Jichuan Zhang

Accurate multi-target filtering is crucial for low-altitude surveillance, where point and group targets often coexist. Poisson multi-Bernoulli mixture (PMBM) filters provide a unified Bayesian framework for the joint filtering of point and group targets under the assumptions of independent target dynamics and standard measurement models. However, in practical scenarios, group targets may generate new targets through member separation, while point targets may produce multiple measurements due to multi-beam sensing and micro-Doppler signatures. These phenomena violate the assumptions of existing PMBM filters and lead to degraded state estimation and target-type inference. To address these challenges, this paper proposes a modified PMBM filter with group target spawning and generalized measurement models for coexisting point and group targets. Specifically, a group-dependent spawning model is incorporated into the prediction step to enable timely detection of newly spawned targets. In addition, a generalized update function is developed to support point-target density updates with measurement sets of arbitrary cardinality, and a measurement-rate-based correction factor is introduced to improve target-type estimation under nonstandard measurement conditions. Furthermore, an efficient Poisson multi-Bernoulli approximation is derived to reduce computational complexity. The effectiveness of the proposed filter is verified through simulation and experimental results.

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.

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.

Bird flocks are typical group targets with various linear formations and high dynamics due to swarm intelligence. This leads to several problems in traditional multi-subobject group target tracking such as shape model mismatch and false correlations. This paper proposes a multi-subobject approach to dynamic formation target tracking. The algebraic graph theory is introduced to analyze the structure of formation targets, then measurements are clustered and combined into subobjects. Based on the existing random matrix approach, two additional filtering branches, including collective filtering and internal structure filtering, are introduced to achieve the robust tracking performance of formation targets. The KL divergence between the prediction and updated densities of the collective filtering is used to determine the change of formation shape. The correct association between the measurements and the subobjects is realized by the guidance of the internal structure filter. Finally, the effectiveness of the proposed method is verified by the simulation and experimental results.