近年来,随着智能驾驶技术的不断发展,毫米波雷达作为汽车安全控制系统的核心组件受到了广泛关注。时分复用(TDM)多输入多输出(MIMO)调频连续波(FMCW)雷达因具有低硬件成本和高角度分辨率的优势而被广泛应用于汽车雷达,但TDMMIMO在实际...近年来,随着智能驾驶技术的不断发展,毫米波雷达作为汽车安全控制系统的核心组件受到了广泛关注。时分复用(TDM)多输入多输出(MIMO)调频连续波(FMCW)雷达因具有低硬件成本和高角度分辨率的优势而被广泛应用于汽车雷达,但TDMMIMO在实际应用中还存在对运动目标检测易出现速度模糊和角度模糊问题,导致检测误差变大,对于自动驾驶场景存在一定的安全隐患。为解决上述TDM MIMO FMCW雷达存在检测误差高的问题,提出了在多普勒模糊下的FMCW MIMO雷达目标参数估计方法,在不增加额外硬件开销的前提下,可确保算法在低复杂度下提升监测的时效性,解决了速度模糊和相位偏移问题;利用加Kaiser窗FFT波束形成方法对目标角度进行测量,从而得到更精准的目标信息。仿真和实验结果验证了所提方法的有效性。展开更多
This paper proposes low-cost yet high-accuracy direction of arrival(DOA)estimation for the automotive frequency-modulated continuous-wave(FMcW)radar.The existing subspace-based DOA estimation algorithms suffer fromeit...This paper proposes low-cost yet high-accuracy direction of arrival(DOA)estimation for the automotive frequency-modulated continuous-wave(FMcW)radar.The existing subspace-based DOA estimation algorithms suffer fromeither high computational costs or low accuracy.We aim to solve such contradictory relation between complexity and accuracy by using randomizedmatrix approximation.Specifically,we apply an easily-interpretablerandomized low-rank approximation to the covariance matrix(CM)and R∈C^(M×M)throughthresketch maties in the fom of R≈OBQ^(H).Here the approximately compute its subspaces.That is,we first approximate matrix Q∈C^(M×z)contains the orthonormal basis for the range of the sketchmatrik C∈C^(M×z)cwe whichis etrated fom R using randomized unifom counsampling and B∈C^(z×z)is a weight-matrix reducing the approximation error.Relying on such approximation,we are able to accelerate the subspacecomputation by the orders of the magnitude without compromising estimation accuracy.Furthermore,we drive a theoretical error bound for the suggested scheme to ensure the accuracy of the approximation.As validated by the simulation results,the DOA estimation accuracy of the proposed algorithm,eficient multiple signal classification(E-MUSIC)s high,closely tracks standardMUSIC,and outperforms the well-known algorithms with tremendouslyreduced time complexity.Thus,the devised method can realize high-resolutionreal-time target detection in the emerging multiple input and multiple output(MIMO)automotive radar systems.展开更多
文摘近年来,随着智能驾驶技术的不断发展,毫米波雷达作为汽车安全控制系统的核心组件受到了广泛关注。时分复用(TDM)多输入多输出(MIMO)调频连续波(FMCW)雷达因具有低硬件成本和高角度分辨率的优势而被广泛应用于汽车雷达,但TDMMIMO在实际应用中还存在对运动目标检测易出现速度模糊和角度模糊问题,导致检测误差变大,对于自动驾驶场景存在一定的安全隐患。为解决上述TDM MIMO FMCW雷达存在检测误差高的问题,提出了在多普勒模糊下的FMCW MIMO雷达目标参数估计方法,在不增加额外硬件开销的前提下,可确保算法在低复杂度下提升监测的时效性,解决了速度模糊和相位偏移问题;利用加Kaiser窗FFT波束形成方法对目标角度进行测量,从而得到更精准的目标信息。仿真和实验结果验证了所提方法的有效性。
文摘This paper proposes low-cost yet high-accuracy direction of arrival(DOA)estimation for the automotive frequency-modulated continuous-wave(FMcW)radar.The existing subspace-based DOA estimation algorithms suffer fromeither high computational costs or low accuracy.We aim to solve such contradictory relation between complexity and accuracy by using randomizedmatrix approximation.Specifically,we apply an easily-interpretablerandomized low-rank approximation to the covariance matrix(CM)and R∈C^(M×M)throughthresketch maties in the fom of R≈OBQ^(H).Here the approximately compute its subspaces.That is,we first approximate matrix Q∈C^(M×z)contains the orthonormal basis for the range of the sketchmatrik C∈C^(M×z)cwe whichis etrated fom R using randomized unifom counsampling and B∈C^(z×z)is a weight-matrix reducing the approximation error.Relying on such approximation,we are able to accelerate the subspacecomputation by the orders of the magnitude without compromising estimation accuracy.Furthermore,we drive a theoretical error bound for the suggested scheme to ensure the accuracy of the approximation.As validated by the simulation results,the DOA estimation accuracy of the proposed algorithm,eficient multiple signal classification(E-MUSIC)s high,closely tracks standardMUSIC,and outperforms the well-known algorithms with tremendouslyreduced time complexity.Thus,the devised method can realize high-resolutionreal-time target detection in the emerging multiple input and multiple output(MIMO)automotive radar systems.
