Vertical-looking radar (VLR) is a significant milestone in the development of insect radars with the capability of detecting the behavior of migratory insects and their biological parameters. In current VLRs, high-speed continuous sampling and long-time integration can barely be performed simultaneously, leading to a low detection probability for tiny insects (weight < 10 mg). Based on the large amount of data acquired by our developed high-range resolution insect radar, the insect echo signals and vertical motion characteristics are initially analyzed and demonstrate that the linear-motion mode is dominant in insect migration; also, the echo signal power of most insects follows the gamma distribution. Based on these characteristics, a long-time integration and detection method for detecting migratory insects, especially tiny targets from echo signals that often dip below the noise level, is proposed. The radial target velocity is also measured as one of the output parameters. The theoretical derivation and optimal choice of detection thresholds are also presented. Simulation and experimental results demonstrate that the proposed method exhibits better insect detection performance and effectively increases the detection range compared with conventional methods. In addition, the measured target velocity can be directly applied to current continuous-sampling VLRs for the ascent and descent rate analysis. Many typical insect migration phenomena have been detected effectively utilizing our developed VLR, and the measured ascent and descent rates of insects agree well with typical take-off, cruising, and landing behaviors. This is the first reported successful VLR application on take-off and landing behaviors of migratory tiny and dense insects.

Dynamic Programming Track Before Detect (DP-TBD) is widely applied in the radar detection for weak targets. The algorithm selects the most advantageous state in each stage to integrate the merit function and return the corresponding target status to the merit function that exceeds the detection threshold in the final stage. However, due to the recursive search strategy included in dynamic programming, the constant false alarm rate (CFAR) detection threshold is difficult to be quickly determined by numerical integration. Monte Carlo counting, extreme value theory or its extended algorithm are commonly used to determine the detection threshold of dynamic programming. However, these methods require many times of Monte Carlo simulation, which is time-consuming and difficult to apply to practical projects. In order to achieve fast and accurate acquisition of CFAR detection threshold, this paper proposes an efficient approximation algorithm to determine the DP-TBD CFAR threshold. The algorithm ap-proximates the computational structure of DP-TBD by structural analogy, and then uses the saddle-point approximation method and the numerical integration of the fitting formula to quickly determine the CFAR threshold. Numerical simulation experiments under various conditions and radar field experiments are carried out to verify the high accuracy of the approximate threshold determined by the proposed algorithm. In addition, the algorithm is further extended and can be applied in the noise background with varying power. The corresponding numerical simulation experiments prove the effectiveness of the extended algorithm.

The use of radar to monitor insect migration is of great significance for pest control and biological migration mechanism research. However, migrating insects usually have small radar-cross-section (RCS) and are accompanied by maneuvering. The current radar detection algorithms mainly have contradictions in detection performance and computational complexity. So it is difficult for traditional radar detection algorithms to detect them effectively. Hence, a novel coherent integration detection algorithm based on dynamic programming (DP) and fractional Fourier transforming (FrFT) is proposed. By combining the advantages of DP and FrFT, the proposed DP-FrFT method can quickly search the target track, and simultaneously perform parameters estimation and motion compensation, achieving high integration gain with relatively low time consumption. The high efficiency of the method is verified with a large number of simulations and sufficient field experiments.

The performance of conventional constant false alarm rate (CFAR) detectors may degrade in nonhomogeneous clutter environments, as accurately estimating the clutter distribution in the cell under test (CUT) using reference cells becomes challenging. In this paper, a CFAR detector based on clutter segmentation with spatial continuity constraints is proposed for target detection within nonhomogeneous weather clutter backgrounds. Analysis of real weather clutter collected by a high-resolution phased array radar indicates that the Rayleigh mixture model can precisely characterize the amplitude distribution of nonhomogeneous weather clutter in spatial domain. The hidden Markov random field (HMRF) model is employed to capture the spatial correlation of weather clutter. Based on this model, clutter segmentation is implemented using the variational expectation-maximization (VEM) algorithm, which provides the posterior class of clutter in each range cell and the estimated parameter of each class. Simulation results indicate that introducing the spatial continuity improves the accuracy of clutter segmentation and parameter estimation. A CFAR detection scheme is proposed, which utilizes the segmentation results to estimate the clutter distribution of the CUT and set the detection threshold accordingly. Experiments conducted using both simulated data and real weather clutter have demonstrated that the proposed method improves detection performance. The proposed method exhibit a maximum increase in detection probability of 8.97% compared to the best-performing benchmark method when the false alarm rate is $10^{-6}$ in real weather clutter.

The recognition of unmanned aerial vehicles (UAVs) has become a core aspect of low-altitude airspace management. Typically, the micro-Doppler characteristics of rotors are employed to accomplish the UAV recognition task. However, the performance of UAV recognition on the basis of micro-Doppler characteristics significantly degrades under low signal-to-noise ratio (SNR) conditions. This paper presents an intelligent small UAV recognition method based on micro-Doppler enhancement to separate UAVs from aerial targets under low SNR conditions. First, a dynamic echo signal model of a UAV rotating blade is established while considering the blade length and rotation frequency parameters. Second, a micro-Doppler characteristic enhancement transformation based on nonlinear body–micro coupling is proposed. In the enhancement algorithm, the phase difference is used to separate the Doppler component of the UAV body and rotor blade. Then, a twodimensional micro-Doppler feature map is created on the basis of the blade length and rotor frequency. Crucially, the microDoppler energy is increased when the values of the parameters match the true values. On the basis of a micro-Doppler feature map, a convolutional neural network (CNN) is constructed to achieve UAV recognition. The effectiveness of the proposed method is verified by experiments using both simulated data and real data. The radar echoes of 2 types of UAVs with S-band carrier frequencies are collected. Among them, DJI-Inspire UAV data with a high SNR of micro-Doppler characteristics are used to verify the correctness of the algorithm, and DJI-Phantom4 UAV data with a low SNR of micro-Doppler characteristics are used to test the performance of the algorithm. The proposed method significantly improves UAV recognition performance under low SNR conditions.

Entomological radars are important for scientific research of insect migration and early warning of migratory pests. However, insects are hard to detect because of their tiny size and highly maneuvering trajectory. Generalized Radon–Fourier transform (GRFT) has been proposed for effective weak maneuvering target detection by long-time coherent detection via jointly motion parameter search, but the heavy computational burden makes it impractical in real signal processing. Particle swarm optimization (PSO) has been used to achieve GRFT detection by fast heuristic parameter search, but it suffers from obvious detection probability loss and is only suitable for single target detection. In this paper, we convert the realization of GRFT into a multimodal optimization problem for insect multi-target detection. A novel niching method without radius parameter is proposed to detect unevenly distributed insect targets. Species reset and boundary constraint strategy are used to improve the detection performance. Simulation analyses of detection performance and computational cost are given to prove the effectiveness of the proposed method. Furthermore, real observation data acquired from a Ku-band entomological radar is used to test this method. The results show that it has better performance on detected target amount and track continuity in insect multi-target detection.