CN113534157A - Multi-target radar detection method and system based on stack filtering - Google Patents

Multi-target radar detection method and system based on stack filtering Download PDF

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CN113534157A
CN113534157A CN202110632030.4A CN202110632030A CN113534157A CN 113534157 A CN113534157 A CN 113534157A CN 202110632030 A CN202110632030 A CN 202110632030A CN 113534157 A CN113534157 A CN 113534157A
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target
unmanned aerial
aerial vehicle
linked list
obstacle
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刘新阳
杨苡
刘璐
刘玉伟
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Beijing Tt Aviation Technology Co ltd
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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
    • G01S13/93—Radar or analogous systems specially adapted for specific applications for anti-collision purposes
    • G01S13/933—Radar or analogous systems specially adapted for specific applications for anti-collision purposes of aircraft or spacecraft
    • 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
    • G01S13/86—Combinations of radar systems with non-radar systems, e.g. sonar, direction finder
    • 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
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/42—Determining position
    • G01S19/45—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement
    • G01S19/46—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement the supplementary measurement being of a radio-wave signal type

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  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Physics & Mathematics (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • General Physics & Mathematics (AREA)
  • Aviation & Aerospace Engineering (AREA)
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Abstract

The invention discloses a multi-target radar detection method and a system based on stack filtering, wherein an unmanned aerial vehicle 1 detects obstacles through a millimeter wave radar; confirming the authenticity of the barrier and obtaining the GPS coordinate of the barrier; uploading the GPS coordinates of the obstacles to a cloud server for storage; when flying to the similar position, the unmanned aerial vehicle 2 can obtain the GPS coordinate of the obstacle through the server, and early warning is carried out in advance.

Description

Multi-target radar detection method and system based on stack filtering
Technical Field
The invention relates to the field of radar detection, in particular to a multi-target radar detection method and system based on stack filtering.
Background
Millimeter wave radar scanning is the main means of unmanned aerial vehicle flight in-process barrier detection. The millimeter wave radar realizes object detection by transmitting an electromagnetic wave and receiving an object reflected wave. However, since the millimeter-wave radar emits a cone-shaped beam, the resolution of the millimeter-wave radar scanning an obstacle is low.
In the prior art, detection parameters of the millimeter wave radar are optimized aiming at different scenes, and object detection under a specific scene is realized. However, the use scene of the unmanned aerial vehicle is complex, and the scheme for optimizing the parameters cannot well meet the obstacle detection requirement of the unmanned aerial vehicle, so that the unmanned aerial vehicle has abnormal evasive actions in the flight process, and even the situation that the unmanned aerial vehicle cannot avoid the obstacle caused by target loss.
A multi-target radar detection method and a multi-target radar detection system based on stack filtering are provided, and secondary analysis is performed on detection results of millimeter wave radars so as to achieve the purpose of correctly identifying obstacles.
Disclosure of Invention
The invention aims to solve the problems that in the prior art, due to the fact that the resolution of a millimeter wave radar is not high, even if the millimeter wave radar is debugged for a specific scene, false alarm conditions still exist, and the millimeter wave radar is too dependent on the radar.
The technical scheme adopted by the invention for solving the technical problems is as follows: a multi-target radar detection method based on stack filtering comprises the steps that an unmanned aerial vehicle 1 detects obstacles through a millimeter wave radar; confirming the authenticity of the barrier and obtaining the GPS coordinate of the barrier; uploading the GPS coordinates of the obstacles to a cloud server for storage; and when the unmanned aerial vehicle 2 flies to a close position, the GPS coordinates of the obstacles can be obtained through the cloud server, and early warning is carried out in advance.
Optionally, the step of confirming the authenticity of the obstacle and obtaining the GPS coordinates of the obstacle comprises: after the radar detects the target, converting a 84 coordinate system of the detected target; the conversion formula is:
X＝(N+H)*cosB*cosL
Y＝(N+H)*cosB*sinL
Z＝[N*(1-e2)+H]*sinB
Figure BDA0003104074290000021
or
Figure BDA0003104074290000022
Figure BDA0003104074290000023
Figure BDA0003104074290000024
In the formula, N is the curvature radius of the ellipsoidal unitary-ground ring, e is the first eccentricity of an ellipsoid, a and b are the long and short radii of the ellipsoid, f is the oblateness of the ellipsoid, W is the first auxiliary coefficient, and X, Y and Z are converted coordinates.
Optionally, the step of confirming the authenticity of the obstacle and obtaining the GPS coordinates of the obstacle further comprises: storing the transformed coordinates of the target point into a linked list; sorting the data in the linked list to obtain a new target linked list; repeatedly executing the above actions, a linked list of all targets about the confidence level can be obtained, and it is determined whether the confidence level is higher than the threshold value? And when the confidence coefficient is higher than a threshold value, uploading the GPS coordinates of the real target to a cloud server, and when the confidence coefficient is lower than the threshold value, discarding the false target.
Optionally, the step of sorting the data in the linked list includes, but is not limited to: removing points with the same position or points with extremely close distance in the linked list; increasing the confidence of the detected points; reducing confidence of undetected points; the point with zero confidence is deleted.
Optionally, the drone 1 may also be a plurality of drones.
The invention also provides a multi-target radar detection system based on stack filtering, which comprises millimeter wave radar devices 1 and 2, an unmanned aerial vehicle 1, an unmanned aerial vehicle 2, a data analysis algorithm module and a cloud server, wherein: the unmanned aerial vehicle 1 is coupled with the millimeter wave radar device 1 and the data analysis algorithm module, detects obstacles through the millimeter wave radar device 1, and transmits the related coordinate information of the obstacles to the data analysis algorithm module; the data analysis algorithm module is coupled with the unmanned aerial vehicle 1 and the cloud server and used for receiving the coordinate information of the obstacle, carrying out algorithm analysis, confirming the authenticity of the obstacle and sending the GPS coordinate of the real obstacle to the cloud server for storage; the cloud server is coupled with the data analysis algorithm module and the unmanned aerial vehicle 2 and is used for sending the GPS coordinates of the obstacles to the unmanned aerial vehicle 2 when the unmanned aerial vehicle 2 flies to a close position, and early warning is carried out in advance; and an unmanned aerial vehicle 2 coupled to the cloud server and the millimeter wave radar device 2 for receiving GPS coordinates of a proximate obstacle from the cloud server.
Optionally, the performing algorithm analysis by the data analysis algorithm module includes: after the radar detects the target, converting a 84 coordinate system of the detected target; the conversion formula is:
X＝(N+H)*cosB*cosL
Y＝(N+H)*cosB*sinL
Z＝[N*(1-e2)+H]*sinB
Figure BDA0003104074290000031
or
Figure BDA0003104074290000032
Figure BDA0003104074290000033
Figure BDA0003104074290000034
In the formula, N is the curvature radius of the ellipsoidal unitary-ground ring, e is the first eccentricity of an ellipsoid, a and b are the long and short radii of the ellipsoid, f is the oblateness of the ellipsoid, W is the first auxiliary coefficient, and X, Y and Z are converted coordinates.
Optionally, the data analysis algorithm module confirming the authenticity of the obstacle includes: storing the transformed coordinates of the target point into a linked list; sorting the data in the linked list to obtain a new target linked list; repeatedly executing the above actions, a linked list of all targets about the confidence level can be obtained, and it is determined whether the confidence level is higher than the threshold value? And when the confidence coefficient is higher than a threshold value, uploading the GPS coordinates of the real target to a cloud server, and when the confidence coefficient is lower than the threshold value, discarding the false target.
Optionally, the step of sorting the data in the linked list includes, but is not limited to: removing points with the same position or points with extremely close distance in the linked list; increasing the confidence of the detected points; reducing confidence of undetected points; the point with zero confidence is deleted.
Optionally, the drone 1 may also be a plurality of drones.
The invention has the following beneficial effects:
firstly, processing a target detected by a radar by an unmanned aerial vehicle through stack filtering, and detecting a real target and a false target;
secondly, the dependence of the unmanned aerial vehicle on the radar is reduced, and the detection precision of the obstacle is improved;
thirdly, the unmanned aerial vehicle uploads the obstacle information to the server, and information sharing of the obstacle is achieved.
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FIG. 1 is a flow chart of a method for multi-target radar detection based on stacked filtering according to the present invention;
FIG. 2 is another flow chart of a method for multi-target radar detection based on stacked filtering according to FIG. 1;
fig. 3 is a schematic diagram of a system for multi-target radar detection based on stacked filtering according to the present invention.
Detailed Description
The technical solution of the present invention is further described below with reference to the following embodiments and the accompanying drawings.
As used in the specification and in the claims, certain terms are used to refer to particular components. As one skilled in the art will appreciate, manufacturers may refer to a component by different names. This specification and claims do not intend to distinguish between components that differ in name but not function. In the following description and in the claims, the terms "include" and "comprise" are used in an open-ended fashion, and thus should be interpreted to mean "include, but not limited to. "substantially" means within an acceptable error range, and a person skilled in the art can solve the technical problem within a certain error range to substantially achieve the technical effect. Furthermore, the term "coupled" is intended to encompass any direct or indirect electrical coupling. Thus, if a first system couples to a second system, that connection may be through a direct electrical coupling, or through an indirect electrical coupling via other systems and couplings. The description which follows is a preferred embodiment of the present application, but is made for the purpose of illustrating the general principles of the application and not for the purpose of limiting the scope of the application. The protection scope of the present application shall be subject to the definitions of the appended claims.
Example 1
The embodiment provides a multi-target radar detection method based on stack filtering. Referring to fig. 1, a specific embodiment of the method for multi-target radar detection based on stack filtering includes:
step 101: the unmanned aerial vehicle 1 detects an obstacle through a millimeter wave radar;
step 102: confirming the authenticity of the barrier and obtaining the GPS coordinate of the barrier;
step 103: uploading the GPS coordinates of the obstacles to a cloud server for storage;
step 104: when flying to a close position, the unmanned aerial vehicle 2 can obtain the GPS coordinates of the obstacles through the cloud server, and early warning is performed in advance.
Wherein, the above unmanned aerial vehicle 1,2 are not specific, are used for illustration only. In practical application, there may be a plurality of drones providing the cloud server with the real obstacle GPS coordinates.
Example 2
The embodiment provides a multi-target radar detection method based on stack filtering. Fig. 2 is another flowchart of the method for multi-target radar detection based on stacked filtering shown in fig. 1; the detailed operation of step 102 shown in fig. 1 is specifically described, which includes:
step 201: after the radar detects the target, the coordinate system of the detected target is converted 84, and the conversion formula is as follows:
X＝(N+H)*cosB*cosL
Y＝(N+H)*cosB*sinL
Z＝[N*(1-e2)+H]*sinB
Figure BDA0003104074290000051
or
Figure BDA0003104074290000052
Figure BDA0003104074290000053
Figure BDA0003104074290000054
In the formula, N is the curvature radius of the ellipsoidal unitary-ground ring, e is the first eccentricity of an ellipsoid, a and b are the long and short radii of the ellipsoid, f is the oblateness of the ellipsoid, W is a first auxiliary coefficient, and X, Y and Z are converted coordinates;
step 202: storing the coordinates of the converted target points into a linked list;
step 203: sorting the data in the linked list to obtain a new target linked list;
step 204: repeatedly executing the above actions, a linked list of all targets about the confidence level can be obtained, and it is determined whether the confidence level is higher than the threshold value? The point with high confidence coefficient is the real target, the point with low confidence coefficient is the false target, when the confidence coefficient is higher than the threshold value, execute step 205, when the confidence coefficient is lower than the threshold value, execute step 206;
step 205: uploading the GPS coordinates of the real target to a cloud server so as to facilitate the use of other unmanned aerial vehicles;
step 206: the dummy target is discarded.
The sorting of the data in the linked list in step 203 specifically includes: 1. removing points with the same position or points with extremely close distance in the linked list; 2. increasing the confidence of the detected points; 3. reducing confidence of undetected points; 4. the point with zero confidence is deleted.
Example 3
The embodiment provides a multi-target radar detection system based on stack filtering. Referring to fig. 3, a specific embodiment of a system 300 for multi-target radar detection based on stack filtering according to the present invention includes millimeter wave radar devices 301 and 306, an unmanned aerial vehicle 1302, an unmanned aerial vehicle 2305, a data analysis algorithm module 303, and a cloud server 304, where:
the unmanned aerial vehicle 1302 is coupled with the millimeter wave radar device 301 and the data analysis algorithm module, detects obstacles through the millimeter wave radar device 301, and transmits the obstacle-related coordinate information to the data analysis algorithm module 303;
the data analysis algorithm module 303 is coupled to the unmanned aerial vehicle 1302 and the cloud server 304, and is configured to receive the coordinate information of the obstacle, perform algorithm analysis, confirm the authenticity of the obstacle, and send the GPS coordinate of the real obstacle to the cloud server 304 for storage;
the cloud server 304 is coupled with the data analysis algorithm module 303 and the unmanned aerial vehicle 2305, and is used for sending the GPS coordinates of the obstacles to the unmanned aerial vehicle 2 when the unmanned aerial vehicle 2 flies to a close position, and early warning is performed;
drone 2305, coupled to cloud server 304 and millimeter wave radar device 306, to receive GPS coordinates of the proximate obstacle from cloud server 304.
Wherein, the above unmanned aerial vehicle 1,2 are not specific, are used for illustration only. In practical application, there may be a plurality of drones providing the cloud server with the real obstacle GPS coordinates.
When the data analysis algorithm module 303 performs algorithm analysis, the following steps are specifically performed:
step 401: after the radar detects the target, the coordinate system of the detected target is converted 84, and the conversion formula is as follows:
X＝(N+H)*cosB*cosL
Y＝(N+H)*cosB*sinL
Z＝[N*(1-e2)+H]*sinB
Figure BDA0003104074290000061
or
Figure BDA0003104074290000062
Figure BDA0003104074290000063
Figure BDA0003104074290000064
In the formula, N is the curvature radius of the ellipsoidal unitary-ground ring, e is the first eccentricity of an ellipsoid, a and b are the long and short radii of the ellipsoid, f is the oblateness of the ellipsoid, W is a first auxiliary coefficient, and X, Y and Z are converted coordinates;
step 402: storing the coordinates of the target point into a linked list;
step 403: sorting the data in the linked list to obtain a new target linked list;
step 404: repeatedly executing the above actions, a linked list of all targets about the confidence level can be obtained, and it is determined whether the confidence level is higher than the threshold value? The point with high confidence coefficient is the real target, the point with low confidence coefficient is the false target, when the confidence coefficient is higher than the threshold value, execute step 405, when the confidence coefficient is lower than the threshold value, execute step 406;
step 405: the coordinates of the real target are uploaded to a cloud server, so that other unmanned aerial vehicles can use the coordinates conveniently;
step 406: the dummy target is discarded.
The sorting of the data in the linked list in the step 403 specifically includes: 1. removing points with the same position or points with extremely close distance in the linked list; 2. increasing the confidence of the detected points; 3. reducing confidence of undetected points; 4. the point with zero confidence is deleted.
The sequence of the above embodiments is only for convenience of description and does not represent the advantages and disadvantages of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
The application direction is as follows:
in our unmanned aerial vehicle system, there are 2 millimeter wave radars for obstacle detection, mounted in front of and behind the aircraft, respectively.
During operation, the targets detected by the front radar and the rear radar are monitored in real time by the unmanned aerial vehicle, and when the existence of an obstacle is confirmed and the flight is obstructed, evasive action is taken.
The position data of the obstacle can be converted to 84 coordinate system, i.e. latitude and longitude, by the algorithm of the present invention. When the unmanned aerial vehicles work in the same land, the position information of the obstacles can be pushed to other unmanned aerial vehicles in the area through the server, so that all the unmanned aerial vehicles in the area can be ensured to find the obstacles quickly.

Claims (10)

1. A multi-target radar detection method based on stack filtering is characterized by comprising the following steps:
the unmanned aerial vehicle 1 detects an obstacle through a millimeter wave radar;
confirming the authenticity of the barrier and obtaining the GPS coordinate of the barrier;
uploading the GPS coordinates of the obstacles to a cloud server for storage; and
when flying to a close position, the unmanned aerial vehicle 2 can obtain the GPS coordinates of the obstacles through the cloud server, and early warning is carried out in advance.
2. The method for stacked filter-based multi-target radar detection according to claim 1, wherein the step of confirming the authenticity of the obstacle and obtaining the GPS coordinates of the obstacle comprises: after the radar detects the target, converting a 84 coordinate system of the detected target; the conversion formula is:
X＝(N+H)*cosB*cosL
Y＝(N+H)*cosB*sinL
Z＝[N*(1-e2)+H]*sinB
Figure FDA0003104074280000011
or
Figure FDA0003104074280000012
Figure FDA0003104074280000013
Figure FDA0003104074280000014
In the formula, N is the curvature radius of the ellipsoidal unitary-ground ring, e is the first eccentricity of an ellipsoid, a and b are the long and short radii of the ellipsoid, f is the oblateness of the ellipsoid, W is the first auxiliary coefficient, and X, Y and Z are converted coordinates.
3. The method for stacked filter-based multi-target radar detection according to claim 2, wherein the step of confirming the authenticity of the obstacle and obtaining the GPS coordinates of the obstacle further comprises: storing the transformed coordinates of the target point into a linked list; sorting the data in the linked list to obtain a new target linked list; repeatedly executing the above actions, a linked list of all targets about the confidence level can be obtained, and it is determined whether the confidence level is higher than the threshold value? And when the confidence coefficient is higher than a threshold value, uploading the GPS coordinates of the real target to a cloud server, and when the confidence coefficient is lower than the threshold value, discarding the false target.
4. The method for multi-target radar detection based on stacked filtering of claim 3, wherein the step of sorting the data in the linked list includes but is not limited to: removing points with the same position or points with extremely close distance in the linked list; increasing the confidence of the detected points; reducing confidence of undetected points; the point with zero confidence is deleted.
5. The method for multi-target radar detection based on stack filtering of any one of claims 1-4, wherein the drone 1 may also be a plurality of drones.
6. The utility model provides a system for multi-target radar detects based on stack formula filtering, its characterized in that, includes millimeter wave radar device 1,2, unmanned aerial vehicle 1, unmanned aerial vehicle 2, data analysis algorithm module and cloud ware, wherein:
the unmanned aerial vehicle 1 is coupled with the millimeter wave radar device 1 and the data analysis algorithm module, detects obstacles through the millimeter wave radar device 1, and transmits coordinate information related to the obstacles to the data analysis algorithm module;
the data analysis algorithm module is coupled with the unmanned aerial vehicle 1 and the cloud server and used for receiving the coordinate information of the obstacle, carrying out algorithm analysis, confirming the authenticity of the obstacle and sending the GPS coordinate of the real obstacle to the cloud server for storage;
the cloud server is coupled with the data analysis algorithm module and the unmanned aerial vehicle 2, and is used for sending the GPS coordinates of obstacles to the unmanned aerial vehicle 2 when the unmanned aerial vehicle 2 flies to a close position, and early warning is carried out in advance; and
the unmanned aerial vehicle 2 is coupled with the cloud server and the millimeter wave radar device 2, and is configured to receive GPS coordinates of a nearby obstacle from the cloud server.
7. The method for multi-target radar detection based on stacked filtering of claim 6, wherein the data analysis algorithm module performing algorithm analysis comprises: after the radar detects the target, converting a 84 coordinate system of the detected target; the conversion formula is:
X＝(N+H)*cosB*cosL
Y＝(N+H)*cosB*sinL
Z＝[N*(1-e2)+H]*sinB
Figure FDA0003104074280000021
or
Figure FDA0003104074280000022
Figure FDA0003104074280000023
Figure FDA0003104074280000024
In the formula, N is the curvature radius of the ellipsoidal unitary-ground ring, e is the first eccentricity of an ellipsoid, a and b are the long and short radii of the ellipsoid, f is the oblateness of the ellipsoid, W is the first auxiliary coefficient, and X, Y and Z are converted coordinates.
8. The system for stacked filter-based multi-target radar detection according to claim 7, wherein the data analysis algorithm module validating the authenticity of the obstacle comprises: storing the transformed coordinates of the target point into a linked list; sorting the data in the linked list to obtain a new target linked list; repeatedly executing the above actions, a linked list of all targets about the confidence level can be obtained, and it is determined whether the confidence level is higher than the threshold value? And when the confidence coefficient is higher than a threshold value, uploading the GPS coordinates of the real target to a cloud server, and when the confidence coefficient is lower than the threshold value, discarding the false target.
9. The system for stacked filter-based multi-target radar detection according to claim 8, wherein the step of sorting the data in the linked list includes but is not limited to: removing points with the same position or points with extremely close distance in the linked list; increasing the confidence of the detected points; reducing confidence of undetected points; the point with zero confidence is deleted.
10. The system for multi-target radar detection based on stack filtering according to any one of claims 6 to 9, wherein the drone 1 may also be a plurality of drones.
CN202110632030.4A 2021-06-07 2021-06-07 Multi-target radar detection method and system based on stack filtering Pending CN113534157A (en)

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