信号处理2025，Vol.41Issue(11)：1800-1813,14.DOI:10.12466/xhcl.2025.11.006
基于时空特征融合与注意力机制的多目标航迹预测算法
Multi-Target Trajectory Prediction Algorithm Based on Spatiotemporal Feature Fusion and Attention Mechanism
摘要
Abstract
In radar multi-target tracking,the robustness of the motion model directly affects overall tracking performance.As the tracking environment becomes increasingly complex,weakly constrained,non-cooperative target tracking is prone to challenges such as target occlusion,measurement loss,and unpredictable target motion paths.This paper proposes a re-gion proposal net-attention-convLSTM-transformer(RPN-AT-ConvLSTMT)spatiotemporal feature extraction and predic-tion network for constructing motion models of weakly constrained,non-cooperative targets.First,one-dimensional radar measurements are extended into a two-dimensional Probability Hypothesis Density(PHD)map using covariance and mean.This enables a probabilistic representation of the regions where targets may exist.To address the challenge of large search areas in multi-target tracking,preprocessing operations such as multi-scale feature transformation and key area anno-tation are applied to the image.These steps help identify local regions or search areas for tracking and reduce the network's focus on irrelevant regions.Furthermore,an improved multi-head attention mechanism based on the Region Proposal Net(RPN)is designed.This mechanism dynamically reconstructs the attention weight matrix using Gaussian distribution char-acteristics,target motion priors,and interaction relationships.It ensures a dynamic balance between global and local re-gions,enhancing the attention mechanism's focus on target-relevant areas.The parallel architecture of the Transformer en-coder enables deep fusion and efficient transmission of spatiotemporal features.Finally,a BP(Back Propagation)neural network is employed to extract the Gaussian distribution's mean and covariance from the predicted PHD map,completing the target state estimation.Ablation experiments verify the effectiveness of the proposed attention mechanism and spatio-temporal fusion module.Simulation results in multi-target tracking scenarios demonstrate that the proposed algorithm sig-nificantly improves tracking accuracy compared to the ConvLSTM method,particularly in cases of occlusion and measure-ment loss,offering a robust solution for weakly constrained,non-cooperative target tracking.关键词
雷达目标跟踪/概率假设密度图/卷积长短时记忆网络/时空序列特征Key words
radar target tracking/probability hypothesis density graph/convolutional long short-term memory/spatio-temporal sequence features分类
信息技术与安全科学引用本文复制引用
朱进,张向荣,刘希龙,谢家强,刘龙..基于时空特征融合与注意力机制的多目标航迹预测算法[J].信号处理,2025,41(11):1800-1813,14.基金项目
陕西省重点研发计划(2024CY-GJHX-14)The Key Research and Development Program of Shaanxi(2024CY-GJHX-14) （2024CY-GJHX-14）
