研究一种基于电磁矢量传感器阵列的多输入多输出(MIMO)雷达目标波离角(DOD)和波达角(DOA)的联合估计算法。提出了一种新型MIMO雷达系统,发射阵列采用常规阵元,而接收阵列采用电磁矢量传感器,在此基础上,算法首先利用矢量传感器的内在结构特点结合子空间旋转不变性质获得目标DOA预估计,随后采用最佳加权子空间拟合算法对DOD和DOA分别进行一维搜索,即可获得目标角度的高精度估计,并讨论了阵列结构对目标DOD和DOA精度的影响。与已有算法相比,该算法适用于不规则阵列结构,且具有无需配对和二维搜索等特点。仿真验证了算法的有效性,其估计精度与CRB界接近。
A joint direction of departure (DOD) and direction of arrival (DOA) estimation algorithm for multiple input and multiple output (MIMO) radar with electromagnetic vector sensors is proposed. A novel bistatic MIMO radar system with multiple transmitting sensors and multiple receiving electromagnetic vector sensors is introduced. The proposed algorithm uses the internal structure features of the vector sensors and the subspace rotation invariance to obtain the initial DOA, and then an optimal weighted subspace fitting algorithm is employed to implement a one-dimensional search to get the DOD and DOA estimations in succession. The impact of array geometry on the estimation accuracy of the DOD and DOA is discussed. The proposed algorithm is suitable for irregular array geometry, and requires no parameter pairing nor two-dimensional searching. Simulations show the effectiveness of the algorithm, and the estimation accuracy is close to that of the CRB.
[1] Li J, Stoica P. MIMO radar with colocated antennas[J]. IEEE Signal Processing Magazine, 2007, 24(5): 106-114.
[2] Xu L, Li J, Stoica P. Target detection and parameter estimation for MIMO radar systems[J]. IEEE Transactions on Aerospace and Electronic Systems, 2008, 44(3): 927-939.
[3] Li J, Stoica P, Xu L, et al. On parameter identifiability of MIMO radar[J]. IEEE Signal Processing Letters, 2007, 14(12): 968-971.
[4] Tajer A, Jajamovich G H, Wang X, et al. Optimal joint target detection and parameter estimation by MIMO radar[J]. IEEE Journal of Selected Topics in Signal Processing, 2010, 4(1): 127-145.
[5] Yan H D, Li J, Liao G S. Multitarget identification localization using bistatic MIMO radar systems[J]. EURASIP Journal on Advances in Signal Processing, 2008, 8(2): 1-8.
[6] Duofang C, Baixiao C, Guodong Q, et al. Angle estimation using ESPRIT in MIMO radar[J]. IET Electronics Letters, 2008, 44(12): 770-771.
[7] Zhang X, Xu D. Low-complexity ESPRIT-based DOA estimation for colocated MIMO radar using reduced-dimension transformation[J]. Electronics Letters, 2011, 47(4): 283-284.
[8] Bencheikh M L, Wang Y. Joint DOD-DOA estimation using combined ESPRIT-MUSIC approach in MIMO radar[J]. IET Electronics Letters, 2010, 46(15): 1081-1083.
[9] Zhang X, Xu L Y, Xu L. Direction of departure (DOD) and direction of arrival (DOA) estimation in MIMO radar with reduced-dimension MUSIC[J]. IEEE Communications Letters, 2010, 14(12): 1161-1163.
[10] Compton R T, Jr. The tripole antenna:an adaptive array with flail polarization flexibility[J]. IEEE Transactions on Antenna Propagation, 1981, 29(6): 944-952.
[11] Nehorai A, Kwok-Chiang H, Tan B T G. Minimum-noise-variance beamformer with an electromagnetic vector sensor[J]. IEEE Transactions on Signal Processing, 1999, 47(3): 601-618.
[12] Hyung-Rae P, Jian L, Hong W. Polarization-space-time domain generalized likelihood ratio detection of radar targets[J]. Signal Processing, 1995, 41(2): 153-164.
[13] Kainam T W, Zoltowski M D. Self-initiating MUSIC-based direction finding and polarization estimation in spatio-polarizational beamspace[J]. IEEE Transactions on Antennas and Propagation, 2000, 48(8): 1235-1245.
[14] Nehorai A, Paldi E. Vector-sensor array processing for electromagnetic source localization[J]. IEEE Transactions on Signal Processing, 1994, 42(2): 376-398.
[15] He J, Jiang S, Wang J, et al. Direction finding in spatially correlated noise fields with arbitrarily-spaced and far-separated subarrays at unknown locations[J]. IET Radar, Sonar & Navigation, 2009, 3(3): 278-284.
[16] Xu Y, Liu Z, Wong K T, et al. Virtual-manifold ambiguity in HOS-based direction-finding with electromagnetic vector-sensors[J]. IEEE Transactions on Aerospace and Electronic Systems, 2008, 44(4): 1291-1308.
CJA