Sparse representation-based joint angle and Doppler frequency estimation for MIMO radar
详细信息 查看全文
- 作者：Jianfeng Li ; Xiaofei Zhang
- 关键词：Multiple ; input multiple ; output (MIMO) radar ; Angle estimation ; Sparse representation ; Doppler frequency
- 刊名：Multidimensional Systems and Signal Processing
- 出版年：2015
- 出版时间：January 2015
- 年：2015
- 卷：26
- 期：1
- 页码：179-192
- 全文大小：663 KB
- 参考文献：1. Bencheikh, M. L., & Wang, Y. (2010). Joint DOD-DOA estimation using combined ESPRIT-MUSIC approach in MIMO radar. / Electronics Letters, / 46(15), 1081-083. CrossRef
2. Bencheikh, M. L., Wang, Y. D., & He, H. Y. (2010). Polynomial root finding technique for joint DOA DOD estimation in bistatic MIMO radar. / Signal Processing, / 90(9), 2723-730. CrossRef
3. Berg, E. V. & Friedlander, M. P. (2007). SPGL1: A solver for large-scale sparse reconstruction, June. http://www.cs.ubc.ca/labs/scl/spgl1.
4. Candes, E., Romberg, J., & Tao, J. (2006). Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information. / IEEE Transactions on Information Theory, / 52(2), 489-09. CrossRef
5. Davenport, M. A., Boufounos, P. T., Wakin, M. B., et al. (2010). Signal processing with compressive measurements. / IEEE Journal of Selected Topics on Signal Processing, / 4(2), 445-60. CrossRef
6. Donoho, D. L. (2006). Compressed sensing. / IEEE Transactions on Information Theory, / 52(4), 1289-306. CrossRef
7. Duofang, C., Baixiao, C., & Guodong, Q. (2008). Angle estimation using ESPRIT in MIMO radar. / Electronics Letters, / 44(12), 770-71. CrossRef
8. Fishler, E., Haimovich, A., Blum, R. S., Cimini, L. J., Chizhik, D., & Valenzuela, R. A. (2004). MIMO radar: An idea whose time has come. In / Proceedings of the IEEE Radar Conference (pp. 71-8).
9. Guo, X., Wan, Q., Chang, C., & Lam, E. Y. (2010). Source localization using a sparse representation framework to achieve super resolution. / Multidimensional System and Signal Processing, / 21(4), 391-02. CrossRef
10. Hu, N., Ye, Z., Xu, D., & Cao, S. (2012). A sparse recovery algorithm for DOA estimation using weighted subspace fitting. / Signal Processing, / 92, 2566-570. CrossRef
11. Jinli, C., Hong, G., & Weimin, S. (2008). Angle estimation using ESPRIT without pairing in MIMO radar. / Electronics Letters, / 44(24), 1422-423. CrossRef
12. Li, J. & Stoica, P. (2006). MIMO radar–diversity means superiority. In / Proceedings of the 14th adaptive sensor array processing workshop (ASAP -6) (pp. 1-). Mass, USA: Lincoln Laboratory.
13. Li, J., Stoica, P., Xu, L., & Roberts, W. (2007). On parameter identifiability of MIMO radar. / IEEE Signal Processing Letters, / 14(12), 968-71. CrossRef
14. Li, J., Liao, G., & Griffiths, H. (2011). Bistatic MIMO radar space-time adaptive processing. In / 2011 IEEE International Radar Conference (pp. 498-02). Kansas City, Missouri: Westin Crown Center.
15. Liu, Z., Ruan, X., & He, J. (2013). Efficient 2-D DOA estimation for coherent sources with a sparse acousticvector-sensor array. / Multidimensional Systems and Signal Processing, / 24(1), 105-20. CrossRef
16. Liu, Y., Wu, M., & Wu, S. (2010). Fast OMP algorithm for 2D angle estimation in MIMO radar. / Electronics Letters, / 46(6), 444-45. CrossRef
17. Malioutov, D., Cetin, M., & Willsky, A. S. (2005). A sparse signal reconstruction perspective for source localization with sensor arrays. / IEEE Transactions on Signal Processing, / 53(8), 3010-022. CrossRef
18. Mallat, S. G., & Zhifeng, Z. (1993). Matching pursuits with time frequency dictionaries. / IEEE Transactions on Signal Procesing, / 41(12), 3397-415.
文摘
An algorithm based on sparse representation for joint angle and Doppler frequency estimation in multiple-input multiple-output radar is proposed. Through the data reconstruction, the algorithm only requires the dictionary for one-dimensional angle [e.g. direction of departure (DOD)], which reduces the computational complexity compared to conventional method using dictionary for two-dimensional angle. The DOD can be estimated by finding the non-zero rows in the recovered matrix, which also contains the information of the direction of arrival (DOA) and the Doppler frequency, and they can be achieved via singular value decomposition and least squares (LS) principle. The estimated DOD, DOA and Doppler frequency can be automatically paired and the parameter estimation performance of the proposed algorithm is better than that of estimation of signal parameters via rotational invariance techniques (ESPRIT)-based algorithm and parallel factor (PARAFAC) method. Furthermore, the proposed algorithm requires no knowledge of the number of targets and works well for coherent targets. Simulation results verify?the?effectiveness of the algorithm.