CN111830469A - Classification, identification and filtering method for noise data of vehicle-mounted millimeter wave radar - Google Patents

Abstract

The invention provides a classification, identification and filtering method for noise data of a vehicle-mounted millimeter wave radar, which realizes classification, identification and filtering of noise data of the vehicle-mounted millimeter wave radar by identifying and filtering invalid noise points of the millimeter wave radar, identifying and filtering virtual noise points of the millimeter wave radar and identifying and filtering longitudinal drift points of the millimeter wave radar.

Description

Classification, identification and filtering method for noise data of vehicle-mounted millimeter wave radar

Technical Field

The invention belongs to the field of automobiles, and particularly relates to a classification, identification and filtering method for noise data of a vehicle-mounted millimeter wave radar.

Background

Under the prior art conditions, the vehicle-mounted millimeter wave radar is generally considered to be not affected by bad weather and can work all the day long, but in the actual millimeter wave radar road test, if the vehicle-mounted millimeter wave radar sputters against raindrops in a front short-distance range (5 meters to 30 meters) on an urban road in the rainy weather, particularly the bad weather of heavy rain, a stable reflection section can be generated, and a reflection point shows drift motion from top to bottom. Therefore, although the OEM manufacturer of the vehicle-mounted millimeter wave radar can perform algorithm processing on original data of the millimeter wave radar and can output data and tracking information of a target level, in the data of the target level of the millimeter wave radar, due to various refraction, reflection and diffraction effects of the radar wave, a large amount of target noise data can be generated, noise data of the millimeter wave radar is generally subjected to noise filtering in a mobileye intelligent visual camera or laser radar fusion mode, but both the intelligent visual camera sensor and the laser radar sensor have high use cost and are not suitable for mass production popularization.

Further, the false alarm rate of the ADAS function or the automatic driving function of the existing mass production vehicle based on the millimeter wave radar has a large rate of influence from the noise data of the millimeter wave radar.

Disclosure of Invention

In view of the above, the invention aims to provide a classification, identification and filtering method for vehicle-mounted millimeter wave radar noise data, which has the characteristics of accurate radar noise data identification, high filtering and calculating speed and wide application scenes.

In order to achieve the purpose, the technical scheme of the invention is realized as follows:

a classification, identification and filtering method for noise data of a vehicle-mounted millimeter wave radar comprises the following steps:

step 1: identification and filtering of invalid noise points of the millimeter wave radar:

parameters in the millimeter wave radar data are: synthesizing a relative distance, a longitudinal relative speed, a longitudinal relative acceleration, a horizontal azimuth angle and a transverse relative speed, limiting the relative distance, the longitudinal relative speed, the longitudinal relative acceleration, the horizontal azimuth angle and the transverse relative speed in a value range, and identifying and filtering invalid noise points;

step 2: recognizing and filtering the virtual noise point of the millimeter wave radar:

firstly, judging whether a virtual scenic spot exists in a current frame, comparing the matching degree of millimeter wave radar signal information pairwise, and if the target feedback points of two millimeter wave radars meet the following five conditions, judging that the two target feedback points are virtual and real point pairs:

longitudinal distance information: (CAN _ TX _ TRACK _ RANGE _ sin (CAN _ TX _ TRACK _ ANGLE)) <0.1 m;

longitudinal velocity information: (CAN _ TX _ TRACK _ RANGE _ RATE) <0.01 m/s;

longitudinal acceleration information: (CAN _ TX _ TRACK _ RANGE _ ACCEL)<0.05m/s²；

Original data information corresponding to the target: (CAN _ TX _ TRACK _ GROUPING _ CHANGED) ═ 0;

target update status information: (CAN _ TX _ TRACK _ STATUS);

the parameters are explained as follows:

CAN _ TX _ TRACK _ RANGE is the relative distance between the target and the radar;

CAN _ TX _ TRACK _ RANGE _ RATE is the relative speed of the target and the radar;

CAN _ TX _ TRACK _ RANGE _ ACCEL is the relative acceleration of the target and the radar;

CAN _ TX _ TRACK _ ANGLE is the horizontal azimuth ANGLE of the target and the radar;

whether the number of the millimeter wave original point clouds corresponding to the target object is CHANGED or not CAN _ TX _ TRACK _ GROUPING _ CHANGED;

CAN _ TX _ TRACK _ LAT _ RATE is the lateral velocity of the target;

CAN _ TX _ TRACK _ STATUS is a STATUS update of the target;

then, as the virtual scenic spot signals are between the continuous metal guardrails and the target vehicle, the virtual scenic spots in the virtual and real point pairs are identified and filtered by comparing the transverse relative position relations of the metal guardrails, the virtual scenic spots and the actual target object feedback points as follows:

dividing a forward direction into two space areas by taking a vehicle driving direction as a middle boundary, setting millimeter wave radar points on two sides of the vehicle to be not less than 12, and when the number of feedback points on one side is less than 12, generating a normally distributed Monte-Carlo sampling result as a background point by taking a relative longitudinal distance (CAN _ TX _ TRACK _ RANCos (CAN _ TX _ TRACK _ ANGLE)) and a relative transverse distance (CAN _ TX _ TRACK _ RANsin (CAN _ TX _ TRACK _ ANGLE)) as random variables, and complementing data on one side with less than 12 points to 12 feedback points;

taking the absolute value of the transverse relative distance as a random variable, directly calculating an expected value E and a variance V of the random variable in two space regions, judging that the region with the metal guardrail is a region on one side in the case of a region with a small expected value and a small variance, and correspondingly, if the transverse distances of all feedback points in the region are negative values, then, the virtual scene point in the virtual and real point pair is a point with a smaller transverse distance, and directly filtering the virtual scene point; if the transverse distances of all the feedback points of the area are positive values, the virtual scene point in the virtual and real point pair is a point with a larger transverse distance, and the virtual scene point is directly filtered.

And step 3: and (3) identifying and filtering a longitudinal drift point of the millimeter wave radar:

for the data information of each point of the millimeter wave radar of the current frame, matching with each millimeter wave radar point of the previous frame one by one, if the ID information of the previous frame and the next frame are consistent, and the CAN _ TX _ TRACK _ STATUS!of the current target feedback point of the current frame! Calculating the change value of the vertical pixel of the current frame minus the previous frame as 1:

(PIXEL_{Current_Frame_X}-PIXEL_{Previous_Frame_X)}

for the data information of each point of the millimeter wave radar of the current frame, matching with each millimeter wave radar point of the previous frame one by one, if the ID information of the previous frame and the next frame are consistent, and the CAN _ TX _ TRACK _ STATUS!of the current target feedback point of the current frame! 1, calculating the change value of the horizontal pixel of the current frame minus the previous frame:

(PIXEL_{Current_Frame_Y}-PIXEL_{Previous_Frame_Y})

for the current feedback point information of the current frame, if the following relation is satisfied, judging that the current feedback point information of the current frame is a longitudinal drift noise point to filter;

(PIXEL_{Current_Frame_X}-PIXEL_{Previous_Frame_X})>40

(PIXEL_{Current_Frame_Y}-PIXEL_{Previous_Frame_Y}) The values within 5 consecutive frames are all greater than zero or all less than zero.

Further, in step 2: and when judging whether the current frame has the virtual scenic spot, the Mth target object in the outer layer circulation does not participate in any calculation in the next circulation process.

Further, in step 2: Monte-Carlo sampling based on normal distributions is as follows:

firstly, in a [0,1] one-dimensional unit interval, two groups of random numbers eta and xi are respectively generated to meet uniform distribution;

second, the generated random numbers in each group are used to calculate the intermediate random number

H＝(2η-1)²+(2ξ-1)²；

Third, if H is more than 1, returning to the first step;

fourthly, if H is less than or equal to 1, calculating the generated intermediate random number:

λ＝[log((2η-1)²+(2ξ-1)²)/(2η-1)²+(2ξ-1)²]^1/2；

and fifthly, taking X ═ λ η as a Monte-Carlo sample value of the relative longitudinal distance, and taking Y ═ λ xi as a Monte-Carlo sample value of the relative transverse distance.

Compared with the prior art, the classification, identification and filtering method for the noise data of the vehicle-mounted millimeter wave radar has the following advantages:

the classification, identification and filtering method for the noise data of the vehicle-mounted millimeter wave radar has the characteristics of accurate radar noise data identification, high filtering and calculating speed and wide application scene.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate an embodiment of the invention and, together with the description, serve to explain the invention and not to limit the invention.

In the drawings:

fig. 1 is a schematic diagram of a classification, identification and filtering method for vehicle-mounted millimeter wave radar noise data according to an embodiment of the present invention.

Detailed Description

It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.

In the description of the present invention, it is to be understood that the terms "center", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on those shown in the drawings, and are used only for convenience in describing the present invention and for simplicity in description, and do not indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and thus, are not to be construed as limiting the present invention. Furthermore, the terms "first", "second", etc. are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first," "second," etc. may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless otherwise specified.

In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art through specific situations.

The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.

As shown in fig. 1, a method for classifying, identifying and filtering noise data of a vehicle-mounted millimeter wave radar includes:

step 1: identification and filtering of invalid noise points of the millimeter wave radar:

parameters in the millimeter wave radar data are: synthesizing a relative distance, a longitudinal relative speed, a longitudinal relative acceleration, a horizontal azimuth angle and a transverse relative speed, limiting the relative distance, the longitudinal relative speed, the longitudinal relative acceleration, the horizontal azimuth angle and the transverse relative speed in a value range, and identifying and filtering invalid noise points;

step 2: recognizing and filtering the virtual noise point of the millimeter wave radar:

firstly, judging whether a virtual scenic spot exists in a current frame, comparing the matching degree of millimeter wave radar signal information pairwise, and if the target feedback points of two millimeter wave radars meet the following five conditions, judging that the two target feedback points are virtual and real point pairs:

longitudinal distance information: (CAN _ TX _ TRACK _ RANGE _ sin (CAN _ TX _ TRACK _ ANGLE)) <0.1 m;

longitudinal velocity information: (CAN _ TX _ TRACK _ RANGE _ RATE) <0.01 m/s;

longitudinal acceleration information: (CAN _ TX _ TRACK _ RANGE _ ACCEL)<0.05m/s²；

Original data information corresponding to the target: (CAN _ TX _ TRACK _ GROUPING _ CHANGED) ═ 0;

target update status information: (CAN _ TX _ TRACK _ STATUS);

the parameters are explained as follows:

CAN _ TX _ TRACK _ RANGE is the relative distance between the target and the radar;

CAN _ TX _ TRACK _ RANGE _ RATE is the relative speed of the target and the radar;

CAN _ TX _ TRACK _ RANGE _ ACCEL is the relative acceleration of the target and the radar;

CAN _ TX _ TRACK _ ANGLE is the horizontal azimuth ANGLE of the target and the radar;

whether the number of the millimeter wave original point clouds corresponding to the target object is CHANGED or not CAN _ TX _ TRACK _ GROUPING _ CHANGED;

CAN _ TX _ TRACK _ LAT _ RATE is the lateral velocity of the target;

CAN _ TX _ TRACK _ STATUS is a STATUS update of the target;

then, as the virtual scenic spot signals are between the continuous metal guardrails and the target vehicle, the virtual scenic spots in the virtual and real point pairs are identified and filtered by comparing the transverse relative position relations of the metal guardrails, the virtual scenic spots and the actual target object feedback points as follows:

dividing a forward direction into two space areas by taking a vehicle driving direction as a middle boundary, setting millimeter wave radar points on two sides of the vehicle to be not less than 12, and when the number of feedback points on one side is less than 12, generating a normally distributed Monte-Carlo sampling result as a background point by taking a relative longitudinal distance (CAN _ TX _ TRACK _ RANCos (CAN _ TX _ TRACK _ ANGLE)) and a relative transverse distance (CAN _ TX _ TRACK _ RANsin (CAN _ TX _ TRACK _ ANGLE)) as random variables, and complementing data on one side with less than 12 points to 12 feedback points;

taking the absolute value of the transverse relative distance as a random variable, directly calculating an expected value E and a variance V of the random variable in two space regions, judging that the region with the metal guardrail is a region on one side in the case of a region with a small expected value and a small variance, and correspondingly, if the transverse distances of all feedback points in the region are negative values, then, the virtual scene point in the virtual and real point pair is a point with a smaller transverse distance, and directly filtering the virtual scene point; if the transverse distances of all the feedback points of the area are positive values, the virtual scene point in the virtual and real point pair is a point with a larger transverse distance, and the virtual scene point is directly filtered.

And step 3: and (3) identifying and filtering a longitudinal drift point of the millimeter wave radar:

for the data information of each point of the millimeter wave radar of the current frame, matching with each millimeter wave radar point of the previous frame one by one, if the ID information of the previous frame and the next frame are consistent, and the CAN _ TX _ TRACK _ STATUS!of the current target feedback point of the current frame! Calculating the change value of the vertical pixel of the current frame minus the previous frame as 1:

(PIXEL_{Current_Frame_X}-PIXEL_{Previous_Frame_X})

for the data information of each point of the millimeter wave radar of the current frame, matching with each millimeter wave radar point of the previous frame one by one, if the ID information of the previous frame and the next frame are consistent, and the CAN _ TX _ TRACK _ STATUS!of the current target feedback point of the current frame! 1, calculating the change value of the horizontal pixel of the current frame minus the previous frame:

(PIXEL_{Current_Frame_Y}-PIXEL_{Previous_Frame_Y})

for the current feedback point information of the current frame, if the following relation is satisfied, judging that the current feedback point information of the current frame is a longitudinal drift noise point to filter;

(PIXEL_{Current_Frame_X}-PIXEL_{Previous_Frame_X})>40

(PIXEL_{Current_Frame_Y}-PIXEL_{Previous_Frame_Y}) The values within 5 consecutive frames are all greater than zero or all less than zero.

Further, in step 2: and when judging whether the current frame has the virtual scenic spot, the Mth target object in the outer layer circulation does not participate in any calculation in the next circulation process.

In step 2: Monte-Carlo sampling based on normal distributions is as follows:

firstly, in a [0,1] one-dimensional unit interval, two groups of random numbers eta and xi are respectively generated to meet uniform distribution;

second, the generated random numbers in each group are used to calculate the intermediate random number

H＝(2η-1)²+(2ξ-1)²；

Third, if H is more than 1, returning to the first step;

fourthly, if H is less than or equal to 1, calculating the generated intermediate random number:

λ＝[log((2η-1)²+(2ξ-1)²)/(2η-1)²+(2ξ-1)²]^1/2；

and fifthly, taking X ═ λ η as a Monte-Carlo sample value of the relative longitudinal distance, and taking Y ═ λ xi as a Monte-Carlo sample value of the relative transverse distance.

The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims (3)

1. A classification, identification and filtering method for noise data of a vehicle-mounted millimeter wave radar is characterized by comprising the following steps: the method comprises the following steps:

step 1: identification and filtering of invalid noise points of the millimeter wave radar:

parameters in the millimeter wave radar data are: the synthetic relative distance, the longitudinal relative speed, the longitudinal relative acceleration, the horizontal azimuth angle and the transverse relative speed are limited in the following value ranges, and invalid noise points are identified and filtered;

step 2: recognizing and filtering the virtual noise point of the millimeter wave radar:

firstly, judging whether a virtual scenic spot exists in a current frame, comparing the matching degree of millimeter wave radar signal information pairwise, and if the target feedback points of two millimeter wave radars meet the following five conditions, judging that the two target feedback points are virtual and real point pairs:

longitudinal distance information: (CAN _ TX _ TRACK _ RANGE _ sin (CAN _ TX _ TRACK _ ANGLE)) <0.1 m;

longitudinal velocity information: (CAN _ TX _ TRACK _ RANGE _ RATE) <0.01 m/s;

longitudinal acceleration information: (CAN _ TX _ TRACK _ RANGE _ ACCEL)<0.05m/s²；

Original data information corresponding to the target: (CAN _ TX _ TRACK _ GROUPING _ CHANGED) ═ 0;

target update status information: (CAN _ TX _ TRACK _ STATUS);

the parameters are explained as follows:

CAN _ TX _ TRACK _ RANGE is the relative distance between the target and the radar;

CAN _ TX _ TRACK _ RANGE _ RATE is the relative speed of the target and the radar;

CAN _ TX _ TRACK _ RANGE _ ACCEL is the relative acceleration of the target and the radar;

CAN _ TX _ TRACK _ ANGLE is the horizontal azimuth ANGLE of the target and the radar;

whether the number of the millimeter wave original point clouds corresponding to the target object is CHANGED or not CAN _ TX _ TRACK _ GROUPING _ CHANGED;

CAN _ TX _ TRACK _ LAT _ RATE is the lateral velocity of the target;

CAN _ TX _ TRACK _ STATUS is a STATUS update of the target;

then, as the virtual scenic spot signals are between the continuous metal guardrails and the target vehicle, the virtual scenic spots in the virtual and real point pairs are identified and filtered by comparing the transverse relative position relations of the metal guardrails, the virtual scenic spots and the actual target object feedback points as follows:

dividing a forward direction into two space areas by taking a vehicle driving direction as a middle boundary, setting millimeter wave radar points on two sides of the vehicle to be not less than 12, and when the number of feedback points on one side is less than 12, generating a normally distributed Monte-Carlo sampling result as a background point by taking a relative longitudinal distance (CAN _ TX _ TRACK _ RANCos (CAN _ TX _ TRACK _ ANGLE)) and a relative transverse distance (CAN _ TX _ TRACK _ RANsin (CAN _ TX _ TRACK _ ANGLE)) as random variables, and complementing data on one side with less than 12 points to 12 feedback points;

taking the absolute value of the transverse relative distance as a random variable, directly calculating an expected value E and a variance V of the random variable in two space regions, judging that the region with the metal guardrail is a region on one side in the case of a region with a small expected value and a small variance, and correspondingly, if the transverse distances of all feedback points in the region are negative values, then, the virtual scene point in the virtual and real point pair is a point with a smaller transverse distance, and directly filtering the virtual scene point; if the transverse distances of all the feedback points of the area are positive values, the virtual scene point in the virtual and real point pair is a point with a larger transverse distance, and the virtual scene point is directly filtered.

And step 3: and (3) identifying and filtering a longitudinal drift point of the millimeter wave radar:

for the data information of each point of the millimeter wave radar of the current frame, matching with each millimeter wave radar point of the previous frame one by one, if the ID information of the previous frame and the next frame are consistent, and the CAN _ TX _ TRACK _ STATUS!of the current target feedback point of the current frame! Calculating the change value of the vertical pixel of the current frame minus the previous frame as 1:

(PIXEL_{Current_Frame_X}-PIXEL_{Previous_Frame_x})

for the data information of each point of the millimeter wave radar of the current frame, the data information is matched with each millimeter wave radar point of the previous frame one by one, if the former and the latter frames of work D information are consistent, and the CAN _ TX _ TRACK _ STATUS!of the current target feedback point of the current frame is! 1, calculating the change value of the horizontal pixel of the current frame minus the previous frame:

(PIXEL_{Current_Frame_Y}-PIXEL_{Previous_Frame_Y})

for the current feedback point information of the current frame, if the following relation is satisfied, judging that the current feedback point information of the current frame is a longitudinal drift noise point to filter;

(PIXEL_{Current_Frame_X}-PIXEL_{Previous_Frame_x})＞40

(PIXEL_{Current_Frame_Y}-PIXEL_{Previous_FrameY}) The values within 5 consecutive frames are all greater than zero or all less than zero.

2. The classification, identification and filtering method for noise data of the vehicle-mounted millimeter wave radar as claimed in claim 1, wherein: in step 2: and when judging whether the current frame has the virtual scenic spot, the Mth target object in the outer layer circulation does not participate in any calculation in the next circulation process.

3. The classification, identification and filtering method for noise data of the vehicle-mounted millimeter wave radar as claimed in claim 1, wherein: in step 2: Monte-Carlo sampling based on normal distributions is as follows:

firstly, in a [0,1] one-dimensional unit interval, two groups of random numbers eta and xi are respectively generated to meet uniform distribution;

second, the generated random numbers in each group are used to calculate the intermediate random number

H＝(2η-1)²+(2ξ-1)²；

Step three, if H is more than 1, returning to the step one;

fourthly, if H is less than or equal to 1, calculating the generated intermediate random number:

λ＝[log((2η-1)²+(2ξ-1)²)/(2η-1)²+(2ξ-1)²]^1/2；

and fifthly, taking X ═ λ η as a Monte-Carlo sample value of the relative longitudinal distance, and taking Y ═ λ xi as a Monte-Carlo sample value of the relative transverse distance.

Images