This work was supported by Special Fund for Research on National Major Research Instruments (Grant No. 31727901).
[1] Hu G, Lim K S, Horvitz N. Mass seasonal bioflows of high-flying insect migrants. Science, 2016, 354: 1584-1587 CrossRef ADS Google Scholar
[2] Smith A D, Reynolds D R, Riley J R. The use of vertical-looking radar to continuously monitor the insect fauna flying at altitude over southern England. Bull Entomol Res, 2000, 90: 265-277 CrossRef Google Scholar
[3] Hu C, Wang Y, Wang R. An improved radar detection and tracking method for small UAV under clutter environment. Sci China Inf Sci, 2019, 62: 29306 CrossRef Google Scholar
[4] Cui K, Hu C, Wang R. Deep-learning-based extraction of the animal migration patterns from weather radar images. Sci China Inf Sci, 2020, 63: 140304 CrossRef Google Scholar
[5] Zhou C, Wang R, Hu C. Equivalent point estimation for small target groups tracking based on maximum group likelihood estimation. Sci China Inf Sci, 2020, 63: 189302 CrossRef Google Scholar
[6] Drake V A, Reynolds D R. Radar Entomology: Observing Insect Flight and Migration. London: CABI, 2012. Google Scholar
[7] Chapman J W, Drake V A, Reynolds D R. Recent Insights from Radar Studies of Insect Flight. Annu Rev Entomol, 2011, 56: 337-356 CrossRef Google Scholar
[8] Long T, Hu C, Wang R. Entomological Radar Overview: System and Signal Processing. IEEE Aerosp Electron Syst Mag, 2020, 35: 20-32 CrossRef Google Scholar
[9] Drake A. AUTOMATICALLY OPERATING RADARS FOR MONITORING INSECT PEST MIGRATIONS. Insect Sci, 2002, 9: 27-39 CrossRef Google Scholar
[10] Drake V A, Hatty S, Symons C. Insect Monitoring Radar: Maximizing Performance and Utility. Remote Sens, 2020, 12: 596 CrossRef ADS Google Scholar
[11] Hu C, Li W, Wang R. Insect flight speed estimation analysis based on a full-polarization radar. Sci China Inf Sci, 2018, 61: 109306 CrossRef Google Scholar
[12] A method for routine monitoring of the aerial migration of insects by using a vertical-looking radar. Phil Trans R Soc Lond B, 1993, 340: 393-404 CrossRef Google Scholar
[13] Hu C, Kong S, Wang R. Identification of Migratory Insects from their Physical Features using a Decision-Tree Support Vector Machine and its Application to Radar Entomology. Sci Rep, 2018, 8: 5449 CrossRef ADS Google Scholar
[14] Drake V A. Distinguishing target classes in observations from vertically pointing entomological radars. Int J Remote Sens, 2016, 37: 3811-3835 CrossRef ADS Google Scholar
[15] Hao Z, Drake V A, Taylor J R. Insect Target Classes Discerned from Entomological Radar Data. Remote Sens, 2020, 12: 673 CrossRef ADS Google Scholar
[16] Drake V A, Chapman J W, Lim K S. Ventral-aspect radar cross sections and polarization patterns of insects at X band and their relation to size and form. Int J Remote Sens, 2017, 38: 5022-5044 CrossRef ADS Google Scholar
[17] Aldhous A C. An investigation of the polarization dependence of insect radar cross sections at constant aspect. Dissertation for Ph.D. Degree. Cranfield: Cranfield Institute of Technology, 1989. Google Scholar
[18] Chapman J W, Smith A D, Woiwod I P. Development of vertical-looking radar technology for monitoring insect migration. Comput Electron Agr, 2002, 35: 95-110 CrossRef Google Scholar
[19] Hobbs S E, Aldhous A C. Insect ventral radar cross-section polarisation dependence measurements for radar entomology. IEE Proc Radar Sonar Navig, 2006, 153: 502-508 CrossRef Google Scholar
[20] Hu C, Li W, Wang R. Insect Biological Parameter Estimation Based on the Invariant Target Parameters of the Scattering Matrix. IEEE Trans Geosci Remote Sens, 2019, 57: 6212-6225 CrossRef ADS Google Scholar
[21] Zrnic D S, Ryzhkov A V. Observations of insects and birds with a polarimetric radar. IEEE Trans Geosci Remote Sens, 1998, 36: 661-668 CrossRef ADS Google Scholar
[22] Melnikov V M, Istok M J, Westbrook J K. Asymmetric Radar Echo Patterns from Insects. J Atmos Ocean Tech, 2015, 32: 659-674 CrossRef ADS Google Scholar
[23] Hu C, Li W, Wang R. Accurate Insect Orientation Extraction Based on Polarization Scattering Matrix Estimation. IEEE Geosci Remote Sens Lett, 2017, 14: 1755-1759 CrossRef ADS Google Scholar
[24] Li W, Hu C, Wang R. Experimental validations of insect orientation extraction based on fully polarimetric measurement. 9 CrossRef Google Scholar
[25] Hu C, Li W D, Wang R, et al. Discrimination of Parallel and Perpendicular Insects Based on Relative Phase of Scattering Matrix Eigenvalues. IEEE T Geosci Remote, 2020. Google Scholar
[26] Mirkovic D, Stepanian P M, Kelly J F. Electromagnetic Model Reliably Predicts Radar Scattering Characteristics of Airborne Organisms. Sci Rep, 2016, 6: 35637 CrossRef ADS Google Scholar
Figure 1
(Color online) Relations of insect body widths to polarimetric RCS parameters: (a) $a_{0}$; (b) $a_{0}$ $\&$ $\alpha_{2}$; (c) $\nu$; (d) $d$. The dots represent the 159 insect specimens. The curves and the wireframe mesh represent the fits to body widths.
Figure 2
(Color online) The fitted insect body widths and $\lg{d}$ with different order polynomials: (a) 2nd-order; (b) 3rd-order;protect łinebreak (c) 4th-order; (d) 5th-order. The dots represent the 159 insect specimens. The curves are the fitting results.
Figure 3
(Color online) Relations of insect body lengths to polarimetric RCS parameters: (a) $a_{0}$; (b) $a_{0}$ $\&$ $\alpha_{2}$; (c) $\nu$; (d) $d$. The dots represent the 159 insect specimens. The curves and the wireframe mesh represent the fits to body lengths.
Figure 4
(Color online) Performances comparison of ${a_0}$, ${a_0}$ $\&$ ${\alpha~_2}$, $\nu~$ and $d$ methods for different body size samples: (a) body length estimation; (b) body width estimation.
Figure 5
(Color online) Relationships between MRE and SNR for (a) body length estimation; (b) body width estimation.
| Parameter | Fitting method | $R$tnotea) ($P$ valuetnoteb) | MRE (%) |
| $\nu$ | 3rd-order polynomial | 0.92 ($P<0.001$) | 13.25 |
| $d$ | 3rd-order polynomial | 0.90 ($P<0.001$) | 15.53 |
| $a_0$ | 3rd-order polynomial | 0.86 ($P<0.001$) | 18.16 |
| $a_0$ $\&$ $\alpha_2$ | Regression analysis | 0.92 ($P<0.001$) | 13.32 |
| Parameter | Fitting method | $R$ ($P$ value) | MRE (%) |
| $\nu$ | 3rd-order polynomial | 0.88 ($P<0.001$) | 13.53 |
| $d$ | 3rd-order polynomial | 0.88 ($P<0.001$) | 14.30 |
| $a_0$ | 3rd-order polynomial | 0.85 ($P<0.001$) | 16.07 |
| $a_0$ $\&$ $\alpha_2$ | Regression analysis | 0.87 ($P<0.001$) | 14.18 |
Download PDF
