We address the challenge of tracking an unknown number of targets in strong
clutter environments using measurements from a radar sensor. Leveraging the
range-Doppler spectra information, we identify the measurement classes, which
serve as additional information to enhance clutter rejection and data
association, thus bolstering the robustness of target tracking. We first
introduce a novel neural enhanced message passing approach, where the beliefs
obtained by the unified message passing are fed into the neural network as
additional information. The output beliefs are then utilized to refine the
original beliefs. Then, we propose a classification-aided robust multiple
target tracking algorithm, employing the neural enhanced message passing
technique. This algorithm is comprised of three modules: a message-passing
module, a neural network module, and a Dempster-Shafer module. The
message-passing module is used to represent the statistical model by the factor
graph and infers target kinematic states, visibility states, and data
associations based on the spatial measurement information. The neural network
module is employed to extract features from range-Doppler spectra and derive
beliefs on whether a measurement is target-generated or clutter-generated. The
Dempster-Shafer module is used to fuse the beliefs obtained from both the
factor graph and the neural network. As a result, our proposed algorithm adopts
a model-and-data-driven framework, effectively enhancing clutter suppression
and data association, leading to significant improvements in multiple target
tracking performance. We validate the effectiveness of our approach using both
simulated and real data scenarios, demonstrating its capability to handle
challenging tracking scenarios in practical radar applications.

通过利用范围 - Doppler 光谱信息，我们提出了一种在强杂波环境中使用雷达传感器测量来跟踪未知数量目标的挑战。我们采用了一种新颖的神经增强消息传递方法，将得到的信念用作增加杂波拒绝和数据关联的附加信息，从而增强了目标跟踪的鲁棒性。我们还提出了一种基于分类的鲁棒多目标跟踪算法，采用神经增强消息传递技术。通过模块化方法，该算法有效地提升了杂波抑制和数据关联的性能，从而在多目标跟踪中取得显著改进。