CN110865365A - Parking lot noise elimination method based on millimeter wave radar - Google Patents
Parking lot noise elimination method based on millimeter wave radar Download PDFInfo
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- CN110865365A CN110865365A CN201911181963.5A CN201911181963A CN110865365A CN 110865365 A CN110865365 A CN 110865365A CN 201911181963 A CN201911181963 A CN 201911181963A CN 110865365 A CN110865365 A CN 110865365A
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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
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/66—Radar-tracking systems; Analogous systems
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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
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
- G01S13/50—Systems of measurement based on relative movement of target
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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
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
- G01S13/50—Systems of measurement based on relative movement of target
- G01S13/52—Discriminating between fixed and moving objects or between objects moving at different speeds
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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
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/87—Combinations of radar systems, e.g. primary radar and secondary radar
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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
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
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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
- G01S7/411—Identification of targets based on measurements of radar reflectivity
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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
- G01S7/418—Theoretical aspects
Abstract
The invention discloses a parking lot noise elimination method based on a millimeter wave radar, which comprises the following steps: the method comprises the steps that firstly, a radar collects data information of electromagnetic waves reflected by all objects in a parking lot in real time, wherein the data information of the electromagnetic waves comprises positions, speeds, angles, Doppler coefficients and echo intensities; secondly, dividing the target into a static object and a moving object according to the target Doppler coefficient, determining the echo intensity range of the position of the target, and judging which current point cloud data are noise information according to the echo intensity, the region of interest (ROI), the point cloud area and the like; and thirdly, eliminating noise of a static object and noise of a moving object. The data information is detected based on the millimeter wave radar, so that the implementation cost of the parking lot can be obviously reduced; and secondly, the information of the automobile and the non-automobile can be accurately distinguished, and the noises of other static objects or moving objects except the target automobile can be accurately eliminated, so that the interference of the noises to the moving target is avoided.
Description
Technical Field
The invention relates to a noise elimination method for a parking lot, in particular to a noise elimination method for the parking lot based on a millimeter wave radar except for automobiles.
Background
The sensors currently used for detecting and tracking vehicles in parking lots include ultrasonic, passive infrared, active infrared (lidar, TOF) and optical camera sensors, but these sensors are susceptible to external environments (such as light, temperature, etc.) and cause false alarms. The millimeter wave radar has all-weather characteristics, is more excellent than other sensors in the aspect of environmental robustness, and can meet the requirements of underground parking lot vehicle detection on the aspects of accuracy, stability and the like, so that the millimeter wave radar is increasingly applied to the fields of security monitoring, intelligent home, intelligent old age maintenance, automatic door control and the like. Especially in the aspect of protecting personal private life and the like, the millimeter wave radar has irreplaceable natural advantages.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the problems in the prior art, the invention aims to provide a parking lot noise elimination method based on a millimeter wave radar, which can accurately distinguish an automobile from a non-automobile while realizing vehicle detection and tracking, thereby accurately eliminating the interference of other noises in a parking lot.
The technical scheme is as follows: a parking lot noise elimination method based on millimeter wave radar comprises the following steps:
the method comprises the steps that firstly, a radar collects the reflected electromagnetic wave data information of all objects in a parking lot in real time, wherein the electromagnetic wave data information comprises position, speed, angle, Doppler coefficient and echo intensity;
dividing the object into a static object and a moving object according to the Doppler coefficient, determining the echo intensity range of the position of the target vehicle, and judging the noise type of the moving object according to the echo intensity and the position;
and step three, eliminating stationary object noise and moving object noise, wherein the moving object noise comprises moving object noise except the target vehicle and false detection points generated by the moving object.
Further, the static object noise is eliminated by adopting a zero Doppler channel target.
Further, dividing the false detection points into a nearby false detection point and a distant false detection point according to the positions of the false detection points;
(1) for nearby false detection points: when the false detection point is within a range of 1-1.5 meters around the target and the echo intensity of the false detection point is within the echo intensity range of the position of the target, treating the false detection point as the target;
(2) for distant false detection points: setting an echo intensity threshold, and filtering the false detection point when the echo intensity is smaller than the echo intensity threshold; according to the coordinate positions of the parking lot lane and the parking space region, an interested region ROI is marked out, when the coordinate of the false detection point is outside the ROI, the false detection point is directly filtered out, and when the coordinate of the false detection point is in the ROI, the false detection point is removed through clustering calculation density.
Further, when the false detection point coincides with the detection point of other dynamic targets, the false detection point is treated as the detection point of other dynamic targets.
Further, the noise of the moving object comprises the noise of a non-automobile displacement object outside the target vehicle, the echo intensity of the object is compared with the echo intensity range of the target at the position, whether the target is an automobile or not is determined, and non-automobile information is filtered.
Further, clustering the target point cloud data according to the filtered noise information to obtain different target clusters, calculating the area of each cluster, setting an area threshold, and filtering out points with the area smaller than the area threshold.
Furthermore, the noise of the moving object comprises other automobile information except the target automobile, the target point cloud data is clustered to obtain different target clusters, tracking filtering processing is carried out on the different clusters, the dynamic target automobile and other automobiles are distinguished, and other automobile information is filtered. Preferably, the extended kalman filtering algorithm is adopted to perform tracking filtering processing on different clusters.
Further, the moving object noise comprises micro animal body noise, a Doppler coefficient threshold value is set, when the Doppler coefficient of the object is smaller than the Doppler coefficient threshold value, the object is determined to be a micro animal body, and micro animal body information is filtered.
Further, when the target is in a plurality of radar cross areas, the radar data carries out coordinate conversion by using a coordinate system of a parking lot plane graph as a standard coordinate system, and each radar data is fused into the same coordinate system of the same plane graph, so that a plurality of groups of vehicle point cloud data are arranged in the coordinate system; directly filtering data outside the cross region; and clustering the multiple groups of point cloud data, obtaining an area with the clustering center point as the center according to the clustering center point and the size of the actual vehicle, wherein the area is the position of the actual vehicle, and filtering data outside the area.
Compared with the prior art, the invention has the following remarkable advantages: 1. the millimeter wave radar has the advantages of all-weather and long-distance detection, and meanwhile, the circuit is simple, the cost is low, batch arrangement in a parking lot is easy to realize, and the implementation cost of the parking lot is obviously reduced. 2. The millimeter wave radar can accurately detect parameter information of an object, such as distance, speed, angle, echo intensity, Doppler and the like, and the accuracy reaches about 5cm, so that an automobile and a non-automobile can be accurately distinguished. 3. The noise generated by other static objects or moving objects except the dynamic target automobile can be accurately eliminated, and the interference of the noise on the moving target is avoided.
Drawings
FIG. 1 is a plan view of an underground parking garage;
FIG. 2 is a schematic diagram of data point distribution of vehicles and non-vehicles (e.g. people) in a current frame;
FIG. 3 is a schematic diagram of the intersection region of two radars.
Detailed Description
The invention adopts the millimeter wave radar to remove noise information of the underground parking lot except for a moving target automobile, electromagnetic waves sent by the millimeter wave radar are reflected with different intensities after encountering different objects, the automobile can be actually understood as a large metal object, the reflection of the metal object is strong, and the reflection intensity of the nonmetal object is small. The reflected echo is received by the radar, so that the distance, speed, angle, echo intensity, Doppler and other parameter information of an object can be obtained according to the transmitted and received electromagnetic wave data information, and meanwhile, the detection precision of the millimeter wave radar reaches about 5cm, so that an automobile and a non-automobile can be accurately distinguished through the millimeter wave radar. The technical solution of the present invention is further described in detail below with reference to the accompanying drawings and the embodiments.
Step one, radar data acquisition:
as shown in figure 1, the millimeter wave radar is preferably arranged on a wall body at one end of a main road, the radar collects data information of electromagnetic waves reflected by moving target vehicles and other all dynamic and static objects in a parking lot in real time, the data information comprises parameter information such as position, speed, Doppler and echo intensity, the detectable range of the radar is 80 meters in the longitudinal direction, the horizontal angle is 120 degrees, and all vehicles entering the parking lot can be monitored by arranging 7-8 millimeter wave radars in a parking lot range of 3000 square meters.
Step two, noise elimination:
the method is used for eliminating other noises except for a dynamic target automobile and comprises a static object noise elimination algorithm and a dynamic object noise elimination algorithm. Static noise includes stationary objects such as walls, pipes, fire hydrants, etc.; the dynamic object noise comprises moving people, animals, electric vehicles, motorcycles, bicycles, other automobiles except target automobiles, rotating cameras, slightly flickering lamps and the like, and false points reflected by moving objects. Dividing the target into a static object and a moving object according to the target Doppler coefficient, determining the echo intensity range of the position of the target, and judging whether the current point cloud data is noise information according to the echo intensity, the region of interest (ROI), the point cloud area and the like, wherein the specific contents are as follows:
1. the static object elimination algorithm mainly aims at static objects (such as objects with Doppler coefficient of 0, such as walls, pipelines, fire hydrants and the like) in the parking lot environment, and preferably can realize the filtering of the static objects by eliminating a zero Doppler channel target, so as to avoid the interference of the static objects on a moving target.
2. The dynamic object elimination algorithm is mainly used for the interference of the noise of a moving object (namely the Doppler coefficient is not 0) in the underground parking lot environment. These dynamic objects mainly include three major classes: false points brought by moving objects; other vehicles, people, electric vehicles, and other objects having displacement other than the target vehicle; the rotating camera, the tiny flashing lamp and the like have objects with fine motion. The noise elimination method for the three types of dynamic objects is specifically as follows:
2.1 elimination of false points: the method mainly eliminates false detection points caused by moving objects in the underground parking lot scene, and avoids the problem of false alarm caused by the fact that the false detection points are mistaken for targets by the millimeter wave radar sensor. Due to the complex environment of the underground parking lot, a plurality of pipelines, fire hydrants and the like are arranged, and other vehicles are arranged, and the metal generates a large amount of refraction and reflection interference on electromagnetic waves transmitted and received by the radar, so that echoes of the same target received by the radar are generated in a plurality of directions, and the targets are judged by mistake. For a particular target, its ghost points may be very close to the target, or may be at intervals.
A. When the false points are within the range of 1-1.5 meters around the target automobile and the echo intensity of the false points is within the echo intensity range of the position of the target, the false points are not easy to distinguish through the echo intensity, and the false points can be directly taken as the target.
B. For false points with certain intervals, the false points are filtered out by the following two methods.
Firstly, the size of the parked vehicles in the underground parking place is limited, two vehicles in the size are selected, 3-5 meters are used as a distance on a main road which can be detected by a radar to measure the echo intensity range of the vehicles in the distance, and the echo intensity range is used as an echo intensity threshold value in the distance.
B.1. If the echo intensity of the far false point is small (less than the echo intensity threshold), the far false point can be directly filtered out.
B.2. The car only appears in the lane and parking space area in the parking lot, the area (ROI) which is interested by the car can be divided according to the coordinate positions of the areas, and if the coordinates of the false points are outside the ROI, the false points are directly filtered; if the false points are within the ROI, they are generally sparse and can be removed by clustering the points to calculate the density.
In another case, if the false points coincide with other targets, they can be defaulted as points of other targets when they cannot be distinguished according to the echo intensity.
2.2 filtering out displaced objects: the target which we want to measure is the automobile finally, so other moving objects other than the automobile are filtered, the target automobile is distinguished from other automobiles, misjudgment of the millimeter wave radar sensor is prevented, and tracking output is carried out on other targets. These displaced objects mainly include other automobiles than the target automobile, humans, animals, electric vehicles, bicycles, motorcycles, and the like. The filtration is carried out by the following three steps: ,
A. the car is a large metal body, which reflects more strongly than other dynamic objects, and therefore has a much higher echo intensity than other objects. The echo intensities of different types of objects at different distances may also vary, and if the echo intensity is too low, the signal may be considered as noise. The specific method comprises the following steps:
the radar minimum measurable echo intensity formula is as follows:
where λ is the operating wavelength, PtTo transmit power, PrFor received power, G is antenna gain, θ and φ are radar beam widths, τ is pulse width, c is speed of light, R is distance from target to radar,
is a constant. For a certain radar, the working wavelength, the transmitting power, the minimum receivable power, the antenna gain and the beam width are all fixed values, and the minimum measurable echo strength is related to the pulse width and the distance from a target to the radar. The echo intensities of the same target at different distances are different, and the echo intensities of different targets at the same position are also different. The echo intensity of the automobile at different distances has a certain range, which can be measured by experiments and combined with the concreteAnd (4) determining the situation. By comparing the echo intensity of the target with the echo intensity range of the position, whether the target is an automobile or not can be known, and non-automobile information is filtered out.
B. After the partial information is filtered by the method, some noise information still remains. The information is filtered by calculating the area of the target point cloud, and the small objects with the area smaller than a certain threshold value, such as people and electric vehicles, are equal in size. The specific method comprises the following steps:
the size of the parking car in the underground parking place is generally 3.6-5.5 m, 1.5-2.2 m in width and 1.3-2.2 m in height, the original point cloud data are effectively clustered after being reflected by a radar to obtain different target clusters, each cluster has a certain area, S is Xsize or Ysize, and S is the area of the cluster and the unit: m is2(ii) a Xsize, Ysize are the length and width of the cluster, unit: and m is selected. As shown in FIG. 2, the reflection area of the car is 1.95-12.1 m2While the area of other dynamic objects is generally small, the threshold value of S is set to 1.9m2Points less than the threshold are filtered out.
C. For other automobile information except for the target automobile, the two methods cannot be used for filtering, the remaining point cloud data can be clustered to obtain different target clusters, and then different automobiles are distinguished by means of tracking filtering processing on different clusters. In this embodiment, it is preferable to perform tracking filtering processing on different clusters by using an extended kalman filtering algorithm.
2.3 filtering the micro-animal bodies: the micro-animal body has a certain speed, but the speed is lower, so the Doppler coefficient is smaller, and a Doppler coefficient threshold value can be set, wherein the threshold value range is as follows: -2.5-2.5, directly filtering out points where the doppler coefficient is less than the threshold.
In addition, a real parking lot may be equipped with multiple radars with cross-over areas between the radars. The following describes the steps of removing noise generated by a target vehicle in a crossing area when the target vehicle is in the crossing area of two radars by taking two radars as an example:
fig. 3 is a schematic diagram of two radar cross-region detection vehicles. In the figure, P1 and P2 are two radars, P1L1M2L2 is the detection range of the radar P1, P2L1M1L2 is the detection range of the radar P2, S is an intersection region of the two radars, and C is one automobile in the intersection region. The specific method comprises the following steps:
the two radar data are subjected to coordinate conversion by using a coordinate system of a parking lot plane graph as a standard coordinate system, and are fused into the same coordinate system of the same plane graph, so that two groups of vehicle point cloud data exist in the coordinate system. Directly filtering data outside the cross region; and clustering the two groups of point cloud data, and obtaining an area S (Xsize Ysize) with the clustering center point as the center according to the clustering center point and the size of the actual vehicle, wherein the area is the position of the actual vehicle, and data outside the area is directly filtered.
Claims (10)
1. A parking lot noise elimination method based on millimeter wave radar is characterized by comprising the following steps:
the method comprises the steps that firstly, a radar collects the reflected electromagnetic wave data information of all objects in a parking lot in real time, wherein the electromagnetic wave data information comprises position, speed, angle, Doppler coefficient and echo intensity;
dividing the object into a static object and a moving object according to the Doppler coefficient, determining the echo intensity range of the position of the target vehicle, and judging the noise type of the moving object according to the echo intensity and the position;
and step three, eliminating stationary object noise and moving object noise, wherein the moving object noise comprises moving object noise except the target vehicle and false detection points generated by the moving object.
2. The millimeter wave radar-based parking lot noise elimination method according to claim 1, wherein: and eliminating the noise of the static object by adopting a zero Doppler channel target.
3. The millimeter wave radar-based parking lot noise elimination method according to claim 1, wherein: dividing the false detection points into near false detection points and far false detection points according to the positions of the false detection points;
(1) for nearby false detection points: when the false detection point is within 1-1.5 meters from the target and the echo intensity is within the echo intensity range of the position of the target, the false detection point is taken as the target for processing;
(2) for distant false detection points: setting an echo intensity threshold, and filtering the false detection point when the echo intensity is smaller than the echo intensity threshold; according to the coordinate positions of the parking lot lane and the parking space region, an interested region ROI is marked out, when the coordinate of the false detection point is outside the ROI, the false detection point is directly filtered out, and when the coordinate of the false detection point is in the ROI, the false detection point is removed through clustering calculation density.
4. The millimeter wave radar-based parking lot noise elimination method according to claim 3, wherein: when the false detection point coincides with the detection point of other dynamic targets, the false detection point is treated as the detection point of other dynamic targets.
5. The millimeter wave radar-based parking lot noise elimination method according to claim 1, wherein: and the moving object noise comprises non-automobile displacement object noise outside the target vehicle, the echo intensity of the object is compared with the echo intensity range of the target at the position, whether the target is an automobile or not is determined, and non-automobile information is filtered.
6. The millimeter wave radar-based parking lot noise elimination method according to claim 5, wherein: and clustering the target point cloud data according to the filtered noise information to obtain different target clusters, calculating the area of each cluster, setting an area threshold value, and filtering out points with the area smaller than the area threshold value.
7. The millimeter wave radar-based parking lot noise elimination method according to claim 5 or 6, wherein: and the noise of the moving object comprises other automobile information except the target automobile, the target point cloud data is clustered to obtain different target clusters, the different clusters are subjected to tracking filtering processing, the dynamic target automobile and other automobiles are distinguished, and other automobile information is filtered.
8. The millimeter wave radar-based parking lot noise elimination method according to claim 7, wherein: and performing tracking filtering processing on different clusters by adopting an extended Kalman filtering algorithm.
9. The millimeter wave radar-based parking lot noise elimination method according to claim 1, wherein: and the moving object noise comprises micro-animal body noise, a Doppler coefficient threshold value is set, when the Doppler coefficient of the object is smaller than the Doppler coefficient threshold value, the object is determined to be a micro-animal body, and micro-animal body information is filtered.
10. The millimeter wave radar-based parking lot noise elimination method according to claim 1, wherein: when the target is in a plurality of radar cross areas, the radar data carries out coordinate conversion by using a coordinate system of a parking lot plane graph as a standard coordinate system, and each radar data is fused into the same coordinate system of the same plane graph, so that a plurality of groups of vehicle point cloud data are arranged in the coordinate system; directly filtering data outside the cross region; and clustering the multiple groups of point cloud data, obtaining an area with the clustering center point as the center according to the clustering center point and the size of the actual vehicle, wherein the area is the position of the actual vehicle, and filtering data outside the area.
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