面向智能交通的雷达通信融合方法与优化设计

Intelligent transportation application requiring both communication links to other vehicles and active environment sensing functions. All vehicles on the road could interact in a cooperative radar sensor network, providing unique safety features and intelligent traffic routing. To reduce the cost of vehicular networking node and realize miniaturization, the main problem is how to integrate radar and communication information on a transceiver platform. The big challenge of this problem lies in the following three aspects: optimizing the method of radar-communication integration, refining the theory of target detection and designing the scheme of environmental perception. To solving the three key issues, this project will explore the following research issues: (1)Approaches to the design of intelligent waveforms, that are suitable for simultaneously performing both data transmission and radar sensing, are proposed. The approach is based on space time/frequency code waveforms utilized in wireless communications. The composite signal is separated and processed by using the software radio technique. Based on the compressive sensing (CS) theory, the composite signal processing is formulated as a sparse signal recovery problem and then the radar-communication integration system with low computational complexity is proposed. (2)The target detection theory is refined according to the special requirements for false negative in intelligent transportation application scenario. (3)Based on the optimization theory, target ranging，tachometry and tracking with Doppler measurements under restrained conditions of sending power and the transmission rate are proposed. These methods have high resolution performance and low computational complexity.

智能交通要求路边设施、车载设备具有实时的交互式无线通信和环境感知能力，在车联网中实现对交通环境高效的、鲁棒的感知，从而提高行驶安全性。为降低车联网络节点成本和实现小型化，其核心问题是如何将雷达与通信信息融合在一个收发平台上。要实现收发合一需解决三个核心问题：雷达通信融合方法的优化；目标检测理论的完善；环境感知优化方案的设计。因此本课题重点研究如下内容：（1）采取空时/频编码进行雷达通信复合信号优化设计，利用软件无线电技术进行信号分离，以低秩压缩感知理论为基础，将复合信号处理转化为稀疏向量重构问题，提出雷达通信融合系统的完整技术框架；（2）根据智能交通场景对漏警风险的特定要求修改内曼皮尔森准则，完善目标检测理论；（3）在传输码率和发射功率满足要求的约束条件下，基于多普勒测量，根据最优化理论思想，设计具有分辨力高、计算复杂度低的目标测速、位置估计和跟踪优化的新方案。

智能交通要求车辆个体具有实时的无线通信和动态环境感知能力，将雷达与通信信息融合在一个收发平台上，实现资源共享、动态可组和高利用率，同时减小平台上系统之间的电磁干扰和能源消耗，降低维护成本。要实现收发合一需解决三个核心问题：雷达通信融合方法的优化；目标检测理论的完善；环境感知优化方案的设计。在时频域和硬件一体化设计的基础上，围绕高效调制器和冲击滤波器组合成的系统架构，课题组提出了以下方法：（1）基于高效调制和空时频编码的雷达通信一体化信号设计方法以及一体化的完整系统框架，用于解决感知通信信息融合问题；（2）根据智能交通场景对漏警风险的特定要求修改内曼皮尔森准则，用于完善目标检测理论；（3）在传输码率和发射功率满足要求的约束条件下，根据最优化理论思想，设计分辨力高、计算复杂度低的目标测速和位置估计方法，用于提高动态环境感知的鲁棒性。依托本项目，项目组发表SCI学术论文13篇，包含国际重要期刊IEEE Transactions on Intelligent Transportation System，IEEE Systems Journal，Signal Processing，Mechanical Systems and Signal Processing，Journal of Optimization Theory and Applications等；申请发明专利1项。