The numerous applications of unmanned aerial vehicles (UAVs) have caused them to receive growing interest worldwide. However, misused UAVs have caused significant public safety and privacy concerns. Therefore, developing an anti-UAV surveillance system is of paramount importance. In this paper, we propose a radar-based UAV swarm surveillance approach. A frequency estimation-based approach is used to obtain a coarse wave path difference estimate. Then, by combining the coarse estimate from the first stage with an estimate from the phase comparison method, a fine and unambiguous estimate of the wave path difference is derived. Through the solution of a system of nonlinear equations regarding the measured range and wave path difference, the proposed method is able to provide an unambiguous three-dimensional (3D) position estimate for a given UAV swarm. Once such 3D positions have been collected throughout an observation period, system tracks of the UAV swarm can be formed. With these tracks, a target object map (TOM) can be generated and handed over to an interceptor to counter the UAV swarm. Theoretical analyses of the accuracy of the estimation results and the restrictions of the proposed method are presented in detail. Several simulations are also carried out to evaluate the performance of the proposed method.

For maneuvering targets, their motion during long observing time will deteriorate the integration results and degrade the performance of range and motion parameters estimation. To solve this problem, a computationally efficient method based on the time-reversal (TR) process and maximum likelihood (ML) principle, i.e., TR-MLE is proposed. The proposed method decouples the joint parameter estimation problem into two simpler problems, which not only increases the efficiency but also improves the estimation performance in low signalnoise-ratio (SNR). Furthermore, a fast method using Chirp- Z transform (CZT) and Newton's method is developed for a more efficient implementation. The theoretical analysis of the noise properties after the TR process is carried out. Then the corresponding Cramér-Rao lower bound (CRLB) which can evaluate the performance loss introduced by the TR process is discussed in detail. Simulated data and real data are used to assess the performance of the proposed method.

To avoid the complicated motion compensation in interferometric inverse synthetic aperture (InISAR) and achieve realtime three-dimensional (3D) imaging, a novel approach for 3D imaging of the target only using a single echo is presented. This method is based on an isolated scatterer model assumption, thus the scatterers in the beam can be extracted individually. The radial range of each scatterer is estimated by the maximal likelihood estimation. Then, the horizontal and vertical wave path difference is derived by using the phase comparison technology for each scatterer, respectively. Finally, by utilizing the relationship among the 3D coordinates, the radial range, the horizontal and vertical wave path difference, the 3D image of the target can be reconstructed. The reconstructed image is free from the limitation in InISAR that the image plane depends on the target's own motions and on its relative position with respect to the radar. Furthermore, a phase ambiguity resolution method is adopted to ensure the success of the 3D imaging when phase ambiguity occurs. It can be noted that the proposed phase ambiguity resolution method only uses one antenna pair and does not require a priori knowledge, whereas the existing phase ambiguity methods may require two or more antenna pairs or a priori knowledge for phase unwarping. To evaluate the performance of the proposed method, the theoretical analyses on estimation accuracy are presented and the simulations in various scenarios are also carried out.

Range-velocity (RV) map (also called the range-Doppler map) is used in radar to detect targets and estimate the range and velocity of the targets. The conventional range-Doppler processing has its limits in attaining a high-resolution RV map with few samples. Moreover, the conventional range-Doppler processing may cause severe range migration problems and bias in the range estimation. In this paper, we propose to attain a high-resolution and accurate RV map with the sparse Bayesian learning (SBL) framework. Unlike the existing works that use the discrete Fourier transform to build the dictionary for sparse representation, we build a dictionary from a model of the wide-band frequency-modulated continuous-wave (FMCW) radar to avoid the model mismatch problem. This dictionary allows for the effective decomposition of the signal and the full utilization of the structure of the signal. In addition, based on the fast marginal likelihood maximization, we also propose a fast method to reduce the computational burden. Simulations show that the proposed method and the proposed fast method are superior in terms of resolution, accuracy, and maximum unambiguous velocity compared with the existing methods.

Modern radar systems commonly utilize monopulse angle estimation techniques for target angle estimation, with the phase comparison method being one of the most widely adopted approaches. While the phase comparison method achieves high estimation precision, it is highly susceptible to noise and exhibits a suboptimal performance under low Signal-to-Noise Ratio (SNR) conditions, leading to a high SNR threshold. Moreover, conventional monopulse angle estimation methods provide limited target information, as a single measurement cannot reveal the target’s motion direction. To address these shortcomings, a novel approach based on the phase comparison method is proposed in this study, with the variation in the wave path difference modeled as a first-order motion model. By accumulating the conjugate-multiplied signals over multiple time steps, the Wave Path Difference Rate of Change Ambiguity Function (WPD-ROC AF) is constructed. A fast algorithm employing the 2D Chirp-Z Transform (2D-CZT) is proposed, enabling multi-pulse angle estimation through the identification of frequency and phase values corresponding to spectral peaks. Simulation results validate that the proposed method outperforms traditional monopulse angle estimation techniques under low-SNR conditions and effectively suppresses static clutter interference. Furthermore, the sign of the WPD-ROC AF is shown to be correlated with the target’s motion direction, providing practical utility for determining the direction of movement in remote sensing scenarios.