多輸入多輸出雷達為一項新興研究且吸引著研究者的關注，多輸入多輸出雷達於傳送端使用多支天線同時發射事先定義好且彼此不相關的訊號，並利用多支天線於接收端接收。為了使不同傳送天線之發射訊號彼此正交，波形編碼是最常見的方式。對於它的分集增益，包含空間分集與波形分集，多輸入多輸出雷達能夠提升系統的自由度，在估測目標物參數上比起傳統的單輸入單輸出雷達有優勢。
對於雷達系統而言，目標物參數估測為其中一項重要的應用。一般來說，目標物由方向、訊號反射強度及速度三項參數來定義。此論文致力於如何準確地估測目標物的方向，並期望於低計算複雜度與高目標物容量的情況下，估測訊號的離開角度與經目標物反射之訊號入射角度。此論文提出一個編碼雷達訊號於傳送端，與兩種角度估測方法於接收端以提升角度估測之解析度，並增加目標物估測數目之容量。
此文章提出一個多輸入多輸出雷達系統的新架構。於多輸入多輸出雷達的傳送端，傳送一個被串聯序列展頻的雷達訊號。此訊號於每支傳送天線傳送時由一組外部序列與內部序列展頻。首先，外部序列假設為一長隨機序列，將雷達訊號展頻一次。對於每支傳送天線而言，此外部序列皆相同，假設雷達訊號經不同目標物反射的延遲時間夠大，此序列即可用來將接收端陣列天線所接收由不同目標物反射的訊號分開。相對於外部序列，內部序列為一組的正交序列，每支傳送天線將使用這組正交序列中的各自獨特的序列，將傳送訊號展頻第二次。使用傳送訊號彼此正交的特性，不同傳送天線所傳送的訊號可以於接收端被分開。此串聯序列的使用能夠增大目標物估測的容量，並提升訊號離開與接收方向的估測準確度。
在此多輸入多輸出雷達之接收端，我們提出兩個結合訊號發射與接收方向估測演算法。第一個方法為二階段基於子空間演算法，此方法的提出是為了避免空間譜的掃描，與額外發射方向和接收方向的配對步驟，以降低演算法的計算複雜度。訊號離開方向之估測首先透過旋轉不變技術，接者利用此訊號離開方向之導引向量將落在訊號子空間的特性，以估測訊號接收方向之導引向量。此方法相對於大步階的空間譜掃描方法能有較低的計算複雜度及較好的角度估測準確度。
第二個方法使用空時技術以估測訊號離開方向與到達方向。透過將導引向量與相對應的外部序列進行Kronecker乘法得出一個新的且擁有較大的秩的訊號空間。因此在外部序列長度大於傳送天線數量乘上接收天線數量的情況下，訊號空間之秩不再受限於傳送天線數量乘上接收天線數量，而是此乘積與外部序列長度中較大的值。此時空方法將在不影響角度估測準確度的情形下提升系統容量與角度的解析度。模擬結果指出所提方法在角度估測準確度與目標物個數估測上的有效性。

MIMO radar, an emerging technology, is attracting the attention of mamy researchers. The MIMO radar utilizes multiple antennas to transmit simultaneously various non-coherent predefined waveforms and to receive the reflected signals. Waveform coding is the most common means of generating orthogonal waveforms, which are emitted from different antennas. Owing to its diversity of gains, including spatial diversity and waveform diversity, MIMO radar has more degrees of freedom (DOF) than conventional SISO radars, so more target parameters can be effectively estimated.
The estimation of target parameters is important in a radar system. Generally, each target is parameterized by its direction, reflected complex amplitude, and velocity. This thesis concerns how to accurately estimate the direction parameters of a target, including the angle of departure (AOD) and the angle of arrival (AOA), with less computational complexity than the conventional spectral-searching methods and the ability to detect more targets. A coded radar signal at the transmitter end and two methods of estimating AOD-AOA at the receiver end are presented to improve the angular resolution of AOD-AOA estimation, and increase the the capacity of the number of targets estimation.
This work develops a new structure for a MIMO radar system. In the transmitter of the proposed MIMO radar system, a radar signal is spread by concatenated codes. A set of outer and inner sequences spreads the signal that is emitted from each antenna element. An outer sequence, which is assumed to be a long Pseudorandom Noise (PN) sequence, spreads this signal first. The outer sequences, which are the same for all transmitted antenna element, enable the receiver array to separate the signals that are reflected from different targets if the difference of the propagation delays between the targets are large enough. The inner sequences is a set of orthogonal sequences, each transmitted antenna element takes a unique code from the set to spread the transmitting signals again. The orthogonality of the transmitted signals that are transmitted from different transmitted antenna elements enables them to be separated in the receiver. The use of concatenated sequences increases the capacity for target detection and favors the angular resolution.
In the receiver of the MIMO radar system, two schemes are proposed for joint AOD-AOA estimation. The first scheme is a two-step subspace-based (TSSB) method, which is developed to reduce the computational complexity by eliminating the need for a spectral-searching process or an AOD-AOA pairing procedure. The AODs are firstly estimated using a signal subspace method that is based on the rotational invariance theory, and then the corresponding AOAs can be obtained because the steering vectors of the AOAs lay in the joint AOD-AOA signal subspace. This method has lower computational complexity and higher AOD-AOA estimation accuracy than the spectral-searching method with large searching grid size.
The second scheme utilizes a spatio-temporal (ST) technique to estimate AODs and AOAs. The Kronecker product of the AOD-AOD steering vector and the corresponding outer sequence yields a new signal space with a higher rank. Consequently, the rank of the signal space is no longer limited by the product of the number of the transmitting and the receiving elements, but by the higher of the length of the outer sequences when the length of the outer sequences exceeds the product of the number of transmitting and receiving elements. The ST method increases the capacity of the number of targets estimation and favors the angular resolution without accuracy degradation of AOD-AOA estimation. Simulation results yield the efficiency of the method in terms of estimation accuracy and detection capacity.

References
[1] M. I. Skolnik, Introduction to Radar Systems, 3rd ed: New York: Mc-Graw-Hil, 2001.
[2] S. Haykin, J. Litva, and T. J. Shepherd, Radar Array Processing: New York: Springer-Verlag, 1993.
[3] R. Buderi, The Invention That Changed the World: The Story of Radar From War to Peace: London, U.K.: Abacus, 1999.
[4] A. L. Swindlehurst and P. Stoica, "Maximum likelihood methods in radar array signal processing," Proc. IEEE, vol. 86, pp. 421-441, 1998.
[5] A. Farina, Antenna Based Signal Processing Techniques for Radar Systems: Norwood, MA: Artech House, 1992.
[6] V. S. Chernyak, Fundamentals of Multisite Radar Systems: New York: Gordon and Breach, 1998.
[7] J. Ward, "Space-time adaptive processing for airborne radar," IEE Colloquium on Space-Time Adaptive Processing, 1998, pp. 2/1-2/6.
[8] W. L. Melvin, "A STAP overview," IEEE Aerosp. Electron. Syst. Mag., vol. 19, pp. 19-35, 2004.
[9] R. Klemm, Principles of Space-Time Adaptive Radar: London: IEE Press, 2002.
[10] J. R. Guerci, Space-Time Adaptive Processing for Radar: Norwood, MA: Artech House, 2003.
[11] C. Y. Chen and P. P. Vaidyanathan, "MIMO radar spacetime adaptive processing and signal design," in Proc. MIMO Radar Signal, ed: John Wiley & Sons, Inc., 2009, pp. 235-281.
[12] E. Fishler, A. Haimovich, R. Blum, D. Chizhik, L. Cimini, and R. Valenzuela, "MIMO radar: an idea whose time has come," in Proc. IEEE Radar Conf., 2004, pp. 71-78.
[13] J. Litva and P. Stoica, MIMO Radar Signal Processing: Eds. New York: Wiley, 2008.
[14] A. M. Haimovich, R. S. Blum, and L. J. Cimini, "MIMO Radar with Widely Separated Antennas," IEEE Signal Processing Mag., vol. 25, pp. 116-129, 2008.
[15] J. Li and P. Stoica, "MIMO Radar with Colocated Antennas," IEEE Signal Processing Mag., vol. 24, pp. 106-114, 2007.
[16] J. M. Colin, "Phased array radars in France: present and future," in Proc. IEEE International Symposium on Phased Array Systems and Technology, 1996, pp. 458-462.
[17] D. W. Bliss and K. W. Forsythe, "Multiple-input multiple-output (MIMO) radar and imaging: degrees of freedom and resolution," in Proc. 37th Asilomar Conf. Signals, Systems and Computers, 2003, pp. 54-59.
[18] D. J. Rabideau and P. Parker, "Ubiquitous MIMO multifunction digital array radar," in Proc. 37th Asilomar Conf. Signals, Systems and Computers, 2003, pp. 1057-1064,.
[19] K. W. Forsythe, D. W. Bliss, and G. S. Fawcett, "Multiple-input multiple-output (MIMO) radar: performance issues," in Proc. 38th Asilomar Conf. Signals, Systems and Computers, 2004, pp. 310-315.
[20] J. Li, P. Stoica, L. Z. Xu, and W. Roberts, "On Parameter Identifiability of MIMO Radar," IEEE Signal Processing Letters, vol. 14, pp. 968-971, 2007.
[21] J. Li and P. Stoica, MIMO radar-diversity means superiority: 14th Annu. Workshop Adaptive Sensor Array Processing, MIT Lincoln Laboratory, Lexington, MA, 2006.
[22] F. C. Robey, S. Coutts, D. Weikle, J. C. McHarg, and K. Cuomo, "MIMO radar theory and experimental results," in Proc. 38th Asilomar Conf. Signals, Systems and Computers, 2004, pp. 300-304.
[23] I. Bekkerman and J. Tabrikian, "Target Detection and Localization Using MIMO Radars and Sonars," IEEE Trans. Signal Processing, vol. 54, pp. 3873-3883, 2006.
[24] I. Bekkerman and J. Tabrikian, "Spatially coded signal model for active arrays," in Proc. IEEE conf. Acoustics, Speech, and Signal Processing 2004, pp. ii-209-12.
[25] L. Z. Xu, J. Li, and P. Stoica, "Adaptive Techniques for MIMO Radar," in Proc. 4th IEEE Workshop Sensor Array and Multichannel, 2006, pp. 258-262.
[26] D. R. Fuhrmann and G. San Antonio, "Transmit beamforming for MIMO radar systems using partial signal correlation," in Proc. 38th Asilomar Conf. Signals, Systems and Computers, 2004, pp. 295-299.
[27] J. Li, P. Stoica, and Y. Xie, "On Probing Signal Design for MIMO Radar," in Proc. 40th Asilomar Conf. Signals, Systems and Computers, 2006, pp. 31-35.
[28] P. Stoica, J. Li, and Y. Xie, "On Probing Signal Design For MIMO Radar," IEEE Trans. Signal Processing, vol. 55, pp. 4151-4161, 2007.
[29] D. R. Fuhrmann and G. San Antonio, "Transmit beamforming for MIMO radar systems using signal cross-correlation," IEEE Trans. Aerosp. and Electron. Syst., vol. 44, pp. 171-186, 2008.
[30] K. W. Forsythe and D. W. Bliss, "Waveform Correlation and Optimization Issues for MIMO Radar," in Proc. 39th Asilomar Conf. Signals, Systems and Computers, 2005, pp. 1306-1310.
[31] L. Z. Xu, J. Li, P. Stoica, K. W. Forsythe, and D. W. Bliss, "Waveform Optimization for MIMO Radar: A Cramer-Rao Bound Based Study," in Proc. 2007 IEEE Conf. Acoustics, Speech and Signal Processing, 2007, pp. II-917-II-920.
[32] J. Li, L. Z. Xu, P. Stoica, K. W. Forsythe, and D. W. Bliss, "Range Compression and Waveform Optimization for MIMO Radar: A Cramer Rao Bound Based Study," IEEE Trans. Signal Processing, vol. 56, pp. 218-232, 2008.
[33] E. Fishler, A. Haimovich, R. S. Blum, L. J. Cimini, D. Chizhik, and R. A. Valenzuela, "Spatial diversity in radars-models and detection performance," IEEE Trans. Signal Processing, vol. 54, pp. 823-838, 2006.
[34] N. H. Lehmann, E. Fishler, A. M. Haimovich, R. S. Blum, D. Chizhik, L. J. Cimini, et al., "Evaluation of Transmit Diversity in MIMO-Radar Direction Finding," IEEE Trans. Signal Processing, vol. 55, pp. 2215-2225, 2007.
[35] S. M. Alamouti, "A simple transmit diversity technique for wireless communications," IEEE Journal on Selected Areas in Communications, vol. 16, pp. 1451-1458, 1998.
[36] D. Tse and P. Viswanath, Fundamentals of Wireless Communication: Cambridge, U.K.: Cambridge Univ. Press, 2005.
[37] P. Stoica and A. Nehorai, "Performance study of conditional and unconditional direction-of-arrival estimation," IEEE Trans. Acoustics, Speech and Signal Processing, vol. 38, pp. 1783-1795, 1990.
[38] X. H. Wu, A. A. Kishk, and A. W. Glisson, "MIMO-OFDM radar for direction estimation," IET Radar, Sonar & Navigation, vol. 4, pp. 28-36, 2010.
[39] V. F. Pisarenko, "The retrieval of harmonics from a covariance function," Geophys. J. R. Astron. Soc., vol. 33, pp. 347-366, 1973.
[40] R. O. Schmidt, "Multiple emitter location and signal parameter estimation," IEEE Tran. Antennas and Propagation, vol. 34, pp. 276-280, 1986.
[41] R. Roy and T. Kailath, "ESPRIT-estimation of signal parameters via rotational invariance techniques," IEEE Trans. Acoustics, Speech and Signal Processing, vol. 37, pp. 984-995, 1989.
[42] M. Haardt, "Efficient one-, two-, and multidimentional high-resolution array signal processing," Ph.D. dissertation, Shaker Verlag, Aachen, Germany, 1997.
[43] A. Barabell, "Improving the resolution performance of eigenstructure-based direction-finding algorithms," in Proc. IEEE Conf. Acoustics, Speech, and Signal Proessing, 1983, pp. 336-339.
[44] Y. Bresler and A. Macovski, "Exact maximum likelihood parameter estimation of superimposed exponential signals in noise," IEEE Trans. Acoustics, Speech and Signal Processing, vol. 34, pp. 1081-1089, 1986.
[45] F. Li and R. J. Vaccaro, "Analysis of Min-Norm and MUSIC with arbitrary array geometry," IEEE Trans. Aerospace and Electronic Systems, vol. 26, pp. 976-985, 1990.
[46] M. Viberg and B. Ottersten, "Sensor array processing based on subspace fitting," IEEE Trans. Signal Processing, vol. 39, pp. 1110-1121, 1991.
[47] N. D. Sidiropoulos, R. Bro, and G. B. Giannakis, "Parallel factor analysis in sensor array processing," IEEE Trans. Signal Processing, vol. 48, pp. 2377-2388, 2000.
[48] S. A. Vorobyov, Y. Rong, N. D. Sidiropoulos, and A. B. Gershman, "Robust iterative fitting of multilinear models," IEEE Trans. Signal Processing, vol. 53, pp. 2678-2689, 2005.
[49] L. Z. Xu, J. Li, and P. Stoica, "Target detection and parameter estimation for MIMO radar systems," IEEE Trans. Aerospace and Electronic Systems, vol. 44, pp. 927-939, 2008.
[50] C. Duofang, C. Baixiao, and Q. Guodong, "Angle estimation using ESPRIT in MIMO radar," Electronics Letters, vol. 44, pp. 770-771, 2008.
[51] C. Jinli, G. Hong, and S. Weimin, "Angle estimation using ESPRIT without pairing in MIMO radar," Electronics Letters, vol. 44, pp. 1422-1423, 2008.
[52] D. Nion and N. D. Sidiropoulos, "A PARAFAC-based technique for detection and localization of multiple targets in a MIMO radar system," in Proc. 2009 IEEE Conf. Acoustics, Speech and Signal Processing, 2009, pp. 2077-2080.
[53] H. D. Yan, J. Li, and G. S. Liao, "Multitarget identification and localization using bistatic MIMO radar systems," EURASIP J. Adv. Signal Processing, vol. 2008, pp. 1-8, 2008.
[54] R. Xie, Z. Liu, and J. X. Wu, "Direction finding with automatic pairing for bistatic MIMO radar," Signal Processing, vol. 92, pp. 198-203, 2012.
[55] X. F. Zhang, L. Y. Xu, L. Xu, and D. Z. Xu, "Direction of Departure (DOD) and Direction of Arrival (DOA) Estimation in MIMO Radar with Reduced-Dimension MUSIC," IEEE Communications Letters, vol. 14, pp. 1161-1163, 2010.
[56] J. Tabrikian and I. Bekkerman, "Transmission diversity smoothing for multi-target localization [radar/sonar systems]," in Proc. 2005 IEEE Conf. Acoustics, Speech, and Signal Processing, 2005, pp. iv/1041-iv/1044.
[57] X. Zhang and D. Xu, "Low-complexity ESPRIT-based DOA estimation for colocated MIMO radar using reduced-dimension transformation," Electronics Letters, vol. 47, pp. 283-284, 2011.
[58] M. L. Bencheikh, Y. Wang, and H. Y. He, "Polynomial root finding technique for joint DOA DOD estimation in bistatic MIMO radar," Signal Processing, vol. 90, pp. 2723-2730, 2010.
[59] M. L. Yang, B. X. Chen, and X. Y. Yang, "Conjugate esprit algorithm for bistatic mimo radar," Electronics Letters, vol. 46, pp. 1692-1694, 2010.
[60] W. T. Shi, J. G. Huang, and C. B. He, "2D angle and doppler frequency estimation in MIMO radar," in Proc. 2011 World Congress Conf. Engineering and Computer Science, vol. I, San Francisco, USA, 2011.
[61] Z. D. Zhi, J. Y. Zheng, P. Ma, and C. S. Liu, "Angle Estimation with Automatic Pairing for Bistatic MIMO Radar," in Proc. 2nd International Congress Image and Signal Processing, 2009, pp. 1-5.
[62] M. Jin, G. S. Liao, and J. Li, "Joint DOD and DOA estimation for bistatic MIMO radar," Signal Processing, vol. 89, pp. 244-251, 2009.
[63] S. Min, C. D'Amours, and A. Yongacoglu, "Design of spreading permutations for MIMO-CDMA based on space-time block codes," IEEE Communications Letters, vol. 14, pp. 36-38, 2010.
[64] Y. Li and J. X. Guo, "A temporal LMS DPI suppression algorithm for CDMA-based passive detection system," in Proc. 4th International Congress Image and Signal Processing, 2011, pp. 52-56.
[65] D. K. Barton and S. A. Leonov, Radar Technology Encyclopedia: Norwood, Ma: Artech House, 1998.
[66] N. Levanon and E. Mozeson, "Radar Signals," ed: New York: Wiley, 2004.
[67] P. Woodward, Probability and Information Theory, With Applications to Radar: Probability and Information Theory, With Applications to Radar., 1957.
[68] J. Qu, K. M. Wong, and Z. Q. Luo, "The estimation of time delay and Doppler stretch of wideband signals," IEEE Trans. Signal Processing, vol. 43, pp. 904-916, 1995.
[69] R. Sharif and S. Abeysekera, "Efficient wideband signal parameter estimation using a radon-ambiguity transform slice," IEEE Trans. Aerospace and Electronic Systems, vol. 43, pp. 673-688, 2007.
[70] M. I. Skolnik, Radar Handbook, 2nd ed.: New York: McGraw-Hill, 1990.
[71] T. Tsao, M. Slamani, P. Varshney, D. Weiner, H. Schwarzlander, and S. Borek, "Ambiguity function for a bistatic radar," IEEE Trans. Aerospace and Electronic Systems, vol. 33, pp. 1041-1051, 1997.
[72] A. W. Rihaczek, Principles of High-Resolution Radar: New York: McGraw-Hill, 1969.
[73] W. L. Rubin and J. V. Difranco, "Analytic representation of wide-band radio frequency signals," Journal of the Franklin Institute, vol. 275, pp. 197-204, 1963.
[74] D. C. Lush and D. A. Hudson, "Ambiguity function analysis of wideband radars," in Proc. 1991 IEEE Conf. Radar, 1991, pp. 16-20.
[75] E. J. Kelly and R. P. Wishner, "Matched-Filter Theory for High-Velocity, Accelerating Targets," IEEE Trans. Military Electronics, vol. 9, pp. 56-69, 1965.
[76] L. H. Sibul and E. L. Titlebaum, "Volume Properties for the Wideband Ambiguity Function," IEEE Trans. Aerospace and Electronic Systems, vol. AES-17, pp. 83-87, 1981.
[77] D. Swick, "Wideband ambiguity function of pseudo-random sequences: An open problem (Corresp.)," IEEE Trans. Information Theory, vol. 14, pp. 602-603, 1968.
[78] M. Dawood and R. M. Narayanan, "Generalised wideband ambiguity function of a coherent ultrawideband random noise radar," IEE Proc. Radar, Sonar and Navigation, vol. 150, pp. 379-386, 2003.
[79] Z. G. Shi, S. Qiao, K. S. Chen, W. Z. Cui, W. Ma, T. Jiang, et al., "Ambiguity function of direct chaotic radar employing microwave chaotic colpitts osciliator," Progr. Electromagn. Res., vol. 77, pp. 1–14, 2007.
[80] M. Cherniakov, T. Zeng, and E. Plakidis, "Ambiguity function for bistatic SAR and its application in SS-BSAR performance analysis," in Proc. 2003 Conf. Radar, 2003, pp. 343-348.
[81] T. Zeng, M. Cherniakov, and L. Teng, "Generalized approach to resolution analysis in BSAR," IEEE Trans. Aerospace and Electronic Systems, vol. 41, pp. 461-474, 2005.
[82] H. H. Yan, O. Hirsch, G. W. Shen, R. Zetik, and R. S. Thomae, "Time Domain UWB Bistatic SAR Ambiguity Function," in Proc. 8th European Conf. Synthetic Aperture Radar, 2010, pp. 1-4.
[83] G. P. Cardillo, "On the use of the gradient to determine bistatic SAR resolution," in Proc. 1990 Merging Technologies International Symposium Antennas and Propagation Society, 1990, pp. 1032-1035.
[84] A. V. Ksendzuk, V. K. Volosyuk, and N. S. Sologub, "Optimisation of the spatial attitude of the bistatic and multistatic synthetic aperture radar," in Proc. 5th International Kharkov Symposium Physics and Engineering of Microwaves, Millimeter, and Submillimeter, 2004, pp. 178-180.
[85] G. San Antonio, D. R. Fuhrmann, and F. C. Robey, "MIMO Radar Ambiguity Functions," IEEE Journal of Selected Topics in Signal Processing, vol. 1, pp. 167-177, 2007.
[86] C. Y. Chen and P. P. Vaidyanathan, "Properties of the MIMO radar ambiguity function," in Proc. 2008 IEEE International Conf. Acoustics, Speech and Signal Processing, 2008, pp. 2309-2312.
[87] Y. I. Abramovich and G. J. Frazer, "Bounds on the Volume and Height Distributions for the MIMO Radar Ambiguity Function," IEEE Signal Processing Letters, vol. 15, pp. 505-508, 2008.
[88] D. Pastina, M. Bucciarelli, and P. Lombardo, "Multistatic and MIMO Distributed ISAR for Enhanced Cross-Range Resolution of Rotating Targets," IEEE Trans. Geoscience and Remote Sensing, vol. 48, pp. 3300-3317, 2010.
[89] P. Stoica and R. L. Moses, Spectral Analysis of Signals: Englewood Cliffs, NJ: Prentice-Hall, 2005.
[90] H. Messer, G. Singal, and L. Bialy, "On the achievable DF accuracy of two kinds of active interferometers," IEEE Trans. Aerospace and Electronic Systems, vol. 32, pp. 1158-1164, 1996.
[91] R. O. Nielsen, "Performance of combinations of projectors and receivers," IEEE Journal of Oceanic Engineering, vol. 27, pp. 753-759, 2002.
[92] A. B. Gershman and N. D. Sidiropoulos, Space-Time Processing for MIMO Communications: New York: Wiley, 2005.
[93] R. Boyer, "Deterministic asymptotic Cramer-Rao bound for the multidimensional harmonic model," Signal Processing, vol. 88, pp. 2869-2877, 2008.
[94] A. Manikas and C. Proukakis, "Modeling and estimation of ambiguities in linear arrays," IEEE Trans. Signal Processing, vol. 46, pp. 2166-2179, 1998.
[95] R. Boyer, "Performance Bounds and Angular Resolution Limit for the Moving Colocated MIMO Radar," IEEE Trans. Signal Processing, vol. 59, pp. 1539-1552, 2011.
[96] S. M. Kay, Fundamentals of Statistical Signal Processing: Estimation Theory: Prentice-Hall, Englewood Cliffs, NJ, USA, 1998.
[97] P. Stoica and A. Nehorai, "MUSIC, maximum likelihood, and Cramer-Rao bound: further results and comparisons," IEEE Trans. Acoustics, Speech and Signal Processing, vol. 38, pp. 2140-2150, 1990.
[98] X. Zhang, Z. Xu, L. Xu, and D. Xu, "Trilinear decomposition-based transmit angle and receive angle estimation for multiple-input multiple-output radar," IET Radar, Sonar & Navigation, vol. 5, pp. 626-631, 2011.
[99] D. Nion and N. D. Sidiropoulos, "Tensor Algebra and Multidimensional Harmonic Retrieval in Signal Processing for MIMO Radar," IEEE Trans. Signal Processing, vol. 58, pp. 5693-5705, 2010.
[100] N. L. Johnson and S. Kotz, Urn Models and Their Applications: Wiley, 1977.
[101] Valentin F. Kolchin, Boris A. Svast’yanov, and V. P. Christyakov, Random Allocations: V. H. Winston & Sons, 1978.
[102] R. Motwani and P. Raghavan, Randomized Algorithms: Cambridge University Press, 1995.