The effective detection of unmanned aerial vehicle (UAV) targets is of great significance to guarantee national military security and social stability. In recent years, with the development of communication and control technology, the movement of UAVs has become increasingly flexible and complex, presenting diverse trajectory forms and different motion models in different phases. The Gaussian mixture probability hypothesis density filter incorporating the linear Gaussian jump Markov system approach (LGJMS-GMPHD) provides an efficient method for tracking multiple maneuvering targets, as applied to the switching of motions between a set of models in a Markovian chain. However, in practice, the motion model parameters of targets are generally unknown and the model switching is uncertain. When the preset filtering model parameters are mismatched, the tracking performance is dramatically degraded. In this paper, within the framework of the LGJMS-GMPHD filter, a deep-learning-based multiple model tracking method is proposed. First, an adaptive turn rate estimation network is designed to solve the filtering model mismatch caused by unknown turn rate parameters in coordinate turn models. Second, a filter state modification network is designed to solve the large tracking errors in the maneuvering phase caused by uncertain motion model switching. Finally, based on simulations of multiple maneuvering targets in cluttered environments and experimental field data verification, it can be concluded that the proposed method has strong adaptability to multiple maneuvering forms and can effectively improve the tracking performance of targets with complex maneuvering motion.

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.

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 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.

Tracking multiple targets within a group is a challenging task in the radar field, especially for a bird flock. Targets in a group are usually closely spaced and exhibit similar characteristics. Additionally, the tracking radar typically employs a narrow beam to achieve a high range–angular resolution, resulting in incomplete measurements within the limited beamwidth. These factors lead to false association and track fragmentation in target tracking. However, in addition to kinematic characteristics, birds exhibit temporally correlated micro-Doppler signatures because of their wingbeat behavior, which can be utilized in target tracking. Therefore, this paper proposes an adaptive TPHD tracking method using Doppler features. First, a Doppler temporal contrastive network is designed to learn the micro-Doppler representation for the association of birds. Then, the learned feature is fused with kinematic parameters, using XGBoost to guide the weight update in the filter. Moreover, adaptive mechanisms are incorporated into the TPHD filter to achieve stable tracking under incomplete measurements. Simulation and experimental results verified the effectiveness of the proposed method and showed better tracking performance than the competing method.