CN113189593A - Weather radar insect reflectivity vertical profile inversion algorithm - Google Patents
Weather radar insect reflectivity vertical profile inversion algorithm Download PDFInfo
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- 238000002310 reflectometry Methods 0.000 title claims abstract description 38
- 241000238631 Hexapoda Species 0.000 title claims abstract description 33
- 238000000034 method Methods 0.000 claims abstract description 24
- 238000009826 distribution Methods 0.000 claims abstract description 13
- 239000013598 vector Substances 0.000 claims description 20
- 239000011159 matrix material Substances 0.000 claims description 17
- 238000000354 decomposition reaction Methods 0.000 claims description 3
- 238000013508 migration Methods 0.000 abstract description 7
- 230000005012 migration Effects 0.000 abstract description 7
- 238000012544 monitoring process Methods 0.000 abstract description 6
- 241000607479 Yersinia pestis Species 0.000 abstract description 4
- 230000009141 biological interaction Effects 0.000 abstract description 2
- 201000010099 disease Diseases 0.000 abstract description 2
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 abstract description 2
- 230000001617 migratory effect Effects 0.000 description 12
- 239000002028 Biomass Substances 0.000 description 2
- 238000007796 conventional method Methods 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
- 239000006185 dispersion Substances 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 241000894007 species Species 0.000 description 2
- 241000271566 Aves Species 0.000 description 1
- 241000288673 Chiroptera Species 0.000 description 1
- 241000700605 Viruses Species 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000004071 biological effect Effects 0.000 description 1
- 230000004069 differentiation Effects 0.000 description 1
- 238000009792 diffusion process Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000002068 genetic effect Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 244000005700 microbiome Species 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000002265 prevention Effects 0.000 description 1
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- 238000012545 processing Methods 0.000 description 1
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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
- G01S13/95—Radar or analogous systems specially adapted for specific applications for meteorological use
- G01S13/958—Theoretical aspects
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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
- G01S13/006—Theoretical aspects
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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
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
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Abstract
The invention discloses a regularization method for inverting insect vertical profile by using weather radar data, which is an insect reflectivity vertical profile inversion algorithm for weather radar; the invention can realize the observation of aerial biological vertical distribution by using the observation data of the weather radar based on the weather radar biological observation model; the method is helpful for monitoring the migration flying condition of organisms, preventing the outbreak of plant diseases and insect pests in different places and researching the biological interaction.
Description
Technical Field
The invention belongs to the technical field of weather radars, and particularly relates to an inversion algorithm of an insect reflectivity vertical profile based on weather radar data.
Background
The biological migration is a great biological phenomenon of human living environment and is an important component of the aerial ecosystem. Hundreds of millions of birds, bats and insects fly in long distance every year, and the flying distance can reach hundreds of kilometers. Biological migration affects species diversity and stability, promotes geographical diffusion and genetic differentiation of species, and simultaneously causes wide-range spread of viruses and microorganisms. The large-scale quantitative observation of the migratory organisms is of great significance to the prevention of the outbreak of plant diseases and insect pests and the research of the evolution process of an ecological system.
China migratory flight insect disasters occur for more than 20 hundred million mu times all year round, and major agricultural and forestry pests and grassland pests have strong long-distance migratory flight capability (thousands of kilometers). The core of the remote migratory flying insect control is to carry out effective large-scale monitoring on the migratory flying insects so as to obtain the number and the track of the migratory flying insects. The traditional insect detection radar cannot carry out effective large-scale monitoring on migratory flying insects. The weather radar network is wide in coverage range and is the most effective means for researching large-scale migration flight. Therefore, the quantitative research of the migratory organisms by using the weather radar has important significance.
In order to study the situation that insects fly at different heights, moustache et al propose a model for inverting the vertical profile of reflectivity by using weather radar reflectivity data. A regularization method is adopted in the model solving method, but the existing regularization method has the problem of instability, and the signal-to-noise ratio is deteriorated to cause the solving result to deviate from the optimal solution, so that the result cannot meet the inversion requirement. The current regularization method has a fuzzy problem on inversion results of different data under different signal-to-noise ratios.
Disclosure of Invention
In view of this, the invention provides an inversion algorithm for a vertical profile of insect reflectivity of a weather radar, which can improve the accuracy of vertical distribution of airborne organisms of the weather radar. This facilitates monitoring of biological migration, analysis of biological activity as influenced by meteorological factors, and study of biological interactions.
An insect reflectivity vertical profile inversion algorithm based on weather radar data comprises the following steps:
dividing the detected altitude of the weather radar into a plurality of altitude layers; reading the angle information of each elevation angle and calculating an observation matrix Am×n：
Am×n＝[β1；...；βm]；
Wherein beta is1；...；βmIs the contribution vector of the reflectivity factor corresponding to the first to the mth beam elevation angles, and each contribution vector is contributed by the height layer at the beam elevation angleiThe method comprises the following steps:
f2(θ, φ) is the weather radar antenna pattern, ΩiIs the volume of the ith elevation layer within the radar beam;Ωrepresents the volume of the entire height layer within the radar beam;
then VPR vector xnAnd a measured reflectance factor vector bmThe relationship between them is represented by the following formula:
Am×nxn＝bm
wherein, the unknown number xn＝[z1,z2,...,zn]T，ziA true value representing a radar reflectivity factor for the ith elevation layer; 1,2, n; bmRepresenting the measured reflectivity factor vectors at different beam elevation angles;
for matrix Am×nIt is subjected to singular value decomposition to obtain the following form
Am×n＝UΣVT
Wherein U is E.Rm×m，V∈Rn×nAre all orthogonal matrices, the matrix Σ ∈ Rm×nIs a diagonal matrix:
Σ＝diag(σ1,σ2,…,σn)
where the element values on the diagonal [ sigma ]iIs matrix Am×nThe singular value of (a);
at this time, the unknown number xnThe solution of each element in (1) is expressed as:
wherein u isiAnd viIs the column vector of U and V, k is the regularization parameter, the value is 1-n;
determining an inflection point in the L curve, and determining a solution of k, specifically:
the L-curve consists of the following discrete points:
λp＝(yp,xp)
wherein y isp＝||Am×nxp-bm||，xp＝||xp||，p＝1,…,n；
Two points from the head to the tail of the L curve and any other points can be fitted into a quadratic curve; removing the points of which the quadratic term coefficient a is less than or equal to 0 from the L curve, and reserving other points;
then, forming a triangle by the reserved point on the L curve and the head point and the tail point; selecting a triangle with the largest area from all the formed triangles, wherein the middle point of the three points forming the triangle with the largest area is an alternative inflection point;
then, on the L curve, calculating the slopes of two adjacent points from the 'alternative inflection point' forward, when the slope is greater than a set value for the first time, taking the point which is close to the 'alternative inflection point' in the two points forming the slope as the 'inflection point' of the L curve, wherein the serial number k of the point is a regularization parameter k;
finally substituting k into xpIn the formula, the vertical distribution reflectivity of the insects in the air is obtained.
Preferably, the height layer spacing is 20 m.
Preferably, the setting value of the slope is modified appropriately according to different data.
Preferably, the set value is-0.5.
The invention has the following beneficial effects:
the invention relates to a weather radar insect reflectivity vertical profile inversion algorithm, which provides an effective means for monitoring large-scale migration biomass. Compared with the traditional migratory flight biomass monitoring method, the method can improve the accuracy of extracting the vertical distribution of the biological reflectivity from the weather radar data.
Drawings
FIG. 1 is a method for selecting a slope-based regularization parameter for a discrete L-curve.
FIG. 2 is a quadratic curve fitted to two different types of points on a dispersion L curve.
Fig. 3 is a triangle formed by three points on the dispersion L curve.
FIG. 4 is a comparison of aerial biological vertical distributions inverted by the method of the present invention and by a conventional method.
Detailed Description
The invention is described in detail below, by way of example, with reference to the accompanying drawings.
For insects or birds flying at large distances, the migratory organisms are concentrated in a thin layer, but the wide horizontal distribution range is one of the most common migration forms. Under stable climatic conditions, most migratory organisms are evenly distributed in the horizontal direction but are concentrated at a specific height according to the characteristics of temperature and wind. Thus, for large scale migratory flight activities, the assumption of a uniform level of biodistribution is reasonable. Under this assumption, the vertical reflectivity profile (VPR) at any point in the detection range of the weather radar is the same, since the radar reflectivity factor reflects the echo intensity of the detected creatures. The VPR can be layered with a radar reflectivity factor of z for each layeri. In this case, VPR can represent x by a column vectorn＝[z1,z2,...,zn]T. The energy of the radar beam is not uniform and the reflectivity factors of the different layers within its coverage area contribute differently to the measured reflectivity factor. The reflectivity factor detected by the weather radar can be expressed as:
wherein beta isiThe contribution degree of the reflectivity factor of each height layer can be calculated by the following formula
Wherein f is2(θ, φ) is the antenna pattern, ΩiIs the volume of the elevation layer within the radar beam. (1) The formula (2) shows the measured reflectance factor zmA reflectivity factor equal to the sum of the integral weights of the layers of different heights over the corresponding volume according to the antenna pattern. To simplify the expression, β can be expressediWritten as observation vector β ═ β1,...,βn]. When this is the case, formula (1) can be represented as
βxn＝zm (3)
Weather radar data from different elevation angles can be written as a vector b of measured reflectivity factorsm＝[zm1,...,zmm]T. The observation vectors associated with each element in the vector may form an observation matrix am×n＝[β1；...；βm]. Then VPR vector xnAnd a measured reflectance factor vector bmThe relationship therebetween can be expressed by the following equation.
Am×nxn＝bm (4)
Solving this equation requires the introduction of regularization means. Regularization method for solving unconstrained linear least square problem for ill-defined equation
min||Am×nxn-bm||，m≥n (5)
Wherein, | | · | | represents solving a two-norm. When the matrix A ism×nIs ill-conditioned, and problem (5) is an ill-conditioned problem, meaning that A is am×nOr bmVery small perturbations in the solution will cause severe perturbations.
There are two main regularization methods, one is the gihonov regularization method, and the other is the truncated singular value method (TSVD). The invention solves the indeterminate equation based on the TSVD.
For matrix Am×nIt can be subjected to singular value decomposition to obtain the following form
Am×n＝UΣVT (6)
Wherein U is E.Rm×m，V∈Rn×nAre all orthogonal matrices, the matrix Σ ∈ Rm×nIs a diagonal matrix:
Σ＝diag(σ1,σ2,…,σn) (7)
where the element values on the diagonal [ sigma ]iIs matrix Am×nSingular value of
The solution of equation (4) at this time can be expressed as
Wherein u isiAnd viIs the column vector of U and V, and k is the regularization parameter, taking the value of 1-n. When k is properly selected, the ill-defined equation (4) is transformed into a well-defined equation.
Solving the regularization parameter k utilizes an L-curve method. The L-curve is an effective method for solving the regularization parameters, and the 'inflection point' of the L-curve corresponds to the regularization parameters. As shown in fig. 1, the L-curve of TSVD is composed of a number of discrete points.
λp＝(yp,xp) (9)
Wherein y isp＝||Axp-b||，xp＝||xp||，p＝1,…,n。
Then, in order to simplify the subsequent processing, points on the L-curve need to be screened, as shown in fig. 2. Two points from head to tail on the L curve and any point except the two points can be fitted into a quadratic curve
y＝ax2+bx+c (10)
The coefficient a of the quadratic term can be used for screening whether points except the head and the tail meet requirements or not. If a >0, the point is satisfactory, otherwise it is not.
Then, as shown in fig. 3, the desired point and the beginning and end points of the L-curve may form a triangle. The triangle having the largest area is selected from all the triangles, and the middle point of the three points constituting the triangle having the largest area is the "alternative inflection point".
Then, the true "inflection point" is sought forward from the "alternative inflection point". The slopes of two adjacent points are calculated from the "alternative inflection point" forward, and as shown in fig. 1, the slopes are negative and gradually increase to 0. When the slope is greater than-0.5, the point which is close to the alternative inflection point in the two points forming the slope is taken as the inflection point of the L curve, and the serial number k of the point is the regularization parameter k.
Then, the regularization parameter k value is substituted into the formula (8), namely the radar reflectivity observed by the weather radar can be inverted to obtain the vertical distribution of the airborne insects. The traditional method can not obtain inversion results with lower errors and more stability. Based on the existing weather radar insect observation model, the result error is smaller through a new regularization parameter solving technology, and the accuracy of insect vertical distribution estimation is improved.
Therefore, the invention provides a regularization method for insect vertical distribution inversion by using weather radar data, and the implementation steps are described in the following specific embodiments:
in order to verify the method, the inversion estimation of the distribution of the insect reflectivity is completed by adopting the inversion algorithm of the insect reflectivity vertical profile based on the weather radar data based on the measured data of the Shandong Binzhou S-band weather radar.
Reading data of a weather radar reflectivity factor Z value, and converting the data into a reflectivity Z. The height inversion interval is set, here to 20 m. Reading the angle information of each elevation angle, and calculating a matrix A according to the existing observation modelm×n。
Step two, calculating a regularization parameter k according to the method, and calculating a regularization solution x by using k and a formula (8)p. Regularization solution xpNamely the required vertical distribution reflectivity of the aerial insects. A comparison of aerial biological vertical distributions inverted by the present method and the conventional method is shown in FIG. 4.
In summary, the above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (5)
1. An insect reflectivity vertical profile inversion algorithm based on weather radar data is characterized by comprising the following steps:
dividing the detected altitude of the weather radar into a plurality of altitude layers; reading the angle information of each elevation angle and calculating an observation matrix Am×n：
Am×n＝[β1；...；βm]；
Wherein beta is1；...；βmIs the contribution vector of the reflectivity factor corresponding to the first to the mth beam elevation angles, and each contribution vector is contributed by the height layer at the beam elevation angleiThe method comprises the following steps:
f2(θ, φ) is the weather radar antenna pattern, ΩiIs the volume of the ith elevation layer within the radar beam;Ωrepresents the volume of the entire height layer within the radar beam;
then VPR vector xnAnd a measured reflectance factor vector bmBetweenIs represented by the following formula:
Am×nxn＝bm
wherein, the unknown number xn＝[z1,z2,...,zn]T，ziA true value representing a radar reflectivity factor for the ith elevation layer; 1,2, n; bmRepresenting the measured reflectivity factor vectors at different beam elevation angles;
for matrix Am×nIt is subjected to singular value decomposition to obtain the following form
Am×n＝U∑VT
Wherein U is E.Rm×m，V∈Rn×nAre all orthogonal matrices, the matrix ∈ Rm×nIs a diagonal matrix:
∑＝diag(σ1,σ2,…,σn)
where the element values on the diagonal [ sigma ]iIs matrix Am×nThe singular value of (a);
at this time, the unknown number xnThe solution of each element in (1) is expressed as:
wherein u isiAnd viIs the column vector of U and V, k is the regularization parameter, the value is 1-n;
determining an inflection point in the L curve, and determining a solution of k, specifically:
the L-curve consists of the following discrete points:
λp＝(yp,xp)
wherein y isp＝||Am×nxp-bm||，xp＝||xp||，p＝1,…,n；
Two points from the head to the tail of the L curve and any other points can be fitted into a quadratic curve; removing the points of which the quadratic term coefficient a is less than or equal to 0 from the L curve, and reserving other points;
then, forming a triangle by the reserved point on the L curve and the head point and the tail point; selecting a triangle with the largest area from all the formed triangles, wherein the middle point of the three points forming the triangle with the largest area is an alternative inflection point;
then, on the L curve, calculating the slopes of two adjacent points from the 'alternative inflection point' forward, when the slope is greater than a set value for the first time, taking the point which is close to the 'alternative inflection point' in the two points forming the slope as the 'inflection point' of the L curve, wherein the serial number k of the point is a regularization parameter k;
finally substituting k into xpIn the formula, the vertical distribution reflectivity of the insects in the air is obtained.
2. The insect reflectivity vertical profile inversion algorithm of claim 1, wherein the height level spacing is 20 m.
3. The insect reflectivity vertical profile inversion algorithm according to claim 1, wherein the slope setting is modified based on different data.
4. The insect reflectivity vertical profile inversion algorithm of claim 3, wherein the set value is-0.5.
5. The insect reflectivity vertical profile inversion algorithm of claim 3, wherein the set value is-0.5.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115049112A (en) * | 2022-05-27 | 2022-09-13 | 中国科学院空天信息创新研究院 | Method and device for predicting spatial distribution of migratory insects |
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| US9594162B1 (en) * | 2014-07-02 | 2017-03-14 | Rockwell Collins, Inc. | Avian hazard detection and classification using airborne weather radar system |
| CN109459751A (en) * | 2018-08-27 | 2019-03-12 | 北京理工大学 | A kind of biological information monitor method of migrating based on weather radar data |
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| US9594162B1 (en) * | 2014-07-02 | 2017-03-14 | Rockwell Collins, Inc. | Avian hazard detection and classification using airborne weather radar system |
| CN109459751A (en) * | 2018-08-27 | 2019-03-12 | 北京理工大学 | A kind of biological information monitor method of migrating based on weather radar data |
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| CHENG HU 等: "A Retrieval Method of Vertical Profiles of Reflectivity for Migratory Animals Using Weather Radar", 《IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING》 * |
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| CN115049112A (en) * | 2022-05-27 | 2022-09-13 | 中国科学院空天信息创新研究院 | Method and device for predicting spatial distribution of migratory insects |
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