CN110308428B - Method for establishing low-altitude clutter distribution simulation model - Google Patents
Method for establishing low-altitude clutter distribution simulation model Download PDFInfo
- Publication number
- CN110308428B CN110308428B CN201810312052.0A CN201810312052A CN110308428B CN 110308428 B CN110308428 B CN 110308428B CN 201810312052 A CN201810312052 A CN 201810312052A CN 110308428 B CN110308428 B CN 110308428B
- Authority
- CN
- China
- Prior art keywords
- clutter
- region
- distribution
- intensity
- determining
- 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.)
- Active
Links
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
- 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
- G01S7/40—Means for monitoring or calibrating
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Radar Systems Or Details Thereof (AREA)
Abstract
The disclosure relates to a method for establishing a low-altitude clutter distribution simulation model. The method comprises the following steps: determining, for each scanning cycle, a number of clutter within the scanning area; respectively determining the probability of clutter appearing in each region; determining the number of the clutter in each region according to the number of the clutter and the probability of the clutter in each region; determining the position of each clutter in each region according to the number of the clutter in each region and the distribution rule of the clutter in each region; a clutter intensity for each clutter is selected from a clutter intensity database. The number and the position of the clutter in each region are determined according to the characteristics of the clutter distribution in different regions, and a low-altitude clutter distribution simulation model which is more in line with the actual law of complex low-altitude clutter distribution can be established. The model established according to the method for establishing the low-altitude clutter distribution simulation model can verify the performance of the radar target detection and tracking algorithm more accurately.
Description
Technical Field
The disclosure relates to the technical field of radars, in particular to a method for establishing a low-altitude clutter distribution simulation model.
Background
Coherent radar can obtain the radial velocity information of a target, is particularly suitable for detecting low-altitude moving targets, and has increasingly become an important technical means for complex low-altitude space region safety monitoring along with the reduction of the manufacturing cost. However, since the area monitored by the system is a low-altitude airspace, the background environment is complex, the clutter interference is strong, and particularly, the number of the clutter is more in the area with the ground feature. At present, an advanced coherent radar system usually adopts a series of radar target detection and tracking algorithms to eliminate clutter and realize the detection and tracking of low-slow small targets. Therefore, the accurate complex low-altitude clutter distribution simulation model is an important basis for verifying the performance of the radar target detection and tracking algorithm.
The complex low-altitude clutter distribution simulation model in the prior art is not consistent with the actual law of complex low-altitude clutter distribution, so that the performance of a radar target detection and tracking algorithm cannot be accurately verified.
Disclosure of Invention
In view of the above, the present disclosure provides a method for establishing a low-altitude clutter distribution simulation model, which can solve the problem that the performance of a radar target detection and tracking algorithm cannot be accurately verified due to the fact that the low-altitude clutter distribution simulation model does not conform to the actual law of complex low-altitude clutter distribution.
According to an aspect of the present disclosure, there is provided a method for building a low-altitude clutter distribution simulation model, including:
determining the number of clutter in a scanning area for each scanning period, wherein the scanning area is divided into a plurality of areas according to the ground feature distribution condition;
respectively determining the probability of clutter appearing in each region;
determining the number of the clutter in each region according to the number of the clutter and the probability of the clutter in each region;
determining the position of each clutter in each region according to the number of the clutter in each region and the distribution rule of the clutter in each region;
a clutter intensity for each clutter is selected from a clutter intensity database.
In one possible implementation, the determining the probability of the clutter occurring in each region separately includes:
and determining the probability of the clutter in each region in the scanning period according to the probability adjusting parameter of the scanning period, the area of each region and the area of the scanning region.
In one possible implementation, determining the position of each clutter in each region according to the number of clutter in each region and the distribution rule of the clutter in each region includes:
for each region, the location of the clutter in the region is determined based on the number of clutter for the region and a clutter distribution function for the region.
In one possible implementation, each region of the scanning region includes a feature region, and the position of the clutter in the feature region follows a gaussian distribution.
In one possible implementation, each of the scanning areas includes a headroom area, and the position of the clutter in the headroom area is subject to uniform distribution.
In one possible implementation, determining a number of clutter within a scan region includes:
the number of clutter within the scanned region is determined from the poisson distribution.
In one possible implementation, selecting a clutter intensity for each clutter from a clutter intensity database comprises:
and aiming at each clutter, randomly selecting a clutter intensity from the clutter intensity database as the clutter intensity of the clutter.
In one possible implementation, the method further includes:
and establishing a clutter intensity database.
In one possible implementation, building a clutter intensity database includes:
and establishing a clutter intensity database based on Rayleigh distribution.
The number and the position of the clutter in each region are determined according to the characteristics of the clutter distribution in different regions, and a low-altitude clutter distribution simulation model which is more in line with the actual law of complex low-altitude clutter distribution can be established. Therefore, the model established according to the method for establishing the low-altitude clutter distribution simulation model can verify the performance of the radar target detection and tracking algorithm more accurately.
Other features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments, which proceeds with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate exemplary embodiments, features, and aspects of the disclosure and, together with the description, serve to explain the principles of the disclosure.
Fig. 1 shows a flowchart of a method of building a low-altitude clutter distribution simulation model according to an embodiment of the present disclosure.
Fig. 2 shows a schematic diagram of a scanning area according to an embodiment of the present disclosure.
Fig. 3 shows a flowchart of a method of building a low-altitude clutter distribution simulation model according to an embodiment of the present disclosure.
Detailed Description
Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. In the drawings, like reference numbers can indicate functionally identical or similar elements. While the various aspects of the embodiments are presented in drawings, the drawings are not necessarily drawn to scale unless specifically indicated.
The word "exemplary" is used exclusively herein to mean "serving as an example, embodiment, or illustration. Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.
Furthermore, in the following detailed description, numerous specific details are set forth in order to provide a better understanding of the present disclosure. It will be understood by those skilled in the art that the present disclosure may be practiced without some of these specific details. In some instances, methods, means, elements and circuits that are well known to those skilled in the art have not been described in detail so as not to obscure the present disclosure.
Fig. 1 shows a flowchart of a method of building a low-altitude clutter distribution simulation model according to an embodiment of the present disclosure. The simulation model established by the method can be applied to verifying the performance of the radar target detection and tracking algorithm, as shown in fig. 1, and the method can include:
step S11, determining the number of clutter within a scanning area for each scanning cycle, wherein the scanning area is divided into a plurality of areas according to the feature distribution.
In the simulation, the scanning area and the scanning period can be set as required, and in the practical application, a model which is in accordance with the actual rule of the complex low-altitude clutter distribution is to be established, and the scanning area can be determined according to the position data of the radar and the scanning range of the radar. The position data of the radar can comprise the height, position coordinates and the like of the radar, and the scanning range of the radar can be determined according to the model or the type and the like of the radar in practical application.
The scan period may be determined according to an update time of the radar data, and may be related to a scan period of the radar.
As an example of the present disclosure, the scanning area may be divided into a feature area and a non-feature area (clearance area) according to a feature distribution, one or more feature areas may be included in one scanning area, and an area outside the plurality of feature areas may represent the clearance area.
In one possible implementation, the number of clutter in the scanning area is determined for each scanning cycle, and the number of clutter in each scanning cycle may be determined according to a distribution rule obeyed by the number of clutter in each scanning cycle.
In one example, taking a poisson distribution as an example, determining the number of clutter within the scan region for each scan cycle may include: for each scanning cycle, the number of clutter within the scanning area is determined from the poisson distribution.
For example, in the ith scanning period, n is generatediNumber of individual clutter niObey the poisson distribution, as shown in formula (1),
ni～B(λ) (1)
in equation (1), λ may represent the average number of random events occurring per unit time, and B (·) may represent a poisson distribution function.
In step S12, the probability of occurrence of clutter in each region is determined.
The execution sequence of step S11 and step S12 is not sequential, and fig. 1 is only one example of the disclosure.
As an example of the present disclosure, the probability of clutter occurring in each region in a scan cycle may be determined based on the area of each region and the area of the scan region.
As described above, the sweeping can be performed according to the distribution of the ground featuresThe scanning area is divided into a feature area and a non-feature area (clearance area), fig. 2 shows a schematic diagram of the scanning area according to an embodiment of the disclosure, as shown in fig. 2, assuming that the scanning area is S, the scanning area may be a width lxLength l, length lyWherein the ground object region is sgThe ground object area can be wide
Long and long
A rectangular area of (a).
It should be noted that fig. 2 is only an example of the scanning area and the feature area, the shapes of the scanning area and the feature area are not limited to the mode shown in fig. 2, the scanning area may also be a sector area, and the like, and the feature area may determine the shape according to the distribution of the actual features.
In one example, the method may further comprise: and determining a probability adjustment parameter corresponding to each scanning period. The probability of clutter occurring in each region in each scan cycle may be determined based on the probability adjustment parameter for each scan cycle, the area of each region, and the area of the scan region, as shown in equation (2),
in equation (2), for example, for the ith cycle, the probability of clutter in the feature region may be PgThe probability of clutter in a headroom region may be 1-Pg. Wherein, θ in the formula (2) can represent a probability adjusting parameter, and the probability adjusting parameter can be set according to actual needs, so as to adjust the probability of the clutter appearing in each region. | sgI is the area of the ground feature region, | S-SgAnd | is the headroom area.
Since the echo intensity of the feature region and the echo intensity of the clearance region are different, the echo intensities of different regions may also be different, and therefore, in another example, for the ith period, the probability of clutter occurring in the feature region may be,
the probability of clutter in a headroom region may be 1-Pg。
Wherein the content of the first and second substances,
may be the average echo intensity of the region of the earth,
may be the average echo intensity of the clear region.
The scanning area may be divided into a plurality of different feature areas, the average echo intensities and the probability adjusting parameters corresponding to the different feature areas may be different, the average echo intensities corresponding to the different feature areas may be determined according to statistical data in the related art, and then the probability of occurrence of clutter in each feature area is calculated respectively.
It should be noted that, in the case where there are a plurality of feature areas in the scanning area, the probability of occurrence of clutter in each feature area may be determined one by one, and then the probability of occurrence of clutter in the headroom area may be determined.
The above ways of determining the probability of clutter occurring in each region are merely a few examples of the present disclosure, and the present disclosure is not limited thereto.
In step S13, the number of clutter in each region is determined based on the number of clutter and the probability of clutter in each region.
In one example, the number of clutter for each region may be determined from the product of the number of clutter determined in step S11 and the probability of clutter occurring in each region determined in step S12.
For example, taking the scanning area shown in FIG. 2 as an example, the number of clutter is n for the ith cycleiThe probability of clutter in the ground feature region may be
The probability of clutter appearing in a headroom region may be
Then, the number of clutter in the ground feature region may be
The number of clutters in the headroom region may be
Step S14, determining the position of each clutter in each region according to the number of clutter in each region and the distribution rule of clutter in each region.
The ground objects can influence the distribution of the clutter, so that the positions of the clutter in the ground object area and the clearance area can obey different distribution rules, and the positions of the clutter in different ground object areas can also obey different distribution rules.
Wherein, the distribution rule of the clutter may refer to a distribution function obeyed by the position of the clutter in different areas, for example, the position of the clutter in the ground feature area obeys gaussian distribution, as shown in formula (4),
wherein (mu)x,μy) Is the center coordinate of the ground feature region (mu)x,μy) Represents the mean of the normal distribution function N { · },
represents the variance of the normal distribution function N { · },
can be calculated according to the following formula (5)
The position of the clutter in the clearance area is subject to uniform distribution, as shown in equation (6),
[x,y]～U{S-sg} (6)
where S may represent the area of the scan region, SgCan represent the area of the ground feature region, and U {. can represent a two-dimensional uniform distribution function.
In one possible implementation, step S14 may include: for each region, the location of the clutter in the region is determined based on the number of clutter for the region and a clutter distribution function for the region.
For example, still taking the example shown in fig. 2, in the feature region, the position of the clutter follows a gaussian distribution, and in step S13, the number of clutter in the feature region is determined, and the position of the clutter in the feature region can be determined by simulating the distribution of clutter using mathematical software. Likewise, the location of the clutter of the headroom region may also be determined in the same manner.
In step S15, a clutter intensity for each clutter is selected from the clutter intensity database.
The clutter intensity database may be pre-established, and may include a plurality of clutter intensity data.
For example, after the position of each clutter in each region is determined, clutter intensity data may be randomly selected for each clutter from the clutter intensity database as the intensity of the clutter, or clutter intensity data may also be selected for each clutter according to actual ground conditions, which is not limited by the disclosure.
The number and the position of the clutter in each region are determined according to the characteristics of the clutter distribution in different regions, and a low-altitude clutter distribution simulation model which is more in line with the actual law of complex low-altitude clutter distribution can be established. Therefore, the model established according to the method for establishing the low-altitude clutter distribution simulation model can verify the performance of the radar target detection and tracking algorithm more accurately.
Fig. 3 shows a flowchart of a method of building a low-altitude clutter distribution simulation model according to an embodiment of the present disclosure. In one possible implementation, as shown in fig. 3, the method may further include:
and step S16, establishing a clutter intensity database.
In one possible implementation, taking ground clutter as an example, a clutter intensity database may be built based on rayleigh distribution.
The intensity e of the clutter conforms to Rayleigh distribution, the Rayleigh distribution function is shown as a formula (7),
in the formula (7), b is a rayleigh coefficient, and e represents the ground clutter intensity.
The clutter intensities of different clutter may conform to different distribution rules, and the above establishment of the clutter intensity database based on rayleigh distribution is only one example of the present disclosure and does not limit the present disclosure in any way.
In one possible implementation, the range of clutter intensity may be determined first. Taking the ground clutter as an example, assume that the value range of the ground clutter intensity is e ∈ [ ]0,et]The clutter intensity database to be built contains M ground clutter intensity data, the intensity of each ground clutter may be calculated according to the following equation (8),
mj＝M·p(ej)·r,e0<ej＝e0+r·j<et (8)
in formula (8), j is 0,1jRepresents the jth ground clutter intensity, r represents the step size, r ═ et-e0|/M。
It should be noted that the above manner of calculating the ground clutter intensity data is only one example of the disclosure, and the number of the clutter and the value range of the intensity may be set according to actual needs. Moreover, the execution sequence of step S16 in fig. 3 is only an example of the present disclosure, and step S16 is completed before step S15, and for example, may be before step S11, and step S16 does not have to be executed each time, and the clutter intensity database established in step S16 may be used multiple times.
The clutter intensity database is pre-established according to the type of the clutter, and clutter intensity data which are more consistent with the actual rule can be selected for the clutter, so that the performance of a radar target detection and tracking algorithm can be more accurately verified according to the model established by the method for establishing the low-altitude clutter distribution simulation model.
Having described embodiments of the present disclosure, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terms used herein were chosen in order to best explain the principles of the embodiments, the practical application, or technical improvements to the techniques in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (9)
1. A method for establishing a low-altitude clutter distribution simulation model is characterized by comprising the following steps:
determining the number of clutter in a scanning area for each scanning period, wherein the scanning area is divided into a plurality of areas according to the ground feature distribution condition;
respectively determining the probability of clutter appearing in each region;
determining the number of the clutter in each region according to the number of the clutter in the whole scanning region and the probability of the clutter in each region;
determining the position of each clutter in each region according to the number of the clutter in each region and the distribution rule of the clutter in each region;
a clutter intensity for each clutter is selected from a clutter intensity database.
2. The method of claim 1 wherein separately determining the probability of clutter occurring in each region comprises:
and determining the probability of the clutter in each region in the scanning period according to the probability adjusting parameter of the scanning period, the area of each region and the area of the scanning region.
3. The method of claim 1, wherein determining the location of each clutter in each region based on the number of clutter in each region and the distribution of clutter in each region comprises:
for each region, the location of the clutter in the region is determined based on the number of clutter for the region and a clutter distribution function for the region.
4. The method of claim 3, wherein the regions of the scan area include clutter regions whose locations obey a Gaussian distribution.
5. The method of claim 4, wherein the scanning area comprises a headroom region, and wherein the position of the clutter in the headroom region is subject to a uniform distribution.
6. The method of claim 5, wherein determining the number of clutter within the scan area comprises:
the number of clutter within the scanned region is determined from the poisson distribution.
7. The method of claim 6, wherein selecting a clutter intensity for each clutter from a clutter intensity database comprises:
and aiming at each clutter, randomly selecting a clutter intensity from the clutter intensity database as the clutter intensity of the clutter.
8. The method according to any one of claims 1-7, further comprising:
and establishing a clutter intensity database.
9. The method of claim 8, wherein building a clutter intensity database comprises:
and establishing a clutter intensity database based on Rayleigh distribution.
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810257062 | 2018-03-27 | ||
| CN2018102570629 | 2018-03-27 |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110308428A CN110308428A (en) | 2019-10-08 |
| CN110308428B true CN110308428B (en) | 2021-05-14 |
Family
ID=68074052
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810312052.0A Active CN110308428B (en) | 2018-03-27 | 2018-04-09 | Method for establishing low-altitude clutter distribution simulation model |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110308428B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112881988A (en) * | 2021-01-11 | 2021-06-01 | 西北工业大学 | Clutter simulation display method in navigation radar simulation training system |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103207387A (en) * | 2013-03-26 | 2013-07-17 | 北京理工雷科电子信息技术有限公司 | Method for quickly simulating airborne phased array pulse Doppler (PD) radar clutter |
| CN104318593A (en) * | 2014-09-30 | 2015-01-28 | 北京环境特性研究所 | Simulation method and system of radar sea clusters |
| US9341712B2 (en) * | 2010-05-12 | 2016-05-17 | The United States Of America, As Represented By The Secretary Of The Navy | Variable resolution uncertainty expert system for digital bathymetry database |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP5491924B2 (en) * | 2010-03-26 | 2014-05-14 | 株式会社東芝 | Target detection apparatus and target detection method |
| CN102255647B (en) * | 2011-06-16 | 2013-12-25 | 南京信息工程大学 | Method for building meteorological satellite channel probability statistic characteristic simulation models |
| CN107607955B (en) * | 2017-09-13 | 2019-11-15 | 北京敏视达雷达有限公司 | A kind of screening plant of data screening method and ground clutter |
-
2018
- 2018-04-09 CN CN201810312052.0A patent/CN110308428B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9341712B2 (en) * | 2010-05-12 | 2016-05-17 | The United States Of America, As Represented By The Secretary Of The Navy | Variable resolution uncertainty expert system for digital bathymetry database |
| CN103207387A (en) * | 2013-03-26 | 2013-07-17 | 北京理工雷科电子信息技术有限公司 | Method for quickly simulating airborne phased array pulse Doppler (PD) radar clutter |
| CN104318593A (en) * | 2014-09-30 | 2015-01-28 | 北京环境特性研究所 | Simulation method and system of radar sea clusters |
Non-Patent Citations (2)
| Title |
|---|
| Simulation and Study of Windshear Signal in Airborne Pulse Doppler Weather Radar;Yu Ying-jie et al.;《3rd International Conference on Computer Science and Network Technology》;20131012;第1152-1156页 * |
| 基于空域特性的低空空域雷达目标检测;陈唯实;《航空学报》;20150925;第36卷(第9期);第3060-3066页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110308428A (en) | 2019-10-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109541634B (en) | Path planning method and device and mobile device | |
| US10101746B2 (en) | Automated vehicle road model definition system | |
| US10222476B2 (en) | Scanning method and surveying system with scanning functionality | |
| US6522288B1 (en) | Method and apparatus for determining location of objects based on range readings from multiple sensors | |
| CN110443786B (en) | Laser radar point cloud filtering method and device, computer equipment and storage medium | |
| US10509980B2 (en) | Method to provide a vehicle environment contour polyline from detection data | |
| US20210103029A1 (en) | Object detection device, object detection system, and object detection method | |
| CN111380510B (en) | Repositioning method and device and robot | |
| CN111179274B (en) | Map ground segmentation method, device, computer equipment and storage medium | |
| CN111801591B (en) | System and method for calibrating light intensity | |
| CN110412378A (en) | Target object detection method and device | |
| CN113111513B (en) | Sensor configuration scheme determining method and device, computer equipment and storage medium | |
| CN111830501B (en) | HRRP history feature assisted signal fuzzy data association method and system | |
| CN110716209B (en) | Map construction method, map construction equipment and storage device | |
| CN115840205A (en) | Laser radar technology-based geosurface area metering method and system | |
| CN110308428B (en) | Method for establishing low-altitude clutter distribution simulation model | |
| CN114966591A (en) | Large target detection method, large target detection device and electronic equipment | |
| CN113269889B (en) | Self-adaptive point cloud target clustering method based on elliptical domain | |
| KR101814023B1 (en) | Apparatus and Method for Automatic Calibration of Finite Difference Grid Data | |
| CN112508970B (en) | Point cloud data segmentation method and device | |
| CN115308770A (en) | Dynamic obstacle detection method based on fitting graph | |
| CN110909569B (en) | Road condition information identification method and terminal equipment | |
| CN117420569B (en) | Inversion method of non-uniform wind field based on Doppler laser wind finding radar | |
| KR102302744B1 (en) | Track generating method and device thereof | |
| Roos et al. | A method for evaluation and optimization of automotive camera systems based on simulated raw sensor data |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |