In this paper,we investigate a distributed multi-input multi-output and orthogonal frequency division multiplexing(MIMO-OFDM) dual-functional radar-communication(DFRC) system,which enables simultaneous communication a...In this paper,we investigate a distributed multi-input multi-output and orthogonal frequency division multiplexing(MIMO-OFDM) dual-functional radar-communication(DFRC) system,which enables simultaneous communication and sensing in different subcarrier sets.To obtain the best tradeoff between communication and sensing performance,we first derive Cramer-Rao Bound(CRB) of targets in detection area,and then maximize the transmission rate by jointly optimizing the power/subcarriers allocation and the selection of radar receivers under the constraints of detection performance and total transmit power.To tackle the non-convex mixed integer programming problem,we decompose the original problem into a semidefinite programming(SDP) problem and a convex quadratic integer problem and solve them iteratively.The numerical results demonstrate the effectiveness of our proposed algorithm,as well as the performance improvement brought by optimizing radar receivers selection.展开更多
Neural network-based methods for intrapulse modulation recognition in radar signals have demonstrated significant improvements in classification accuracy.However,these approaches often rely on complex network structur...Neural network-based methods for intrapulse modulation recognition in radar signals have demonstrated significant improvements in classification accuracy.However,these approaches often rely on complex network structures,resulting in high computational resource requirements that limit their practical deployment in real-world settings.To address this issue,this paper proposes a bottleneck residual network with efficient soft-thresholding(BRN-EST)network,which integrates multiple lightweight design strategies and noise-reduction modules to maintain high recognition accuracy while significantly reducing computational complexity.Experimental results on the classical low-probability-of-intercept(LPI)radar signal dataset demonstrate that BRN-EST achieves comparable accuracy to state-of-the-art methods while reducing computational complexity by approximately 50%.展开更多
针对现有基于毫米波雷达的人体动作识别方法存在精度低、模型复杂度高等问题,文章提出一种基于多阶段特征协同处理的毫米波雷达人体动作识别(Human Action Recognition with Multi-Stage Feature Collaboration from Radar,HAR-MFC)方...针对现有基于毫米波雷达的人体动作识别方法存在精度低、模型复杂度高等问题,文章提出一种基于多阶段特征协同处理的毫米波雷达人体动作识别(Human Action Recognition with Multi-Stage Feature Collaboration from Radar,HAR-MFC)方法。该方法通过对雷达回波数据进行分析处理,提取每种动作的微多普勒图,并将其作为识别模型的分类特征。首先,特征提取模块负责提取微多普勒图中的动作特征并减少冗余计算;接着,特征融合模块实现局部细节特征与全局语义信息的有效关联;最后,特征优化模块加速模型的收敛过程。实验结果表明,该模型在自建数据集上的识别准确率达到97.66%,参数量仅为0.7599 M;在格拉斯哥公开数据集上的准确率为96.30%,这表明该模型具有较强的泛化能力。展开更多
基金supported by the National Key R&D Program of China (2023YFB2905605)the National Natural Science Foundation of China (62072229)。
文摘In this paper,we investigate a distributed multi-input multi-output and orthogonal frequency division multiplexing(MIMO-OFDM) dual-functional radar-communication(DFRC) system,which enables simultaneous communication and sensing in different subcarrier sets.To obtain the best tradeoff between communication and sensing performance,we first derive Cramer-Rao Bound(CRB) of targets in detection area,and then maximize the transmission rate by jointly optimizing the power/subcarriers allocation and the selection of radar receivers under the constraints of detection performance and total transmit power.To tackle the non-convex mixed integer programming problem,we decompose the original problem into a semidefinite programming(SDP) problem and a convex quadratic integer problem and solve them iteratively.The numerical results demonstrate the effectiveness of our proposed algorithm,as well as the performance improvement brought by optimizing radar receivers selection.
基金supported by the National Defense Pre-Research Foundation of China during the“14th Five-Year Plan”under Grant No.629010204.
文摘Neural network-based methods for intrapulse modulation recognition in radar signals have demonstrated significant improvements in classification accuracy.However,these approaches often rely on complex network structures,resulting in high computational resource requirements that limit their practical deployment in real-world settings.To address this issue,this paper proposes a bottleneck residual network with efficient soft-thresholding(BRN-EST)network,which integrates multiple lightweight design strategies and noise-reduction modules to maintain high recognition accuracy while significantly reducing computational complexity.Experimental results on the classical low-probability-of-intercept(LPI)radar signal dataset demonstrate that BRN-EST achieves comparable accuracy to state-of-the-art methods while reducing computational complexity by approximately 50%.
文摘针对现有基于毫米波雷达的人体动作识别方法存在精度低、模型复杂度高等问题,文章提出一种基于多阶段特征协同处理的毫米波雷达人体动作识别(Human Action Recognition with Multi-Stage Feature Collaboration from Radar,HAR-MFC)方法。该方法通过对雷达回波数据进行分析处理,提取每种动作的微多普勒图,并将其作为识别模型的分类特征。首先,特征提取模块负责提取微多普勒图中的动作特征并减少冗余计算;接着,特征融合模块实现局部细节特征与全局语义信息的有效关联;最后,特征优化模块加速模型的收敛过程。实验结果表明,该模型在自建数据集上的识别准确率达到97.66%,参数量仅为0.7599 M;在格拉斯哥公开数据集上的准确率为96.30%,这表明该模型具有较强的泛化能力。
