, wherein the correction channel model is used for correcting one or more of path loss characteristics, amplitude distribution characteristics, clustering characteristics and reflection characteristic characteristics in the standard scene multi-inverse model and the specific scene multi-inverse model.
An assistant driving method capable of suppressing radar short-distance harmonic waves comprises the following steps:
s01: information transmission/reception: the transceiver (Tx/Rx) is configured to radiate a transmitting radar signal and receive an echo signal of the transmitting radar signal, pre-process the transceiver (Tx/Rx) transmit/receive signal, obtain original target point information including, but not limited to, target speed, target distance, azimuth and elevation;
s02: extracting interesting original target point information, calculating the harmonic order and the harmonic amplitude of the original target point in the scene, and separating the harmonic part and the corresponding harmonic order of each target point from the original target point information;
s03: and 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 corresponding part.
The harmonic order judging module is used for judging the order of harmonic waves generated by each target point in the original target point information and the position of each order harmonic wave.
And further, the harmonic amplitude calculation module is used for calculating the amplitude of the harmonic belonging to each order harmonic position.
And the harmonic correlation module is used for acquiring the correlation between the harmonic order 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 points generate harmonics, the order of the harmonics generated by each target point, the harmonic amplitude corresponding to each harmonic order generated by each target point, and the association relationship among the harmonics generated by each target point, the harmonic orders generated by the target points, and the amplitudes corresponding to the harmonics of each order after the target points change within the field range extending 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 harmonic part of the target point position, and outputs the analysis result to the moving object judgment module.
And 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 inputs the input data into the scene multi-inverse model to obtain the harmonic order and the harmonic amplitude of each order of each target point.
Further, the scene multiple inverse model is used for representing the reflection relation of each reflector and reflection position of a specific scene, and the scene multiple inverse model may be an S-V multiple inverse model or a multiple inverse model reflecting the reflection relation of the specific scene obtained according to actual measurement of the specific scene.
Further, the standard scene multi-inverse model extracts characteristic parameters according to the measured scene environment, and determines the scene coverage and height.
Furthermore, the S-V multi-inverse model extracts characteristic parameters by a millimeter wave radar through measuring the environment to obtain a standard model, the standard model extracts the environment of the parking lot according to the measurement result, and the coverage range is 7-20 m and is up to 10 Ghz.
For example, the measuring range of the office environment model is 3-28 m and 2-8Ghz, and the measuring range is 5-17 m and is as high as 3-6Ghz for outdoor environment model. The industrial model environment is extracted according to the measurement result, the coverage range is 3-10Ghz, and the distance is 2-8 meters.
Further, the scene multiple inverse model comprises one or more standard scene multiple inverse models and/or a specific scene multiple inverse model.
Furthermore, the scene is divided into a standard area and a specific area by the specific scene multi-inverse model, the standard area extracts characteristic parameters according to the measured scene environment, and modeling is specifically carried out according to the reflection relation of the reflectors at specific positions.
Further, an evaluation module is further included, 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 characteristic according to the characteristics including but not limited to path loss characteristics, amplitude distribution characteristics, clustering characteristics and reflection characteristics.
Further, the impact of the path loss characteristics on the evaluation module's evaluation is that implementing this model of the path loss characteristics on a computer involves generating N correlated lognormal variables representing N different groups, and then applying the appropriate path loss distance between the antennas around the body. This can be done by generating N related 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 of each group, without reproducing the covariance matrix C.
Further, after the multiple inverse models of the standard scene form a parameterized channel, the multiple inverse models can be used for generating a set of impulse responses, and in turn, used for testing the structural performance of different millimeter wave transceivers.
Such as typical indoor and outdoor environments, compares the generic channel model with the fact model by characteristics of path loss, amplitude distribution, clustering, etc., and simulates and measures differences between the channel model and the fact model to modify the channel model.
The system further comprises a correction channel model, wherein the correction channel model is used for correcting one or more of path loss characteristics, amplitude distribution characteristics, clustering characteristics and reflection characteristic characteristics in the standard scene multi-inverse model and the specific scene multi-inverse model.
An application of a driving-assistant system capable of suppressing short-range harmonic waves of radar,
in a driving assistance system:
at least one transceiver 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 the transmitting/receiving signals of the transceiver (Tx/Rx) and obtaining original target point information including but not limited to target speed, target distance, azimuth angle and elevation angle;
the target point extracting module extracts interesting original target point information;
the harmonic calculation module is used for calculating the harmonic order and the harmonic amplitude of the original target point in the scene, and separating the harmonic part and the corresponding harmonic order of each target point from the information of the original target point;
and 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 is used for storing a software program corresponding to the driving assisting method capable of suppressing the short-distance harmonic waves of the radar and/or a driving assisting system capable of suppressing the short-distance harmonic waves of the radar.
Under the condition that a 77GHz millimeter wave radar is applied to ADAS high-speed scenes and low-speed scenes such as automatic parking, a radar transmitting signal can be reflected for many times in a strong detection target (strong RCS), if a vehicle, a guardrail, a metal roadblock in a parking lot and the like, because the automobile radar is designed by adopting FMCW chirp-sequence waveforms mostly, the mathematical expression form of the transmitting signal is
Wherein A isTxRepresenting the strength of the transmitted signal, fc0Representing carrier frequency，
Representing an initial phase, mu represents a signal scanning time factor, the signal scanning time factor is related to a bandwidth B and a scanning duration Ts, wherein mu is B/Ts, at a time t, a transmitting signal is propagated for n times to obtain a strong detection target passing through a channel h (t), a receiver receives an extra virtual scene generated by repeated reflection, and the propagation of the two signals and a difference frequency signal have mathematical relations that:
Sbeat＝(((sTx(t)*h(t))·σRCS,n(t)*h(t))·sTx(t))*lp(t), (2)
wherein, the inside of the brace is respectively convolution operation and channel response function operation variable h (t), sigmaRCS，n(t)The target RCS is detected and the low pass filter function lp (t) is applied to equation (2), and the reduced converted difference frequency signal is written as:
where An denotes the intensity of the signal, τnRepresenting the round-trip period of target n, a radar snapshot consisting of K scans lasting several milliseconds, where the round-trip time of the signal of target n can be expressed as:
τn＝τ0+vD,n·K·TPRI(4)
wherein tau is0VD, n, K and TPRI are the responses of the initial round trip period, doppler velocity, number of scans, scan and pulse time intervals, respectively. Thus, the expression of the difference signal of the target n in the frequency domain, like equation (2), ω (t) represents a window function. Between the doppler frequency range fR to fD. Considering that under complex scenes, the echo signal can comprise multiple reflections, combined with [ S-V ]]Can express the whole echo signal of the target n as
Wherein
representing the target point signal portion, Tl, τ kl representing the arrival time of the l-th reflection node and the arrival time of the kth illumination from node l. β kl is the corresponding exponentially decaying signal gain the model obtained from equation (6) can directly deduce the time-frequency response of the 1, 2, …, n harmonics from the target signal of the first portion, where the signal gain can be calibrated using channel measurements, such as [ Molisch ]]The calibration can be classified according to scenes, such as parking lots, outdoors and the like.
As shown in the figure, a specific first harmonic suppression method is provided, which can suppress multiple harmonics in a complex environment as shown in equation (5), and is not limited to the scheme shown in fig. 15.
As shown in the figure, the 1 st harmonic of the range-Doppler spectrum obtained by the two-dimensional FFT of the target n after the K-times FMCW snapshot is located at the same position as the target m, and according to the formula (6) and the above calibration scheme, all harmonic signals (including the amplitude and the phase) of the range-Doppler spectrum can be calculated according to the target point signal portion (Line-of-Sight) of the target n, so that the position of the harmonic interference of the whole target n (such as the interference of the target m and the first harmonic) can be suppressed by simple complex subtraction, and the correct amplitude and phase of the target m can be obtained.
In one embodiment, as shown in fig. 1, when the current vehicle is traveling directly upward and there is a vehicle traveling laterally in front of the current vehicle, when the millimeter-wave radar detects a target point, the position of a first harmonic wave generated in the direction of an extended line of equal distance behind the target point is shown by a dotted line, and the position of the first harmonic wave on the spectrogram is the target point of a pedestrian directly in front of the current vehicle, which is shown by a circle and is located at the same position on the spectrogram. As shown in the figure, the ordinate represents doppler velocity, the abscissa represents relative distance, and then, the position is close to the origin, the node is represented by a triangle symbol as a target point (LOS1) which is closest to the current vehicle and is located on the cross vehicle, the node is represented by a cross symbol as a target point (LOS2) which is slightly distant from the current vehicle, the triangle symbol point which is located on the extension line of the target point (LOS1) and is equidistant from the target point (LOS1) and the vehicle providing the field of view is a first virtual-view harmonic point of the target point (LOS1), and the circle at the same position as the first virtual-view harmonic point represents a pedestrian. The cross sign at the position on the extended line of the target point (LOS2) equidistant from the vehicle providing the field of view (LOS2) is the first virtual-view harmonic point of the target point (LOS2), and the circle at the same position as the first virtual-view harmonic point represents a pedestrian.
In one embodiment, as shown in fig. 2, when the current vehicle is traveling directly above and there is a longitudinally opposing vehicle in front of the current vehicle, when the millimeter wave radar detects a target point, due to multiple reflections of the wave, the position of a first harmonic wave generated in the direction of an extended line at an equal distance behind the target point is shown by a dotted line, and the position of the first harmonic wave on the spectrogram is the same as that of a pillar located directly in front of the current vehicle as the target point, which is shown by a circle and located above the spectrogram. As shown, the ordinate represents doppler velocity and the abscissa represents relative distance, then close to the origin, when a vehicle providing a field of view is traveling at a very slow speed, it can be detected that the mast is stationary at doppler velocity as a target, but it can be seen that there are two pulses in the direction of distance, the first pulse being the target point (LOS1) found on the longitudinal vehicle body, and the second pulse being the first harmonic point of the first pulse, where the true target point (LOS2) formed by the mast is also included, and the portion of the second pulse associated with the first pulse can be removed, leaving the portion of the true target point formed by the mast.
As shown in fig. 3, the center is the vehicle providing the field of view, which is ready to come out of the parking space, when the vehicle speed is very slow. There are stops on both the left and right sides of the vehicle providing a field of view. The VT1 is a vehicle traveling from left to right, but since there is a parking between the VT1 and the vehicle connecting line providing the field of view, and the virtual scene of the parking is just at a position close to the target point detected by the vehicle traveling from left to right when the virtual scene is represented on the spectrogram, when the first harmonic point is processed, it is necessary to obtain the amplitude parameter of the first harmonic point based on the parking lot model constructed by the S-V multiple inverse model according to the above method, and further calculate the position of the first harmonic on the spectrogram. Thereby stripping the first harmonic from the vehicle target point traveling from the left to the right. The relative speed and distance relation of the vehicle can be sensed by the vehicle providing the visual field, and a basis is provided for the autonomous judgment of the vehicle. In addition, a pedestrian (represented by a circle) beside the VT1 vehicle is located at the same distance from the vehicle providing the field of view, so the target point representing the pedestrian is located in the spectrogram close to the position of the vehicle traveling from left to right. And the moving direction of the pedestrian is different from that of the VT1 vehicle, the pedestrian and the vehicle can be separated by combining environmental information, a clustering algorithm and a dynamic-static separation algorithm through an S-V multiple inverse model.
VT2 is a vehicle traveling at low speed from left to right, and the virtual scene position formed by the first reflection at the detection point of the left stop is similar to the position of the left pedestrian (indicated by a circle) on the spectrogram within 20 seconds of the vehicle just passing the position shown in the figure. The algorithm of the scheme is required to be used for respectively stripping the left-side pedestrian and the vehicle running at a low speed from left to right.
As shown in fig. 4, a turning part on the left side of the vehicle is blocked by a wall, and when the vehicle providing the field of view detects the wall, a virtual scene with the wall appears behind a real wall due to a multiple reflection phenomenon. If moving pedestrians or vehicles are present at the virtual scene position of the wall, then when the virtual scene wall is removed, the problem of how to retain real pedestrians and vehicles is also involved, and particularly when multiple reflections generate multiple virtual scenes, on an extension line of a connecting line between a vehicle providing a visual field and a wall target point, obstacles, moving objects and the like present at positions with a distance equal to the distance between the vehicle providing the visual field and the wall target point are overlapped with the positions of the harmonics on the frequency spectrogram. The waveform functions of the harmonics need to be found out respectively and stripped sequentially.
As shown in fig. 5, when the vehicle is ready to park in the sight-providing space, the vehicle is parked in both the left and right sides of the target parking space, but the intermediate parking space is not parked but is provided with a ground lock. When the sight-providing vehicle travels to the current position, it detects a target point for left-side parking, the first harmonic position of which is easily found. 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 ground lock is also filtered due to the position on the spectrogram close to the first harmonic position. The vehicle is not aware of the ground lock in the visual field, and is planned to stop to the parking space, so that the collision risk can occur. It is necessary to obtain a ground lock according to the S-V model. And when the virtual scene harmonic wave is filtered, the function of the harmonic wave is effectively identified, and the harmonic wave and the wave are stripped according to the identification function. Therefore, the detection point of the ground lock can be reserved, and the auxiliary vehicle can detect the ground lock in advance.
As shown in fig. 6, when the vehicle is ready to park in the sight-providing space, the vehicle is parked at both the left and right sides of the target parking space, and the vehicle at the left side is opened. When the vehicle with the visual field drives to the current position, the target point of left-side parking is detected, 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 of the target point on the frequency spectrogram is close to the first harmonic position. The vehicle is not aware of the door in the field of vision, and is planned to stop to the parking space, so that the risk of collision with the door can occur.
As shown in fig. 7-12, are slices in the intensity and distance dimensions of 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 related to a target point is extracted from three-dimensional FFT, the graph can see a pulse with the highest intensity to represent the target point, the pulse with the second highest intensity on 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 with the distance from the first reflection point approximately equal to the distance from the target point to the first reflection point. The total frequency spectrum of the target object in the three-dimensional Fourier transform spectrogram is equal to the frequency spectrum of the target object point, the frequency spectrum number of the target object reflection point in the frequency spectrum and the number of moving objects (pedestrians and bicycles) in the frequency spectrum.
Preferably, in the S-V multiple inverse model, when the second harmonic, the third harmonic, and the fourth harmonic occur, virtual scenes generated by the harmonics affect the detection of the obstacle located at the center of the vehicle with the view field provided as the center and located at the same radius as the second harmonic virtual scene, the third harmonic virtual scene, and the fourth harmonic virtual scene. Particularly when the relative velocity vector of the vehicle providing the field of view and the left side parking is also the same as the relative velocity vector of the vehicle providing the field of view and the obstacle. And similarly, respectively finding the wave function of each subharmonic by the algorithm of the scheme, and respectively separating the second harmonic virtual scene and the real obstacle, the third harmonic virtual scene and the real obstacle, and the fourth harmonic virtual scene and the obstacle which are positioned at similar positions in the frequency spectrogram.
A computer storage medium is used for storing a software program corresponding to the millimeter wave radar-based driving assistance method and/or a millimeter wave radar-based driving assistance system.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which may be accomplished by those skilled in the art without departing from the spirit and scope of the present invention as set forth in the appended claims.
Claims (10)
1. An assistant driving method capable of suppressing radar short-range harmonics is characterized by comprising the following steps:
s01: information transmission/reception: the transceiver (Tx/Rx) is configured to radiate a transmitting radar signal and receive an echo signal of the transmitting radar signal, pre-process the transceiver (Tx/Rx) transmit/receive signal, 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 the harmonic order and the harmonic amplitude of the original target point in the scene, and separating the harmonic part and the corresponding harmonic order of each target point from the original target point information;
s03: and when the amplitude of the specific position in the original target point 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.
2. The driving assistance method capable of suppressing the radar close-range harmonics according to claim 1, wherein the portion of calculating the harmonic order and the harmonic amplitude of the original target point in the scene in step S02 is a harmonic calculation module, and the harmonic calculation module includes a harmonic order determination module and a harmonic amplitude calculation module.
3. The driving assist method capable of suppressing radar close-range harmonics according to claim 2, wherein the harmonic order determination module is configured to determine the order of harmonics generated at each target point in the original target point information and the position of each order harmonic.
4. The driving assist method capable of suppressing radar close-range harmonics according to claim 3, wherein the harmonic amplitude calculation module is configured to calculate the amplitude of the harmonic to which each order harmonic position belongs.
5. The assistant driving method capable of suppressing the radar close-range harmonics according to claim 4, further comprising a harmonic correlation module for obtaining a correlation between the harmonic order determination module and the harmonic amplitude calculation module.
6. The method as claimed in claim 5, wherein the association range obtained by the harmonic association module includes, but is not limited to, whether the target points generate harmonics, the order of harmonics generated at each target point, the harmonic amplitudes corresponding to each harmonic order generated at each target point, and the association relationship among the target points, the harmonics generated at the target points, the harmonic orders, and the harmonic amplitudes corresponding to each order after the target points are changed within the visual field as time goes on.
7. The driving assistance method capable of suppressing the short-range harmonics of radar according to claim 1, wherein the sum S of the target point information and the harmonic calculation module informationnstRepresented by the following formula:
where An denotes the intensity of the signal, τ0Is the initial round trip period, τnDenotes the round trip period of the target n, mu denotes the signal scan time factor, TPRIRepresenting the response of the scanning and pulse time intervals, VD, n the Doppler velocity, K the number of scans, fc0Which is indicative of the carrier frequency,
representing the initial phase, mu representing a signal scan time factor, the signal scan time factor being related to the bandwidth B and the scan duration Ts, fR，nFunction representing range and round trip period, fD，nFunction representing speed and dry period, SnstN reflection point lines of sight are characterized.
8. The driving assistance method according to claim 1, wherein the moving object determining module is configured to determine the non-corresponding portion S when determining the non-corresponding portionPDExpression (c):
wherein T isl,τklRepresents the arrival time of the l-th reflection node and the k-th time from the node ltime of arrival of irradiation,. betaklFor a corresponding exponentially decaying signal gain.
9. The method of claim 8, wherein the echo signal of the transmitted radar signal is calculated according to the sum S of the target point information and the harmonic calculation module informationnst(ii) a The moving object judging module is used for judging the non-corresponding part S when the non-corresponding part is judgedPDCan be expressed as:
where An denotes the intensity of the signal, τ0Is the initial round trip period, τnDenotes the round trip period of the target n, mu denotes the signal scan time factor, TPRIRepresenting the response of the scanning and pulse time intervals, VD, n the Doppler velocity, K the number of scans, fc0Which is indicative of the carrier frequency,
representing the initial phase, mu representing a signal scan time factor, the signal scan time factor being related to the bandwidth B and the scan duration Ts, fR，nFunction representing range and round trip period, fD，nFunction representing speed and dry period, SnstCharacterizing n reflection point lines of sight; wherein T isl,τklrepresenting the arrival time of the l-th reflection node and the arrival time of the k-th illumination from node l, βklFor a corresponding exponentially decaying signal gain.
10. A computer storage medium is used for storing a software program corresponding to the driving assistance method capable of suppressing the short-range harmonics of the radar.
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