This paper considers the non-line-of-sight(NLOS)vehicle localization problem by using millimeter-wave(MMW)automotive radar.Several preliminary attempts for NLOS vehicle detection are carried out and achieve good resul...This paper considers the non-line-of-sight(NLOS)vehicle localization problem by using millimeter-wave(MMW)automotive radar.Several preliminary attempts for NLOS vehicle detection are carried out and achieve good results.Firstly,an electromagnetic(EM)wave NLOS multipath propagation model for vehicle scene is established.Subsequently,with the help of available multipath echoes,a complete NLOS vehicle localiza-tion algorithm is proposed.Finally,simulation and experimental results validate the effectiveness of the established EM wave propagation model and the proposed NLOS vehicle localization algorithm.展开更多
With the development of automated driving vehicles, more and more vehicles will be fitted with more than one automotive radars, and the radar mutual interference will become very significant. Vehicle to everything (V2...With the development of automated driving vehicles, more and more vehicles will be fitted with more than one automotive radars, and the radar mutual interference will become very significant. Vehicle to everything (V2X) communication is a potential way for coordinating automotive radars and reduce the mutual interference. In this paper, we analyze the positional relation of the two radars that interfere with each other, and evaluate the mutual interference for different types of automotive radars based on Poisson point process (PPP). We also propose a centralized framework and the corresponding algorithm, which relies on V2X communication systems to allocate the spectrum resources for automotive radars to minimize the interference. The minimum spectrum resources required for zero-interference are analyzed for different cases. Simulation results validate the analysis and show that the proposed framework can achieve near-zero-interference with the minimum spectrum resources.展开更多
随着自动驾驶技术的发展,越来越多的汽车装载车载雷达,不同车辆的车载雷达之间会产生相互干扰,导致虚假目标的出现或基底噪声的增加,降低检测性能。针对汽车雷达之间的相互干扰问题,提出了一种基于注意力机制的深度复数卷积循环网络(Dee...随着自动驾驶技术的发展,越来越多的汽车装载车载雷达,不同车辆的车载雷达之间会产生相互干扰,导致虚假目标的出现或基底噪声的增加,降低检测性能。针对汽车雷达之间的相互干扰问题,提出了一种基于注意力机制的深度复数卷积循环网络(Deep Complex Convolution Recurrent Network with Attention,DCCRN-Attention),在频域实现干扰抑制。模型使用复数网络将信号的实部和虚部联合起来进行特征学习,能同时预测干扰抑制后目标的幅度和相位,并在跳跃连接中引入注意力机制聚焦于更重要的特征信息,抑制无关信息。实验结果表明,所提模型能有效抑制干扰、提高目标的信噪比(Signal to Noise Ratio,SNR),在评价指标上均优于基线方法。展开更多
为了应对车载毫米波雷达大斜视成像困难的问题,本文提出一种改进的极坐标格式算法(Polar Format Algorithm,PFA)对条带车载毫米波雷达斜视回波进行基于子孔径拼接的成像。该算法从条带数据与聚束数据的特点出发,将全孔径回波划分为子孔...为了应对车载毫米波雷达大斜视成像困难的问题,本文提出一种改进的极坐标格式算法(Polar Format Algorithm,PFA)对条带车载毫米波雷达斜视回波进行基于子孔径拼接的成像。该算法从条带数据与聚束数据的特点出发,将全孔径回波划分为子孔径,利用PFA处理子孔径数据。由于PFA存在波前弯曲误差,子图像不能直接拼接,因此对每一幅子图像进行几何失真校正。同时,以重叠子孔径的划分方式保证成像结果的高分辨率。最后截取子图像进行拼接得到条带SAR成像结果。所提方法解决了车载毫米波雷达大斜视情况下两维耦合严重的问题。通过对点目标和实测数据的仿真与分析验证了所提方法的有效性。展开更多
基金supported by the National Natural Science Foundation of China(62201510,62001091,61801435,61871080,61801435)the Initial Scientific Research Foundation of University of Science and Technology of China(Y030202059018051)+2 种基金Yangtze River Scholar Program,Sichuan Science and Technology Program(2019JDJQ0014)111 Project(B17008)Henan Provincial Department of Science and Technology Research Project(202102210315,212102210029,202102210-137).
文摘This paper considers the non-line-of-sight(NLOS)vehicle localization problem by using millimeter-wave(MMW)automotive radar.Several preliminary attempts for NLOS vehicle detection are carried out and achieve good results.Firstly,an electromagnetic(EM)wave NLOS multipath propagation model for vehicle scene is established.Subsequently,with the help of available multipath echoes,a complete NLOS vehicle localiza-tion algorithm is proposed.Finally,simulation and experimental results validate the effectiveness of the established EM wave propagation model and the proposed NLOS vehicle localization algorithm.
基金support by China Information Communication Technologies Group Corporationsupported in part by Chinese Ministry of Education-China Mobile Communication Corporation Research Fund under Grant MCM20170101the European Union’s Horizon 2020 research and innovation programme under the Marie Skldowska-Curie Grant Agreement No.793345
文摘With the development of automated driving vehicles, more and more vehicles will be fitted with more than one automotive radars, and the radar mutual interference will become very significant. Vehicle to everything (V2X) communication is a potential way for coordinating automotive radars and reduce the mutual interference. In this paper, we analyze the positional relation of the two radars that interfere with each other, and evaluate the mutual interference for different types of automotive radars based on Poisson point process (PPP). We also propose a centralized framework and the corresponding algorithm, which relies on V2X communication systems to allocate the spectrum resources for automotive radars to minimize the interference. The minimum spectrum resources required for zero-interference are analyzed for different cases. Simulation results validate the analysis and show that the proposed framework can achieve near-zero-interference with the minimum spectrum resources.
文摘随着自动驾驶技术的发展,越来越多的汽车装载车载雷达,不同车辆的车载雷达之间会产生相互干扰,导致虚假目标的出现或基底噪声的增加,降低检测性能。针对汽车雷达之间的相互干扰问题,提出了一种基于注意力机制的深度复数卷积循环网络(Deep Complex Convolution Recurrent Network with Attention,DCCRN-Attention),在频域实现干扰抑制。模型使用复数网络将信号的实部和虚部联合起来进行特征学习,能同时预测干扰抑制后目标的幅度和相位,并在跳跃连接中引入注意力机制聚焦于更重要的特征信息,抑制无关信息。实验结果表明,所提模型能有效抑制干扰、提高目标的信噪比(Signal to Noise Ratio,SNR),在评价指标上均优于基线方法。
文摘为了应对车载毫米波雷达大斜视成像困难的问题,本文提出一种改进的极坐标格式算法(Polar Format Algorithm,PFA)对条带车载毫米波雷达斜视回波进行基于子孔径拼接的成像。该算法从条带数据与聚束数据的特点出发,将全孔径回波划分为子孔径,利用PFA处理子孔径数据。由于PFA存在波前弯曲误差,子图像不能直接拼接,因此对每一幅子图像进行几何失真校正。同时,以重叠子孔径的划分方式保证成像结果的高分辨率。最后截取子图像进行拼接得到条带SAR成像结果。所提方法解决了车载毫米波雷达大斜视情况下两维耦合严重的问题。通过对点目标和实测数据的仿真与分析验证了所提方法的有效性。
