低复杂度多输入多输出雷达目标角度估计方法
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摘要
针对多重信号分类(multiple signal classification,MUSIC)算法通过谱峰搜索得到目标的角度估计的计算复杂度较高的问题,提出一种用于单基地多输入多输出(multiple input multiple output,MIMO)雷达目标角度估计的低复杂度求根MUSIC方法。首先通过降维变换降低接收数据的维度,然后在低维空间中根据导向矢量和噪声子空间的正交性,构造求根多项式,并通过求解该多项式的根来得到目标的波达方向(direction of arrival,DOA)估计。仿真实验表明,与MUSIC算法和RC-MUSIC算法相比,该算法具有更低的运算复杂度,且在低信噪比条件下具有更好的角度估计性能。
In order to solve the problem of high computational complexity of multi-signal classification(MUSIC)algorithm in obtaining target angle estimation by spectral peak search,a low-complexity root-finding MUSIC method for target angle estimation in monostatic multi-input multi-output(MIMO)radar is proposed.Firstly,the dimension of the received data is reduced by reduced dimensional transformation.Then,a MUSIC-based polynomial is constructed in the lower-dimensional space according to the orthogonality of the steering vector and the noise subspace,and the directions of arrival(DOAs)are estimated by solving the roots of the polynomial.Simulation results show that as compared with MUSIC algorithm and reduced complexity(RC-MUSIC)algorithm,the proposed algorithm has lower computational complexity and better angle estimation performance under low signal-to-noise ratio conditions.
In order to solve the problem of high computational complexity of multi-signal classification(MUSIC)algorithm in obtaining target angle estimation by spectral peak search,a low-complexity root-finding MUSIC method for target angle estimation in monostatic multi-input multi-output(MIMO)radar is proposed.Firstly,the dimension of the received data is reduced by reduced dimensional transformation.Then,a MUSIC-based polynomial is constructed in the lower-dimensional space according to the orthogonality of the steering vector and the noise subspace,and the directions of arrival(DOAs)are estimated by solving the roots of the polynomial.Simulation results show that as compared with MUSIC algorithm and reduced complexity(RC-MUSIC)algorithm,the proposed algorithm has lower computational complexity and better angle estimation performance under low signal-to-noise ratio conditions.
引文
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[9]ZHANG X F,XU L Y,XU L,et al.Direction of Departure(DOD)and Direction of Arrival(DOA)Estimation in MIMO Radar with Reduced-Dimension MU-SIC[J/OL].IEEE Communications Letters,2010,14(12):1161-1163[2017-05-02].https://doi.org/10.1109/LCOMM.2010.102610.101581.
[10]ZHAO Y,SHUI P,LIU H.Computationally Efficient DOA Estimation for MIMO Radar[C/OL]//International Congress on Image and Signal Processing.IEEE,2009:1-3[2017-05-12].http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=5304414.DOI:10.1109/CISP.2009.5304414.
[11]张秦,张林让,郑桂妹,等.任意阵列双基地MIMO雷达的半实值MUSIC目标DOD和DOA联合估计[J/OL].系统工程与电子技术,2016,38(3):532-538[2017-04-18].http://www.cnki.net/kcms/detail/11.2422.TN.20151215.1629.004.html.DOI:10.3969/j.issn.1001-506X.2016.03.09.
[12]ZHENG G M.Beamspace root-MUSIC algorithm for joint DOD DOA estimation in bistatic MIMO radar[J/OL].Wireless personal communications,2014,75(4):1879-1889[2017-06-10].https://link.springer.com/article/10.1007%2Fs11277-013-1442-z.DOI:10.1007/s11277-013-1442-z.
[2]LI J,JIANG D,ZHANG X.DOA estimation based on combined unitary ESPRIT for coprime MIMO radar[J/OL].IEEE Communications Letters,2017,21(1):96-99[2018-06-07].https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7593229.DOI:10.1109/LCOMM.2016.2618789.
[3]ZHANG Y,ZHANG G,WANG X H.Computationally efficient DOA estimation for monostatic MIMO radarbased on covariance matrix reconstruction[J/OL].Electronic Letters,2017,53(2):111-113.[2017-07-02].http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7827355.DOI:10.1049/el.2016.3818.
[4]SHI J,HU G P,ZONG B F,et al.DOA estimation using multipath echo power for MIMO radar in lowgrazing angle[J/OL]〗.IEEE Sensors Journal,2016,16(15):6087-6094[2017-07-01].http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7498629.DOI:10.1109/JSEN.2016.2580621.
[5]WANG X P,WANG W,LI X,et al.Sparsity-Aware DOA Estimation Scheme for Noncircular Source in MI-MO Radar[J/OL]〗.Sensors,2016,16(4):539[2017-06-25].http://www.mdpi.com/1424-8220/16/4/539.DOI:10.3390/s16040539.
[6]XIE N,GUO W B,ZHANG L,et al.High Resolution DOA Estimation Method in MIMO Radar[J/OL].Wireless Personal Communications,2016,91(1):219-236[2017-06-03].https://link.springer.com/content/pdf/10.1007%2Fs11277-016-3456-9.pdf.DOI:10.1007/s11277-016-3456-9.
[7]VALLET P,MESTRE X,LOUBATON P.Performance Analysis of an Improved MUSIC DoA Estimator[J/OL].IEEE Transactions on Signal Processing,2015,63(23):6407-6422[2017-04-05].http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7180404.DOI:10.1109/TSP.2015.2465302.
[8]姜晖,单洁,石敏.基于传播算子的二维ESPRIT算法[J/OL].西安邮电大学学报,2013,18(2):18-21.[2017-07-03].http://www.cnki.net/kcms/doi/10.13682/j.issn.2095-6533.2013.02.010.html.DOI:10.13682/j.issn.2095-6533.2013.02.010.
[9]ZHANG X F,XU L Y,XU L,et al.Direction of Departure(DOD)and Direction of Arrival(DOA)Estimation in MIMO Radar with Reduced-Dimension MU-SIC[J/OL].IEEE Communications Letters,2010,14(12):1161-1163[2017-05-02].https://doi.org/10.1109/LCOMM.2010.102610.101581.
[10]ZHAO Y,SHUI P,LIU H.Computationally Efficient DOA Estimation for MIMO Radar[C/OL]//International Congress on Image and Signal Processing.IEEE,2009:1-3[2017-05-12].http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=5304414.DOI:10.1109/CISP.2009.5304414.
[11]张秦,张林让,郑桂妹,等.任意阵列双基地MIMO雷达的半实值MUSIC目标DOD和DOA联合估计[J/OL].系统工程与电子技术,2016,38(3):532-538[2017-04-18].http://www.cnki.net/kcms/detail/11.2422.TN.20151215.1629.004.html.DOI:10.3969/j.issn.1001-506X.2016.03.09.
[12]ZHENG G M.Beamspace root-MUSIC algorithm for joint DOD DOA estimation in bistatic MIMO radar[J/OL].Wireless personal communications,2014,75(4):1879-1889[2017-06-10].https://link.springer.com/article/10.1007%2Fs11277-013-1442-z.DOI:10.1007/s11277-013-1442-z.