CN112904332A - Gradient detection algorithm of millimeter wave radar altimeter - Google Patents
Gradient detection algorithm of millimeter wave radar altimeter Download PDFInfo
- Publication number
- CN112904332A CN112904332A CN202110082229.4A CN202110082229A CN112904332A CN 112904332 A CN112904332 A CN 112904332A CN 202110082229 A CN202110082229 A CN 202110082229A CN 112904332 A CN112904332 A CN 112904332A
- Authority
- CN
- China
- Prior art keywords
- slope
- radar
- aerial vehicle
- unmanned aerial
- millimeter wave
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000001514 detection method Methods 0.000 title claims abstract description 17
- 238000012545 processing Methods 0.000 claims description 5
- 238000000034 method Methods 0.000 claims description 4
- 238000013461 design Methods 0.000 claims description 3
- 238000010586 diagram Methods 0.000 claims description 3
- RZVHIXYEVGDQDX-UHFFFAOYSA-N 9,10-anthraquinone Chemical compound C1=CC=C2C(=O)C3=CC=CC=C3C(=O)C2=C1 RZVHIXYEVGDQDX-UHFFFAOYSA-N 0.000 abstract description 3
- 230000001174 ascending effect Effects 0.000 abstract description 3
- 238000007796 conventional method Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000004880 explosion Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
Images
Classifications
-
- 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
- G01S13/882—Radar or analogous systems specially adapted for specific applications for altimeters
-
- 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
- G01S13/06—Systems determining position data of a target
- G01S13/08—Systems for measuring distance only
-
- 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
- G01S13/06—Systems determining position data of a target
- G01S13/42—Simultaneous measurement of distance and other co-ordinates
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- Computer Networks & Wireless Communication (AREA)
- General Physics & Mathematics (AREA)
- Electromagnetism (AREA)
- Radar Systems Or Details Thereof (AREA)
Abstract
The invention discloses a gradient detection algorithm of a millimeter wave radar altimeter, which comprises the following steps: (1) designing a waveform and acquiring data; (2) obtaining distance and orientation data; (3) outputting target point cloud data; (4) calculating the height value of the unmanned aerial vehicle; (5) detecting the slope of the unmanned aerial vehicle; (6) and outputting a radar result. According to the gradient detection algorithm of the millimeter wave radar altimeter, accurate altitude estimation is carried out by using two-dimensional point cloud data of a millimeter wave radar, and meanwhile, the slope of a slope is obtained by using multi-point least square fitting; the radar can obtain flight height information in real time, can also detect that the ground is an ascending slope or a descending slope, and can feed back lifting or descending signals to the unmanned aerial vehicle control module in advance to trigger lifting or descending actions, so that the response speed of flight control is improved, and the unmanned aerial vehicle can also keep relatively stable high flight on the slope surface.
Description
Technical Field
The invention relates to the technical field of slope detection, in particular to a slope detection algorithm of a millimeter wave radar altimeter.
Background
When the unmanned aerial vehicle flies in the complex environment with fluctuating terrain such as mountains, hills and terraces, the height drop can be caused in the slope flight due to the fact that the unmanned aerial vehicle flies and controls signals with certain lag, constant-height flight is difficult to keep, and severe consequences such as unmanned aerial vehicle explosion and casualties can be caused seriously. In order to ensure safe operation of the unmanned aerial vehicle in a complex environment, an effective solution becomes extremely important.
Disclosure of Invention
According to the gradient detection algorithm of the millimeter wave radar altimeter, accurate altitude estimation is carried out by using two-dimensional point cloud data of a millimeter wave radar, and meanwhile, the slope of a slope is obtained by using multi-point least square fitting; the radar can obtain flight height information in real time, can also detect that the ground is an ascending slope or a descending slope, and can feed back lifting or descending signals to the unmanned aerial vehicle control module in advance to trigger lifting or descending actions, so that the response speed of flight control is improved, and the unmanned aerial vehicle can also keep relatively stable high flight on the slope surface.
In order to achieve the purpose, the invention adopts the following technical scheme:
a gradient detection algorithm of a millimeter wave radar altimeter comprises the following steps:
(1) waveform design and data acquisition: after the transmitting and receiving waveforms of the millimeter wave radar are configured, acquiring multi-channel echo data;
(2) obtaining distance and orientation data: processing the multi-channel echo data to obtain a distance azimuth map;
(3) outputting target point cloud data: performing a CFAR detection algorithm in a distance direction diagram, outputting target point cloud data, obtaining xy coordinates of a target, and obtaining echo energy reflected by a radar;
(4) calculating the height value of the unmanned aerial vehicle: selecting a strongest point in the point cloud data, clustering a plurality of points near the strongest point into a target cluster, solving a mass center, and calculating to obtain the distance from the target mass center to the radar, wherein the distance is the current flight height H;
(5) unmanned aerial vehicle slope detects: the radar point cloud data may be represented as L { (x)1,y1),(x2,y2),(x3,y3)...(xn,yn) Taking the function f (x) ax + b, and solving a fitting curve by using a least square method so that the function f (x) fits L as much as possible; the principle of least squares is to find the best function match of data by minimizing the sum of squares of errors, i.e. minimizing the Q value, as shown in the formula:
solving the minimum value of Q ═ f (a, b), i.e. solving for the point (a, b), making the value of f (a, b) extremely small, and solving for the value of f (a, b) extremely small using the partial derivative, as shown in the formula:
after the slope a and the intercept b are obtained, the inclination angle theta of a straight line is obtained, wherein when theta is greater than 0, the slope is downhill, and when theta is less than 0, the slope is uphill;
(6) outputting a radar result: and the radar outputs the height value H and the ground slope value theta of the unmanned aerial vehicle in real time.
Preferably, in the step (2), the two-dimensional FFT processing is performed on the multi-channel echo data to obtain a distance bitmap.
Compared with the prior art, the invention has the beneficial effects that:
(1) the millimeter wave radar altimeter can measure the flying height in real time all day long and all weather, and can output a two-dimensional point cloud image;
(2) the slope detection algorithm can detect that the ground is an ascending slope or a descending slope, and feeds back a lifting or descending signal to the unmanned aerial vehicle control module in advance to trigger lifting or descending action, so that the response speed of flight control is improved, and the unmanned aerial vehicle can also keep relatively stable high flight on the slope;
(3) the radar algorithm of the patent is high in operation speed and high in real-time performance, and feasibility of the radar algorithm is verified through a test flight experiment, and a good flight effect is achieved.
Drawings
FIG. 1 is a radar point cloud result chart during uphill flight according to the present invention;
FIG. 2 is a radar point cloud result chart during downhill flight according to the present invention;
FIG. 3 is a diagram showing the result of the radar point cloud during flying on a flat ground according to the present invention;
FIG. 4 is a flow chart of a slope detection algorithm for the radar altimeter of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
In the description of the present invention, "a plurality" means two or more unless otherwise specified; the terms "upper", "lower", "left", "right", "inner", "outer", "front", "rear", "head", "tail", and the like, indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, are only for convenience in describing and simplifying the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed in a particular orientation, and be operated, and thus, should not be construed as limiting the invention. Furthermore, the terms "first," "second," "third," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it is to be noted that, unless otherwise explicitly specified or limited, the terms "connected" and "connected" are to be interpreted broadly, e.g., as being fixed or detachable or integrally connected; can be mechanically or electrically connected; may be directly connected or indirectly connected through an intermediate. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Example (b):
referring to fig. 1-4, a gradient detection algorithm for a millimeter wave radar altimeter includes the following steps:
(1) waveform design and data acquisition: after the transmitting and receiving waveforms of the millimeter wave radar are configured, acquiring multi-channel echo data;
(2) obtaining distance and orientation data: performing two-dimensional FFT processing on the multi-channel echo data (the two-dimensional FFT is a conventional method and is not described in detail) to obtain a distance square map;
(3) outputting target point cloud data: carrying out a CFAR detection algorithm (the CFAR detection algorithm is a conventional method and is not repeated) in the distance square map, outputting target point cloud data, and obtaining xy coordinates of a target and echo energy reflected by the radar;
(4) calculating the height value of the unmanned aerial vehicle: selecting a strongest point in the point cloud data, clustering a plurality of points near the strongest point into a target cluster, solving a mass center, and calculating to obtain the distance from the target mass center to the radar, wherein the distance is the current flight height H;
(5) unmanned aerial vehicle slope detects: the radar point cloud data may be represented as L { (x)1,y1),(x2,y2),(x3,y3)...(xn,yn) Taking the function f (x) ax + b, and solving a fitting curve by using a least square method so that the function f (x) fits L as much as possible; the principle of least squares is to find the best function match of data by minimizing the sum of squares of errors, i.e. minimizing the Q value, as shown in the formula:
solving the minimum value of Q ═ f (a, b), i.e. solving for the point (a, b), making the value of f (a, b) extremely small, and solving for the value of f (a, b) extremely small using the partial derivative, as shown in the formula:
after the slope a and the intercept b are obtained, the inclination angle theta of a straight line is obtained, wherein when theta is greater than 0, the slope is downhill, and when theta is less than 0, the slope is uphill;
(6) outputting a radar result: and the radar outputs the height value H and the ground slope value theta of the unmanned aerial vehicle in real time.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be able to cover the technical scope of the present invention and the equivalent alternatives or modifications according to the technical solution and the inventive concept of the present invention within the technical scope of the present invention.
Claims (2)
1. A gradient detection algorithm of a millimeter wave radar altimeter is characterized by comprising the following steps:
(1) waveform design and data acquisition: after the transmitting and receiving waveforms of the millimeter wave radar are configured, acquiring multi-channel echo data;
(2) obtaining distance and orientation data: processing the multi-channel echo data to obtain a distance azimuth map;
(3) outputting target point cloud data: performing a CFAR detection algorithm in a distance direction diagram, outputting target point cloud data, obtaining xy coordinates of a target, and obtaining echo energy reflected by a radar;
(4) calculating the height value of the unmanned aerial vehicle: selecting a strongest point in the point cloud data, clustering a plurality of points near the strongest point into a target cluster, solving a mass center, and calculating to obtain the distance from the target mass center to the radar, namely the current flight height H;
(5) unmanned aerial vehicle slope detects: the radar point cloud data may be represented as L { (x)1,y1),(x2,y2),(x3,y3)…(xn,yn) Taking the function f (x) ax + b, and solving a fitting curve by using a least square method so that the function f (x) fits L as much as possible; the least square method principle is that the best function matching of data is found by minimizing the square sum of errors, namely, the Q value is minimum, and the formula is as follows:
solving the minimum value of Q ═ f (a, b), i.e. solving for the point (a, b), making the value of f (a, b) extremely small, and solving for the value of f (a, b) extremely small using the partial derivative, as shown in the formula:
after the slope a and the intercept b are obtained, the inclination angle theta of a straight line is obtained, wherein when theta is greater than 0, the slope is downhill, and when theta is less than 0, the slope is uphill;
(6) outputting a radar result: and the radar outputs the height value H and the ground slope value theta of the unmanned aerial vehicle in real time.
2. The gradient detection algorithm of the millimeter wave radar altimeter according to claim 1, characterized in that in the step (2), the two-dimensional FFT processing is performed on the multi-channel echo data to obtain a distance direction map.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110082229.4A CN112904332A (en) | 2021-01-21 | 2021-01-21 | Gradient detection algorithm of millimeter wave radar altimeter |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110082229.4A CN112904332A (en) | 2021-01-21 | 2021-01-21 | Gradient detection algorithm of millimeter wave radar altimeter |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112904332A true CN112904332A (en) | 2021-06-04 |
Family
ID=76117530
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110082229.4A Pending CN112904332A (en) | 2021-01-21 | 2021-01-21 | Gradient detection algorithm of millimeter wave radar altimeter |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112904332A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115033026A (en) * | 2022-08-10 | 2022-09-09 | 长沙莫之比智能科技有限公司 | Obstacle avoidance and height determination method for oblique side-mounted unmanned aerial vehicle millimeter wave radar carrying cloud deck |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107515620A (en) * | 2017-10-20 | 2017-12-26 | 广州极飞科技有限公司 | A kind of unmanned plane imitative ground flight control method and device |
| CN108664034A (en) * | 2018-05-10 | 2018-10-16 | 杭州瓦屋科技有限公司 | Imitative ground flying method and device of the plant protection drone in tea place |
| CN108828621A (en) * | 2018-04-20 | 2018-11-16 | 武汉理工大学 | Obstacle detection and road surface partitioning algorithm based on three-dimensional laser radar |
| CN110134131A (en) * | 2019-04-10 | 2019-08-16 | 广州极飞科技有限公司 | Control method and control device and unmanned vehicle for unmanned vehicle |
| CN111413708A (en) * | 2020-04-10 | 2020-07-14 | 湖南云顶智能科技有限公司 | Unmanned aerial vehicle autonomous landing site selection method based on laser radar |
| CN111465822A (en) * | 2018-11-26 | 2020-07-28 | 深圳市大疆创新科技有限公司 | Ground point cloud map precision evaluation method, device and system and unmanned aerial vehicle |
-
2021
- 2021-01-21 CN CN202110082229.4A patent/CN112904332A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107515620A (en) * | 2017-10-20 | 2017-12-26 | 广州极飞科技有限公司 | A kind of unmanned plane imitative ground flight control method and device |
| CN108828621A (en) * | 2018-04-20 | 2018-11-16 | 武汉理工大学 | Obstacle detection and road surface partitioning algorithm based on three-dimensional laser radar |
| CN108664034A (en) * | 2018-05-10 | 2018-10-16 | 杭州瓦屋科技有限公司 | Imitative ground flying method and device of the plant protection drone in tea place |
| CN111465822A (en) * | 2018-11-26 | 2020-07-28 | 深圳市大疆创新科技有限公司 | Ground point cloud map precision evaluation method, device and system and unmanned aerial vehicle |
| CN110134131A (en) * | 2019-04-10 | 2019-08-16 | 广州极飞科技有限公司 | Control method and control device and unmanned vehicle for unmanned vehicle |
| CN111413708A (en) * | 2020-04-10 | 2020-07-14 | 湖南云顶智能科技有限公司 | Unmanned aerial vehicle autonomous landing site selection method based on laser radar |
Non-Patent Citations (1)
| Title |
|---|
| 孙慧等: "《概率论与数理统计》", 31 October 2017 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115033026A (en) * | 2022-08-10 | 2022-09-09 | 长沙莫之比智能科技有限公司 | Obstacle avoidance and height determination method for oblique side-mounted unmanned aerial vehicle millimeter wave radar carrying cloud deck |
| CN115033026B (en) * | 2022-08-10 | 2022-12-20 | 长沙莫之比智能科技有限公司 | Obstacle avoidance and height determination method for oblique side-mounted unmanned aerial vehicle millimeter wave radar carrying cloud deck |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106990401B (en) | full-waveform airborne laser radar data-based two-class elevation error correction method | |
| CN104391507B (en) | Control method and system of unmanned aerial vehicle, and unmanned aerial vehicle | |
| CN107783133B (en) | Anti-collision system and anti-collision method for fixed-wing unmanned aerial vehicle of millimeter wave radar | |
| US9110170B1 (en) | Terrain aided navigation using multi-channel monopulse radar imaging | |
| CN100365429C (en) | Synthetic aperture radar moving target imaging method | |
| CN110426690B (en) | Automatic calibration method for airborne weather radar beam pointing | |
| CN108957445A (en) | A kind of low-altitude low-velocity small targets detection system and its detection method | |
| CN103529437B (en) | Method used for captive-balloon-borne phased array radar to distinguish open space targets under multi-target condition | |
| US20070222667A1 (en) | Methods and systems for identifying high-quality phase angle measurements in an interferometric radar system | |
| CN107817488A (en) | The unmanned plane obstacle avoidance apparatus and barrier-avoiding method merged based on millimetre-wave radar with vision | |
| CN108520640B (en) | Ultra-wideband-based unmanned aerial vehicle navigation method, navigation equipment and unmanned aerial vehicle | |
| CN202141803U (en) | Multi-mode millimeter wave radar used for unmanned device for electric power line patrol | |
| JP2009115823A (en) | Method and device for radar data processing | |
| CN108761419A (en) | Low level wind shear velocity estimation method based on combination main channel self-adaptive processing when empty | |
| AU2015223120A1 (en) | Single platform Doppler geolocation | |
| CN108181620B (en) | Three-coordinate radar trace point quality evaluation method | |
| WO2014165266A1 (en) | Single beam wind speed and direction determination | |
| CN107783128B (en) | Multi-target anti-collision system of fixed-wing unmanned aerial vehicle based on millimeter wave radar | |
| CN106405513A (en) | Ground clutter suppression method based on multi-layer scanning contrast | |
| CN112904332A (en) | Gradient detection algorithm of millimeter wave radar altimeter | |
| WO2022047505A1 (en) | Radar odometry system and method | |
| CN115480240A (en) | Multi-sensor cooperative detection method for small unmanned aerial vehicle | |
| CN103064084A (en) | Ambiguity solving method based on distance frequency domain | |
| CN111025298A (en) | Unmanned aerial vehicle topography survey system | |
| CN114966579A (en) | Method and device for acquiring calibration parameters of radar system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20210604 |
|
| RJ01 | Rejection of invention patent application after publication |