Based on Improved ESPRIT Algorithm Radar Multi-target Recognition

At present, application of dense targets is one of the important means of penetration. In many observation conditions, the echoes from the dense targets mixed with many aliasing signals, and conventional radar signal processing algorithms do not take the aliasing signals into account. Therefore it is difficult for conventional algorithms to recognize multi-targets. In this paper, an improved ESPRIT algorithm is proposed which can recognize the multi-targets from the aliasing echoes and greatly reduce the computational complexity without changing the algorithm accuracy, especially can obtain a better estimation in the case of low SNR environment. The proposed algorithm can firstly quickly realize the estimate of scattering center parameters of target echoes, and then based on the estimation, the aliasing targets can be recognized. The Simulation also verifies the improved ESPRIT algorithm has a better identification and recognition capability of aliasing targets in low SNR condition. Moreover because of reduction of the computational complexity, the performance of proposed algorithm is faster than conventional methods, especially in the case of multiple aliasing scattering centers.

[1]  Jianjiang Zhou,et al.  Multiple moving target resolution and imaging based on ISAR principle , 1995, Proceedings of the IEEE 1995 National Aerospace and Electronics Conference. NAECON 1995.

[2]  Victor C. Chen,et al.  Radar imaging of multiple moving targets , 1997, Optics & Photonics.

[3]  Desheng Wang,et al.  A new algorithm for group tracking , 2001, 2001 CIE International Conference on Radar Proceedings (Cat No.01TH8559).

[4]  Sabino Gadaleta,et al.  Multiple frame cluster tracking , 2002, SPIE Defense + Commercial Sensing.

[5]  Aubrey B. Poore,et al.  Multiple hypothesis clustering and multiple frame assignment tracking , 2004, SPIE Defense + Commercial Sensing.

[6]  T. Kirubarajan,et al.  Tracker and signal processing for the benchmark problem with unresolved targets , 2006, IEEE Transactions on Aerospace and Electronic Systems.

[7]  S.J. Davey,et al.  Tracking Possibly Unresolved Targets with PMHT , 2007, 2007 Information, Decision and Control.

[8]  Thiagalingam Kirubarajan,et al.  Tracking of spawning targets with multiple finite resolution sensors , 2008 .

[9]  Huimin Chen,et al.  Tracking of spawning targets with multiple finite resolution sensors , 2002, IEEE Transactions on Aerospace and Electronic Systems.

[10]  T. Kirubarajan,et al.  Joint detection and tracking of unresolved targets with monopulse radar , 2008, IEEE Transactions on Aerospace and Electronic Systems.

[11]  Xin Zhang,et al.  Detection and Localization of Multiple Unresolved Extended Targets via Monopulse Radar Signal Processing , 2009, IEEE Transactions on Aerospace and Electronic Systems.

[12]  Hao Ling,et al.  Improved Current Decomposition in Helical Antennas Using the ESPRIT Algorithm , 2010 .

[13]  Feng Yang,et al.  DOA Estimation with Sub-Array Divided Technique and Interporlated ESPRIT Algorithm on a Cylindrical Conformal Array Antenna , 2010 .

[14]  R. Tharmarasa,et al.  Tracking multiple unresolved targets using MIMO radars , 2010, 2010 IEEE Aerospace Conference.

[15]  Jiajia Jiang,et al.  THREE-DIMENSIONAL LOCALIZATION ALGORITHM FOR MIXED NEAR-FIELD AND FAR-FIELD SOURCES BASED ON ESPRIT AND MUSIC METHOD , 2013 .