Qi Jiang

Tracking multiple aerial targets within a group is a challenging task in the radar field. Group targets are usually closely spaced and have similar features, which leads to uncertain associations in target tracking. Additionally, targets within a group interact with each other, making it difficult to establish an accurate prior motion model. These factors limit the conventional model-driven tracking methods, resulting in false associations and tracking errors. Therefore, this paper proposes a group target tracking method based on spatial-temporal attention graph networks. First, a generalized graph representation is constructed to model the various interactions among group targets. Then, a two-stage network framework is introduced to learn the spatial and temporal relationships, thereby achieving robust tracking. Specifically, a message passing network with spatial-temporal attention is designed for data association of closely spaced targets, and a graph-aided filter state modification network is designed to correct the tracking error caused by mismatched motion models. Within the framework of multiple hypothesis tracking, data association and track filtering are optimized in a data-driven way. Simulation and experiment results demonstrate that the method outperforms the competing methods.

Ensuring constant false alarm rate (CFAR) detection in complex non-Gaussian heterogeneous backgrounds poses a major challenge in low-altitude target monitoring. Heterogeneous Gamma clutter is a typical non-Gaussian heterogeneous background. Its heavy-tailed characteristics, together with masking effects from strong neighboring scatter interference, cause inaccurate background level estimation in conventional detectors. As a result, the detection performance degrades significantly. To overcome above detection difficulties, this paper proposes a Gamma-distributed background Bayesian interference control CFAR (GBIC-CFAR) detector. A standard CFAR detection procedure is developed using Bayesian methods, resulting in a Bayesian predictive inference model for Gamma clutter. Meanwhile, by constructing a reasonable predictive inference distribution to control interference, effective compensation for interference is achieved. Additionally, to address errors caused by inaccurate prior knowledge of interference in the Bayesian detector, a method for selecting interference prior probabilities based on discrete truncated Gaussian function is proposed. By adjusting the truncation threshold, the prior probabilities are optimized, improving the robustness of the proposed detector. Finally, the effectiveness of the proposed algorithm is validated through simulations and real data.

Estimating the 3-D alignment of migratory insects is essential for understanding their 3-D directional behavior. The existing approach reconstructs 3-D alignment using azimuthal angle measurements from dual-view entomological radars. However, the measurement errors often propagate into the reconstructed 3-D alignment, causing significant inaccuracies, especially under low signal-to-noise ratio (SNR) conditions. To overcome this problem, this letter proposes a new method based on a multiview, multifrequency, and full-polarization entomological radar system. The method directly estimates insect 3-D alignment by integrating polarization scattering matrices (SMs) from multiple views and frequencies, leveraging the assumption of scattering symmetry in insect bodies. For symmetric targets, the off-diagonal SM elements vanish when the polarization direction aligns with the symmetry plane. Based on this theory, an optimization problem is formulated to estimate 3-D alignment by minimizing the sum of the powers of off-diagonal elements across multiview and multifrequency SMs. Both simulations and field experiments demonstrate that the proposed method achieves substantially higher accuracy than the traditional method.

Accurate estimation of targets' number is a crucial prerequisite for observing group target. Traditional methods estimate target number by resolving targets in the range/angle domain, but they become ineffective when targets are densely distributed or the system resolution is limited. While some super-resolution methods can estimate the number of densely distributed targets, they require the echo signals to remain stationary and suffer performance degradation when the group is highly dynamic and individuals are fast fluctuating. This paper proposes a target number estimation method using multi-angle incoherent observations. Firstly, this paper analyzes the fast fluctuation characteristics of echoes from a highly dynamic and densely distributed group target, and demonstrates the incoherence of echoes at different observation frames. Then the group target's multi-angle observation matrix and the corresponding covariance matrix is constructed. Finally, the relationship between the target number and the eigenvalue distribution of the observation covariance matrix is formulated, and the number is accurately estimated through the principal eigenvalues segmentation. Furthermore, by modifying the cost function of the principal eigenvalues segmentation method based on the Minimum Description Length criterion, this paper improves the “underestimation” issue of principal eigenvalues segmentation under low signal-to-noise ratio conditions. The proposed method can achieve the number estimation of a highly dynamic and densely distributed group target using only a single sensor with limited resolution. Simulation and experimental results demonstrate that the proposed method achieves a correct estimation rate of over $80 \%$ when the target spacing is less than the range resolution.

The robust tracking of group targets, such as bird flocks and UAV swarms, is crucial for situational awareness and individual tracking. The main characteristics of group targets are their complex spatial structures and highly dynamic behavior. Traditional single ellipsoidal tracking methods cannot accurately describe such complex shapes. On the other hand, existing multi-ellipsoidal tracking methods assume mutual independence between sub-objects. This assumption can lead to inaccurate responses to changes in group states and false associations between measurements and sub-objects. This paper proposes an adaptive multivariate approach for dynamic group target tracking. A multivariate model is first proposed to model sub-object states precisely. Both collective and internal structural relations are incorporated to adapt to the group's complicated dynamics. A variational Bayesian approach is derived to jointly estimate the multivariate state and the association weights of measurements. Additionally, an adaptive sub-object partitioning algorithm is proposed to analyze various group structures automatically. The effectiveness of the proposed method is verified by simulation and experiment results. The results demonstrate that our method outperforms state-of-the-art methods in tracking accuracy and robustness, especially in dynamic scenarios.

Tracking the complex shapes of group targets, which provides essential information for situational awareness, is a critical task in group target observation. Traditional tracking methods represent group shapes using a fixed number of elliptical sub-objects and model the shape evolution with one simple dynamic model. However, in practice, group shapes change rapidly due to swarm intelligence, and the prior evolution model is often unknown. These factors degrade shape estimation accuracy, leading to poor tracking performance. In this paper, we propose a robust multiple-model tracking approach to accurately track group targets with complex and dynamic shapes. This approach treats the dynamic changes in group shapes as special maneuvers in group structures. First, a group structure maneuver (GSM) model, based on a multi-ellipsoidal representation, is introduced to describe the dynamic behavior of group shapes. Then, a prediction method employing multiple GSM models is designed to capture the dynamic evolution of group shapes and enhance the adaptability to rapid shape changes. Furthermore, to achieve hybrid estimation across different GSM models, a hierarchical fusion strategy is developed. Specifically, a group structure combination method is derived and integrated into multiple parallel estimators to jointly update the number and states of the sub-objects. The outputs of these estimators are then fused using the weighted Kullback-Leibler Average (KLA) method to obtain a high-accuracy overall estimation. Finally, the superiorities of the proposed method, such as early maneuver detection and high tracking accuracy, are validated through simulation and experimental results.

Detectors based on matrix information geometry have been developed recently and demonstrated advantages against conventional methods for detection of targets within nonhomogeneous clutter backgrounds. However, existing methods employing geometric measures to distinguish target and clutter lack sufficient consideration of statistical distribution of covariance matrices on manifold. In this paper, a detector is proposed utilizing the statistical characteristic of covariance matrices on Riemannian manifold, which is modeled by Riemannian Gaussian distribution. A second-order detection problem is formulated utilizing sample covariance matrices. Statistical characteristic of sample covariance matrices on the symmetric positive-definite (SPD) matrix manifold is analyzed to address the detection problem. It is verified that the distribution of sample covariance matrix of multivariate Gaussian observations can be well approximated by Riemannian Gaussian distribution when the number of samples is large. Then the likelihood ratio test (LRT) detector based on Riemannian Gaussian distribution is derived according to the Neyman-Pearson principle. In practical applications, the unknown parameter of clutter distribution in the LRT detector is substituted by the geometric mean of samples in reference cells, which is the maximum likelihood estimate of the mean of Riemannian Gaussian distribution. The above results and the theoretical performance of proposed detector have been validated through numerical simulations. Experiments based on real weather clutter, ground clutter and sea clutter demonstrate that the proposed detector outperform conventional detectors and existing detectors using geometric metrics.

Group targets such as birds and UAVs are the research hotspot in the radar field recently. Group target reconstruction that estimates the positions of individual targets becomes one of the core research requirements. But group targets are usually closely spaced with similar velocity and it is hard to distinguish multiple targets, resulting in angular glint. Meanwhile, they usually have weak echo and fly in a low altitude, which leads to lots of missed detection and false alarms. These characters increase the difficulty of group target reconstruction. Compared with a single radar, multiradar system with complementary multi-view observation can improve the accuracy and completeness of reconstruction. However, a large number of missed detection and false alarms make it difficult to correctly associate multisensor measurements. Moreover, large sensor bias further aggravate the difficulty. This paper proposed a topology feature aided joint association and registration algorithm to simultaneously acquire association and bias estimation results. The topology feature is applied in the association step to improve association correctness. A nonlinear least median of squares estimator is proposed in the registration step to improve the accuracy and robustness of bias estimation. Multi-frame information is used to further improve the association and registration performance. Simulation and experiment results show the better bias estimation and association performance of the proposed method compared with existing algorithms, and better reconstruction performance compared with a single radar.

Tracking individuals within a group is one of the major tasks of group target observation. Tracking radar must feature high frame rate and high range-angular resolution to achieve the stable multitarget tracking performance. However, two major problems arise from this scenario. First, the narrow beam of tracking radar does not allow the complete observation of group target, causing the fluctuation of target number as the radar-target geometry changes; second, false alarms may be target-dependent and distributed around the targets, which is contrary to the traditional spatially uniform clutter model. This paper proposes a Gaussian mixture probability hypothesis density filter for multitarget tracking using a collaborative radar system. The system consists of one scanning radar and one tracking radar. The former outputs the group's collective states (centroid, extension, etc.), which are used as the priors for the tracking radar. The tracking radar is responsible for the multitarget tracking. The density of target birth and death are set according to the priors. The update equation of the probability hypothesis density in target-dependent false alarms is derived and simplified to meet the practical application requirements. Finally, the effectiveness of the proposed filter is verified by the simulation and experimental results.