Low Complexity DFT Based DOA Estimation for Synthetic Nested Array Using Single Moving Sensor
The issue of direction of arrival (DOA) estimation for synthetic nested array is investigated in this paper. The synthetic nested array (SNA) is formed by one single sensor moving according to the configuration of the physical nested array. With the synthetic array, both high resolution DOA estimation and array aperture miniaturization requirements can be met. To reduce the computationally complexity for SNA, a discrete Fourier transform (DFT) based algorithm is proposed which needs no eigen decomposition. We first reconstruct the data matrix reshaped from the data received by moving senor to obtain the observation vector and then get the initial DOA estimates via DFT of the observation vector. At last the fine estimates can be obtained through searching for peaks corrected by phase rotation matrix over a small sector. The proposed algorithm for SNA can achieve better bearing estimation performance than spatial smoothing (SS) subspace based methods such as SS-MUSIC and SS-ESPRIT, due to the fact that it can fully utilize array aperture while SS-MUSIC and SS-ESPRIT lose a half. Besides, the proposed algorithm involves full degree of freedoms (DOF). Numerical simulations validate the efficiency and superiority of the proposed algorithm.
KeywordsSynthetic array Nested array Direction of arrival Low complexity DFT
This work is supported by China NSF Grants (61371169, 61601167, 61601504), Jiangsu NSF (BK20161489), the open research fund of State Key Laboratory of Millimeter Waves, Southeast University (No. K201826), the Fundamental Research Funds for the Central Universities (No. NE2017103) and Graduate Innovative Base (laboratory) Open Funding of Nanjing University of Aeronautics and Astronautics (kfjj20170412).
- 3.Jianpeng, W. (2011). Moving array signal processing technology based on spatial and temporal extending. Changsha: National University of Defense Technology.Google Scholar
- 4.Hao, X. C., Xie, S. G., Zeng, X. Y., Du, X., & Wang, C. (2016). A near-field radiation source localization method based on passive synthetic arrays using single channel receiver. In IEEE international symposium on microwave, antenna, propagation, and EMC technologies (pp. 31–35).Google Scholar
- 5.Al-Ardi, E. M., Shubair, R. M., & Al-Mualla, M. E. (2006). Direction of arrival estimation in a multipath environment: An overview and a new contribution. Applied Computational Electromagnetics Society Journal, 21(3), 226–238.Google Scholar
- 8.Xie, D., Niu, T., Huang, J., & Ge, H. (2008).Maximum likelihood parameters estimation in non-uniform noise fields using moving array. In 42nd Asilomar conference on signals, systems and computers (pp. 1732–1735).Google Scholar
- 10.Sullivan, E. J. (2000). On the role of modeling in passive synthetic aperture processing. In OCEANS 2000 MTS/IEEE conference and exhibition (Vol 1, pp. 7–9).Google Scholar
- 15.Ramirez, J., Odom, J., & Krolik, J. (2014). Exploiting array motion for augmentation of co-prime arrays. In Sensor array and multichannel signal processing workshop (pp. 525–528).Google Scholar
- 16.Ramirez, J., & Krolik, J. (2015). Multiple source localization with moving co-prime arrays. In IEEE international conference on acoustics, speech and signal processing (pp. 2374–2378).Google Scholar
- 17.Demissie, B., Oispuu, M., & Ruthotto, E. (2008). Localization of multiple sources with a moving array using subspace data fusion. In International conference on information fusion (pp. 1–7).Google Scholar
- 18.Keller, D. R., Moon, T. K., & Gunther, J. H. (2006). Narrowband source localization from a moving array of sensors. In 40th Asilomar conference on signals, systems and computers (pp. 2285–2289).Google Scholar
- 19.Ke, Z., Peng, M., & Zhang, J. Y. (2011) DOA estimation algorithm based on FFT in switch antenna array. In IEEE CIE international conference on radar (pp. 1425–1428).Google Scholar
- 20.See, C. M. S. (2003). A single channel approach to high resolution direction finding and beamforming. In Proceedings of the IEEE international conference on acoustics, speech, and signal processing (vol. 215, pp. V-217–220).Google Scholar
- 28.Rao, B., & Hari, K. V. S. (1990). Effect of spatial smoothing on state space methods/ESPRIT. In 5th ASSP workshop on spectrum estimation and modeling (pp. 377–381).Google Scholar