An Inversion Algorithm for the Body Weight Parameters of Insects in Oblique Flight Posture

Abstract

The present invention discloses an algorithm for inverting the body weight parameters of insects in an oblique flight posture, aiming to provide a method for estimating the body weight of insects in a non-level flight posture, so as to solve the problem of low inversion accuracy caused by the unified use of the level flight assumption in the inversion of the body size of migrating insects by vertical monitoring insect radars. In the case where the attitude angle of the insect measured by the radar is known, the method can compensate the mass (taking the logarithm)-inflection point RCS fitting curve of the level flight insect to obtain the oblique flight fitting curve at the current attitude angle of the insect; then obtain the multi-frequency point scattering cross-section area RCS data of the insect, and search for the RCS value σ corresponding to the highest peak inflection point of the multi-frequency RCS curve of the insect based on the least squares idea max ; Next, substitute σ max into the compensated oblique flight fitting curve, and the estimated value of the body weight of the insect at the current non-level flight attitude angle can be obtained.

Description

Technical Field

[0001] The present invention belongs to the technical field of insect radar, and particularly relates to an algorithm for inverting the body weight parameters of insects in a diagonal flight attitude. Background Art

[0002] Insect radar has experienced the development from scanning radar system to vertical radar system. Currently, the most widely used insect radar is the ZLC system vertical observation radar. The antenna of this type of radar points vertically upward, and the linearly polarized beam deviates from the vertical axis by a very small angle for rapid conical scanning, and can measure the RCS of the target in different linearly polarized directions, that is, the polarization pattern. Under the assumption of "insect level flight", the polarization pattern characteristics of the insect abdomen when the radar wave is incident vertically are studied, and it is found that the RCS is the largest when the body axis of small and medium-sized insects is parallel to the polarization direction, and the mapping relationship between the average RCS of the insect polarization pattern and the body weight. By extracting the direction of the maximum RCS and fitting the relationship between the average RCS and the body weight, the ZLC system vertical observation radar realizes the ability to invert the body weight of insects.

[0003] Currently, the inversion of insect biological parameters by insect radar is carried out under the assumption of "insect level flight". During the cruising stage of insect migration, level flight is the main mode; during the takeoff and landing stages, insects mainly fly diagonally. Based on the research on insect swarm migration by dual-polarization weather radar, it is found that even during the cruising stage, there are small-angle diagonal flights of insects. When insects fly diagonally, the vertically observed radar wave is no longer vertically incident, and the surface of the insect body facing the radar changes. Since the electromagnetic scattering of insects is sensitive to the attitude, this will obviously affect the echo characteristics of insects, and further affect the target biological parameters measured by insect radar. However, so far, there is no research on the influence of insect diagonal flight on the accuracy of the body axis orientation and body weight inversion methods proposed under the assumption of "insect level flight", nor is there any research on inverting the body attitude of insects based on insect radar. Summary of the Invention

[0004] In view of this, the present invention provides a method for estimating the body weight of insects in a non-level flight attitude.

[0005] The technical solution of the present invention is as follows:

[0006] An algorithm for inverting the body weight parameters of insects in a diagonal flight attitude, comprising the following steps:

[0007] Step 1: Use a multi-band radar system to obtain the radar scattering data of the to-be-measured insects at different frequency points, and further analyze to obtain the relationship between the frequency and the measurement data of the maximum RCS;

[0008] Step 2: According to the RCS-frequency measurement data curve graph obtained in Step 1, based on the search method in the sense of least squares, obtain the parameter estimation value of the peak inflection point in the RCS-frequency measurement data curve graph;

[0009] Step 3. Use the RCS corresponding to the peak inflection point in the RCS-frequency measurement data curve obtained in Step 2 and the pitch angle of the insect body axis relative to the plane of the radar antenna polarization coordinate system to invert the weight of the obliquely flying insect.

[0010] Beneficial effects:

[0011] The present invention proposes a method for estimating the weight of insects in non-level flight postures, which is expected to solve the problem of low inversion accuracy caused by the unified use of the level flight assumption for inverting the weight of migratory insects by vertical monitoring insect radars, and is of great significance for insect radar target recognition and classification. Description of the drawings

[0012] Figure 1 , Experimental scene for measuring the full polarization scattering matrix of insects with different body tilts;

[0013] Figure 2 , Multi-view antenna support structure;

[0014] Figure 3 , Weight error scatter plot;

[0015] Figure 4 , Average error of weight inversion at different oblique flight angles;

[0016] Figure 5 , Algorithm flow chart. Specific implementation manners

[0017] The following takes examples in conjunction with the drawings to describe the present invention in detail.

[0018] Step 1. Obtain multi-frequency RCS data of insects

[0019] Use a multi-band radar system to detect the insects to be measured and obtain the scattering signals at N frequency points. The number of frequency points in each band is not less than 10. For the nth frequency point, obtain the full polarization scattering matrix corresponding to the insects to be measured, which is expressed as:

[0020]

[0021] Among them, is the horizontal polarization component, is the vertical polarization component, and are the cross polarization components.

[0022] Substitute the full polarization scattering matrix into the following formula:

[0023]

[0024] Obtain the measurement data R of the maximum value of the RCS at the nth frequency pointn Traverse N frequency points to obtain the measurement data of the maximum RCS value corresponding to each frequency point. Take the frequency corresponding to each frequency point as the abscissa and the measurement data of the maximum RCS value as the ordinate to form a measurement data curve graph. Based on the measurement data curve graph, obtain the power function relationship between the frequency and the measurement data of the maximum RCS value:

[0025] R n =α(f - f0) β +σ max (3)

[0026] where f0 represents the abscissa of the peak inflection point in the curve graph, σ max is the ordinate of the peak inflection point, that is, the measurement data of the maximum RCS value, and α and β are parameters that determine the steepness of the power function curve.

[0027] Step 2. Obtain the parameter estimation values of the peak inflection point in the measurement data curve graph

[0028] Describes a search method based on the least squares sense for obtaining the parameter estimation values of the peak inflection point in the measurement data curve graph. The specific steps are as follows:

[0029] 1) Select the data within the peak inflection point window

[0030] From the measurement data curve graph generated in Step 1, determine the window containing the peak inflection point and extract the RCS measurement data within this window.

[0031] 2) Set the search range and interval

[0032] Set the search ranges and traversal search intervals for the parameters α, β, f0, and σ max

[0033] 3) Parameter search and error calculation

[0034] Within their respective search ranges, traverse a set of possible values of the parameters α, β, f0, and σ max and substitute them into the power function relationship to calculate the theoretical values at the corresponding frequency points. Then, subtract the calculated values from the measured values at the corresponding frequency points obtained in Step 1 to obtain the error values. Traverse all frequency points within the window to obtain the error values corresponding to each frequency point, and sum the squares of all error values:

[0035]

[0036] 4) Parameter optimization

[0037] According to the set search interval, continuously change the parameters α, β, f0, and σ max ​, calculate the sum of the squares of the corresponding error values. Select a set of parameters corresponding to the minimum sum of the squares of the error values as the estimation result of the first peak inflection point parameter:

[0038]

[0039] Step 3. Invert the insect body weight

[0040] Assume that the pitch angle of the insect body axis relative to the h-v plane of the radar antenna polarization coordinate system is θ (unit: °). At this time, the estimation formula of the insect body weight can be expressed as follows:

[0041]

[0042] Among them, is the RCS corresponding to the peak inflection point in the measurement data curve obtained by least squares estimation in Step 2. a and b are quantities related to the pitch angle θ. Through a large number of simulation calculations, the calculation formulas of a and b are as follows:

[0043] a = 4.444×10 -7 θ 2 -9.3333×10 -5 θ + 0.0555 (7)

[0044] b = 1.5267×10 -4 θ 2 -0.0041θ + 4.1247 (8)

[0045] Therefore, substitute the parameter estimation value of the peak inflection point in the RCS-frequency measurement data curve in Step 2 into the insect body weight inversion formula for the current oblique flight angle, and the estimated value of the weight of the measured oblique flying insect can be obtained.

[0046] Example:

[0047] To verify the aforementioned insect body weight inversion method in non-level flight postures, a full-polarization scattering matrix measurement experiment of insects with different body tilting angles was carried out in a microwave anechoic chamber using a multi-view full-polarization measurement device. The experimental scenario is as Figure 1 shown. The measurement device mainly includes a full-polarization measurement device and a multi-view antenna support structure. Among them, the full-polarization measurement device consists of a vector network analyzer and a dual-polarization antenna. The multi-view antenna support structure is as Figure 2As shown. The main body of the structure is a quarter - circular metal track, and other metal structures are mainly used to support the track. The radius of the large circle where the circular arc track is located is 2m. On the circular arc track, a pair of antenna bases (one transmitting and one receiving) are set at every 15° central angle for installing dual - polarized antennas. If the position directly below the center of the large circle where the circular arc track is located is taken as the reference angle (0°) of the central angle, then the positions of the antenna bases are 0°, 15°, …, 75°. In the figure, and respectively represent the radar line - of - sight direction, the H - polarization direction, and the V - polarization direction.

[0048] For 23 insects (such as Helicoverpa armigera, Spodoptera frugiperda, Agrotis ypsilon, etc.), based on the measured broadband RCS data in the X, Ku, and Ka bands, a method for estimating the weight of insects in non - level flight postures described in the present invention is adopted to complete the inversion of the weight parameters when the pitch angles are 0° / 15° / 30° / 45°. The inflection point information extracted from 23 insects at different pitch angles is shown in Table 1.

[0049] Table 1 Results of extracting inflection point parameters at each oblique - flight angle

[0050]

[0051] The weight inversion formulas when the pitch angles are 0° / 15° / 30° / 45° are shown in Table 2. Next, substitute the parameter estimation values of the above - mentioned peak inflection points into the weight inversion formulas corresponding to their respective pitch angles, and the corresponding weight estimation values can be obtained. The statistical chart of the weight inversion errors of all insects at different pitch angles is as Figure 3 shown, where the abscissa is the true weight of the insects and the ordinate is the relative error of the weight inversion.

[0052] Table 2 Weight inversion formulas at each oblique - flight angle

[0053] Angle Body weight (unit: mg / dBsm) 0 <![CDATA[m = 10 0.0548σ+4.0965 > 15 <![CDATA[m = 10 0.0563σ+4.1817 > 30 <![CDATA[m = 10 0.0510σ+4.0534 > 45 <![CDATA[m = 10 0.0529σ+4.2760 >

[0054] The statistical results of the average weight inversion errors of 23 insects at different oblique - flight angles are as Figure 4 shown, and the results are statistically shown in Table 3.

[0055] Table 3 Average weight inversion errors at different oblique - flight angles

[0056] Angle Average relative error 0 9.10％ 15 9.12％ 30 14.15％ 45 16.01％

[0057] Based on the above - mentioned measured data inversion results, the following conclusions can be obtained:

[0058] For insects of different sizes, the method proposed by the present invention can effectively invert the body weight of insects at different oblique flight angles, and the maximum inversion error does not exceed 1 / 5 of the true value; overall, the body weight estimation error increases with the increase of the body tilt angle.

[0059] In summary, the above are only the preferred embodiments of the present invention and are not intended to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An algorithm for inverting the weight parameters of insects in oblique flight posture, characterized in that: include: Step 1: Use a multi-band radar system to obtain radar scattering data of the insect to be tested at different frequencies, and further analyze the relationship between the frequency and the measured data of the maximum RCS value; Step 2: Based on the RCS-frequency measurement data curve obtained in step 1, a search method based on the least squares meaning is used to obtain a parameter estimation value of a peak inflection point in the RCS-frequency measurement data curve; Step 3: Invert the weight of the obliquely flying insect using the RCS corresponding to the peak inflection point in the RCS-frequency measurement data curve obtained in step 2 and the elevation angle of the insect body axis relative to the plane of the radar antenna polarization coordinate system; In the step 3, the pitch angle of the insect body axis relative to the plane of the polarization coordinate system of the radar antenna is used to obtain the inversion formula of the insect weight at the current oblique flight angle, and the RCS corresponding to the peak inflection point in the measurement data curve obtained by least squares estimation in step 2 is substituted into the inversion formula to obtain the estimated value of the weight of the measured insect; assuming that the pitch angle of the insect body axis relative to the hv plane of the polarization coordinate system of the radar antenna is θ, unit: °, the estimation formula of the insect weight is expressed as follows: in, is the RCS corresponding to the peak inflection point in the measured data curve obtained by least squares estimation in step 2. a and b are quantities related to the pitch angle θ. The calculation formulas of a and b are as follows: a=4.444×10 -7 i 2 -9.3333×10 -5 θ+0.0555 b=1.5267×10 -4 i 2 -0.0041θ+4.1247.

2. The inversion algorithm for weight parameters of insects in oblique flight posture according to claim 1 is characterized by: In the step 1, it is assumed that a multi-band radar system is used to detect the insect to be detected, and scattering signals of N frequency points are obtained, and the number of frequencies in each band should be as large as possible; for the nth frequency point, the full polarization scattering matrix corresponding to the insect to be detected is obtained, which is expressed as: in, is the horizontal polarization component, is the vertical polarization component, and is the cross-polarization component; Substitute the full polarization scattering matrix into the following equation: Get the measured data R of the maximum RCS value of the nth frequency point n ; Traverse N frequency points to obtain the measurement data of the RCS maximum value corresponding to each frequency point; take the frequency corresponding to each frequency point as the horizontal coordinate, and take the measurement data of the RCS maximum value as the vertical coordinate to form an RCS-frequency measurement data curve; Based on the RCS-frequency measurement data curve, obtain the power function relationship between the frequency and the measurement data of the RCS maximum value, which is expressed by the following formula: R n =α(f-f0) β +σ max Among them, f0 represents the horizontal coordinate of the peak inflection point in the curve graph, σ max is the ordinate of the peak inflection point, that is, the measured data of the maximum RCS value, while α and β are parameters that determine the steepness of the power function curve.

3. The inversion algorithm for weight parameters of insects in oblique flight posture according to claim 1 is characterized by: In the step 2, a search method based on the least squares meaning is used to obtain the parameter estimation value of the peak inflection point in the measurement data curve graph; the specific steps are as follows: 1) Select the data within the peak inflection point window From the measurement data curve graph generated in step 1, determine a window containing a peak inflection point, and extract RCS measurement data within the window; 2) Set the search range and interval Set the parameters α, β, f0 and σ max The search interval and traversal search interval; 3) Parameter search and error calculation In their respective search intervals, traverse the parameters α, β, f0 and σ max A set of possible values ​​of is substituted into the power function relationship to calculate the theoretical value at the corresponding frequency point; then, the calculated value is subtracted from the measured value of the corresponding frequency point obtained in step 1 to obtain the error value; all frequency points in the window are traversed to obtain the error value corresponding to each frequency point, and the squares of all error values ​​are summed: 4) Parameter Optimization According to the set search interval, the parameters α, β, f0 and σ are continuously changed in their respective search intervals. max , calculate the corresponding sum of squared error values; select a set of parameters that minimizes the sum of squared error values ​​as the estimation result of the first peak inflection point parameter:

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