For vertical-looking radars (VLRs), it is typically assumed that insects maintain a steady and approximately horizontal flight attitude as they pass through the radar beam, allowing for the estimation of insect morphological parameters by measuring the Radar Cross Section (RCS) for a ventral aspect. However, for tracking radars, which dynamically track and monitor insects, the attitude of the insect relative to the radar beam constantly changes. This dynamic change in attitude renders traditional insect morphological parameter estimation methods based on the ventral-aspect RCS ineffective. This paper proposes a novel method for estimating the morphological parameters of insects in non-horizontal flight attitudes. By determining the azimuth and pitch angles of the insect’s body axis relative to the radar antenna reference coordinate system and reconstructing the scattering matrix (SM) of the insect from non-horizontal attitudes to a horizontal attitude, we achieve the estimation of morphological parameters for insects in non-horizontal attitudes. The effectiveness of the proposed method is validated using a fully-polarimetric multi-angle observation dataset of 33 insects from 6 species measured in a microwave anechoic chamber. The mean relative errors in estimating the mass and length of the insects across 20 different observation angles are 20.06% and 12.85%. Compared to estimates obtained without making the correction, the accuracy of mass and body length estimation is improved by 19.80% and 13.79%, respectively.

Insects are categorized into two classes, “parallel (PA)” and “perpendicular (PE),” based on the relationship between radar cross section (RCS) values when the polarization direction is PA and PE to the insect body axis. Distinguishing between these classes is essential for accurately measuring insect orientation and morphological parameters. The current classification method relies on the relative phase sign of two eigenvalues from the insect’s polarization scattering matrix (SM). However, this method is susceptible to phase unwrapping errors and noise in practical applications. To enhance classification accuracy, the multifrequency characteristics of the relative amplitudes of SM eigenvalues, the polarization pattern shape, and insect class were analyzed using multifrequency SM data from both electromagnetic simulations and microwave anechoic chamber measurements. The analysis revealed that insect class can be distinguished based on the relative amplitude and phase of SM eigenvalues at two subfrequencies in the X-band. Building on this, a classification model for PA and PE insects was developed using the random forest (RF) algorithm, with the relative eigenvalues at 9.5 and 11.5 GHz as key features. Simulations demonstrated that the proposed method outperforms the traditional approach, particularly at low signal-to-noise ratios (SNRs). The model was then applied to a multifrequency, fully-polarimetric entomological radar, achieving 99.4% accuracy in distinguishing PA and PE insect classes based on body-axis alignment in the field. Finally, the reliability of the method was further validated through observations of freely flying migratory insects.

Insect morphological parameters, including mass and length, are crucial for species identification. Entomological radar can estimate morphological parameters by establishing mappings from insect radar cross section (RCS) estimators to them. Current high-accuracy methods relying on absolute RCS estimators are sensitive to echo intensity. When applied to radars without angle measurement capability, these methods may underestimate morphological parameters. This underestimation arises from the inability of such radars to compensate for reduced echo intensity caused by insects deviating from the beam center. A method using single-frequency echo intensity-independent estimators (EIIEs) was attempted; however, it could only estimate the mass of insects below 200 mg with limited accuracy. This article explores the use of multifrequency EIIEs (MFEIIEs) to enhance the estimation of insect mass and length. Based on the multifrequency scattering dataset for 159 insects measured in an anechoic chamber, the insect multifrequency scattering matrix (SM) was studied. The study revealed that four EIIEs, including the amplitude ratio and phase difference of SM eigenvalues, and two relative RCS features related to the shape of the insect polarization pattern, were correlated with insect mass and length with varied correlations with frequency. Subsequently, morphological parameter estimation was achieved by establishing the mappings from MFEIIEs to mass and length using a random forest algorithm. The presented dataset demonstrated that this method was suitable for insects below 1000 mg. Finally, the method’s effectiveness and robustness were demonstrated through field measurements on 160 insects, which yielded mean relative estimation errors of 21.82% for mass and 13.18% for length.

The accurate identification of migratory insect species is pivotal for effective pest forecasting and control strategies. Radar entomology continues to face challenges in insect identification, prompting the exploration of innovative solutions. The precision of conventional insect identification methodologies relying on morphological parameters or radar cross section (RCS) shape was inherently constrained. This study employed ensemble learning algorithms, utilizing multiple electromagnetic scattering parameters of insects as features for species classification, thereby enhancing radar’s capability to identify insects. Experiments of measuring insects using two unmanned aerial vehicles (UAVs) were carried out, aiming to establish an electromagnetic scattering database. Data were collected using a multifrequency fully polarimetric entomological radar, capturing echoes from nine major migratory pests in mainland China. Extracting the insect scattering matrix (SM) yielded a total of 22 electromagnetic scattering features categorized into four classes. Three ensemble learning algorithms were employed for classification: random forest (RF), extreme gradient boosting (XGBoost), and stacked generalization (SG). The results demonstrated that the model trained with the XGBoost algorithm consistently exhibited outstanding performance across various frequencies. In the X-band (9.5 GHz, typical operating frequency of entomological radar), the proposed XGBoost algorithm achieved an average identification accuracy of 87.50% for the nine pest species, which is approximately 13% higher than the traditional identification methods based on body size parameters. This study validated the feasibility of insect species identification based on electromagnetic scattering parameters, offering promising prospects for radar entomology to overcome challenges in insect identification.

Insect mass could be estimated by estimators calculated by the radar cross-section (RCS) measured by entomological radar, which is essential for the statistics of migratory biomass and classification of insects. In order to obtain the insect mass through radar, the RCS of various insects are measured in the Microwave Anechoic Chamber by a specially designed system containing dual-polarization antennas, which point at insects in the measurement process, and the mapping between RCS and the insect mass are constructed and applied to entomological radar. However, insects might deviate from beam center in practice, and the echo intensity will decrease. The decrease cannot be compensated for radar without angle measurement capability. Through the study of insect scattering matrix (SM), it is found that co-polarization estimators, such as co-polarization ratio and co-polarization phase, are echo intensity independent and correlated with insect mass. Therefore, a co-polarization estimators calculation method driven by model and data is elaborately designed, and an estimation method for insect mass is given on basis of the co-polarization estimators. Analyses present that the method is suitable for insects below 200mg, and the mean relative mass estimation error is lower than 30% in X band and 20% in Ku band. The effectiveness and robustness of this method is verified by measuring 39 individual insects with a Ku band fully polarimetric radar in field. The proposed methods provide a way to estimate insect mass for entomological radar without angle measurement capability.

The ability of insect orientation measurement of entomological radars supported the study of insect heading behaviors. However, the current entomological radars can only measure the two-dimension (2D) orientation that is the projection of the three-dimension (3D) orientation on the horizontal plane. The ability to measure the 3D orientation of insect will promote the study of the vertical and 3D heading behaviors of insect. In this study, an insect 3D orientation estimation method based on cooperative observation from two views of entomological radars is proposed. The expression of 3D orientation is deduced based on 2D orientation, azimuth and elevation measured with two radars at different stations through vector operation. The simulations are conducted to verify the effectiveness of the proposed method. The result shows that the estimation error of 3D orientation depends on that of the 2D orientation and the included angle between two lines of sight of two radars. A multi-aspect fully polarimetric rig is designed to measure insect echo signals from two aspects in a microwave anechoic chamber. The experiment data is used to further validate the effectiveness of the proposed method.

For insect radar observations, exploiting radar echoes from insects to accurately estimate size parameters such as body mass, length, and width of insects can help to identify insect species. At present, the commonly used method for estimating insect body size parameters in insect radar is to use the monotonic mapping relationship between insect RCS parameters of a single frequency (mainly 9.4 GHz) and body size, and obtain the empirical formula for body size estimation by polynomial fitting. However, the useful information used by the traditional methods is limited (1 to 2 features), and these retrieval methods are simple and with limited estimation accuracy. This paper proposed a feature-selection-based machine learning method for insect body size estimation, which could effectively improve the body size parameter estimation accuracy of insect radars. First of all, based on the published insect scattering dataset (9.4GHz, 366 specimens of 76 species), stepwise regression was used to select the optimal feature combinations for body size estimation, then three machine learning methods, Random Forest Regression (RFR), Support Vector Regression (SVR) and Multilayer Perceptron (MLP), were adopted to achieve estimation of insect body size. Among them, RFR has the best performance (mass 18.83%, length 11.37%, width 16.87%). Subsequently, based on the measured dataset of migratory insects (5532 specimens of 23 species), the influence of the estimation error of insect body size on the identification accuracy of migratory insect species was analyzed. When incorporating the estimation error of the feature-selection-based RFR method, the insect identification rate of 83.68% was reached.

The measurement of insect radar cross section (RCS) is a prerequisite for the studies such as the quantitative estimation of insect population density and the identification of insects using entomological radar. In this article, we established a multiband polarimetric RCS measurement system in the microwave anechoic chamber. The targets’ range profile at different frequencies can be obtained based on the step frequency continuous wave, and meanwhile the clutter elimination and polarimetric calibration were applied to reduce the measuring error. The multifrequency (X-/Ku-/Ka-bands) polarimetric RCSs of 169 insects belonging to 21 species were measured and reported, which is the first time to systematically present the multifrequency polarimetric RCSs of insects. The mass of all specimens range from 25.6 to 964 mg, and their ventral-aspect RCSs range from −57.47 to −32.17 dBsm at X-band, from −48.27 to −33.87 dBsm at Ku-band and from −69.76 to −36.40 dBsm at Ka-band. For small insects less than 300 mg, the HH polarization RCS increases rapidly with frequency at X-band and fluctuates with the frequency at Ku-band, while the VV polarization RCS increases monotonically with frequency at X- and Ku-band. For larger insects, the HH polarization RCS decreased slowly with frequency at X-band and fluctuates with the frequency at Ku-band, while the VV polarization RCS increases with the frequency, then reaches the maximum, finally fluctuates with the frequency. At Ka-band, the measured polarization RCS versus frequency curves are smooth and all show similar variation. The measurement results verify the effectiveness and accuracy of the established system.