Performance Analysis of the Scenario-Based Construction Method for Real Target ISAR Recognition

Due to the di-culty in estimating the 2D image plane of the inverse synthetic aperture radar (ISAR) image, we recently proposed a new paradigm to construct the training database based on the ∞ight scenario. However, because the ∞ight condition for the training and the test data was identical, much more study is required for this method to be applied to the real ISAR scenario. This paper presents a study on the factor that can afiect the applicability of scenario-based method to the real target ISAR recognition. Simulation results using flve scatterer models show that accurate measurement of ∞ight direction and aspect angle variation are required and enough bandwidth larger than 200MHz should be guaranteed for the successful classiflcation.

[1]  Jong-Il Park,et al.  A Comparative Study on ISAR Imaging Algorithms for Radar Target Identification , 2010 .

[2]  Kyung-Tae Kim,et al.  Construction of ISAR Training Database for Automatic Target Recognition , 2011 .

[3]  Kyung-Tae Kim,et al.  IMPROVEMENT OF ITERATIVE PHYSICAL OPTICS USING PREVIOUS INFORMATION TO GUIDE INITIAL GUESS , 2012 .

[4]  Chung-ching Chen,et al.  Target-Motion-Induced Radar Imaging , 1980, IEEE Transactions on Aerospace and Electronic Systems.

[5]  Kyung-Tae Kim,et al.  ISAR Imaging of Multiple Targets Using Edge Detection and Hough Transform , 2008 .

[6]  Li Xi,et al.  Autofocusing of ISAR images based on entropy minimization , 1999 .

[7]  Chan-Hong Kim,et al.  Radar Cross Section Measurements of a Realistic Jet Engine Structure with Rotating Parts , 2011 .

[8]  T. Yeo,et al.  Interferometric ISAR Imaging on Squint Model , 2008 .

[9]  Kyung-Tae Kim,et al.  ISAR Imaging of Multiple Targets Based on Particle Swarm Optimization and Hough Transform , 2009 .

[10]  Hyo-Tae Kim,et al.  STEPPED-FREQUENCY ISAR MOTION COMPENSATION USING PARTICLE SWARM OPTIMIZATION WITH AN ISLAND MODEL , 2008 .

[11]  Zhensen Wu,et al.  HIGH RESOLUTION RANGE PROFILE IDENTIFYING SIMULATION OF LASER RADAR BASED ON PULSE BEAM SCATTERING CHARACTERISTICS OF TARGETS , 2009 .

[12]  Kyung-Tae Kim,et al.  Construction of Training Database Based on High Frequency RCS Prediction Methods For ATR , 2008 .

[13]  Hyo-Tae Kim,et al.  ENHANCED RANGE ALIGNMENT USING A COMBINATION OF A POLYNOMIAL AND GAUSSIAN BASIS FUNCTIONS , 2009 .

[14]  Y. Álvarez-López,et al.  On the Influence of Coupling AMC Resonances for RCS Reduction in the SHF Band , 2011 .

[15]  Y. Álvarez-López,et al.  A Novel Approach for RCS Reduction Using a Combination of Artificial Magnetic Conductors , 2010 .

[16]  Kyung-Tae Kim,et al.  IMPROVEMENT OF RCS PREDICTION USING MODIFIED ANGULAR DIVISION ALGORITHM , 2012 .

[17]  Noh-Hoon Myung,et al.  A Novel Hybrid Aipo-MoM Technique for Jet Engine Modulation Analysis , 2010 .

[18]  P. C. Gao,et al.  Fast RCS prediction using multiresolution shooting and bouncing ray method on the GPU , 2010 .

[19]  Kyungjin Park,et al.  RCS Prediction Acceleration and Reduction of Table Size for the Angular Division Algorithm , 2009 .

[20]  Kyungjin Park,et al.  Efficient RCS Prediction Method Using Angular Division Algorithm , 2009 .

[21]  Noh-Hoon Myung,et al.  MODIFIED HILBERT-HUANG TRANSFORM AND ITS APPLICATION TO MEASURED MICRO DOPPLER SIGNATURES FROM REALISTIC JET ENGINE MODELS , 2012 .

[22]  Chih-Wei Huang,et al.  Application of ICA technique to PCA based radar target recognition , 2010 .

[23]  Luis E. Garcia-Castillo,et al.  RCS COMPUTATION USING A PARALLEL IN-CORE AND OUT-OF-CORE DIRECT SOLVER , 2011 .

[24]  Kyung-Tae Kim,et al.  Efficient classification of ISAR images , 2005 .

[25]  Ming-Chung Fang,et al.  Application of SVD noise-reduction technique to PCA based radar target recognition , 2008 .

[26]  Hyo-Tae Kim,et al.  BEAM TRACING FOR FAST RCS PREDICTION OF ELECTRICALLY LARGE TARGETS , 2011 .

[27]  Noh-Hoon Myung,et al.  High Resolution Range Profile-Jet Engine Modulation Analysis of Aircraft Models , 2011 .

[28]  Seung-Ku Han,et al.  EFFICIENT RADAR TARGET RECOGNITION USING A COMBINATION OF RANGE PROFILE AND TIME- FREQUENCY ANALYSIS , 2010 .

[29]  Kyung-Tae Kim,et al.  Classification of ISAR images using sparse recovery algorithms , 2014, 2014 IEEE Conference on Antenna Measurements & Applications (CAMA).

[30]  Junfeng Wang,et al.  Minimum-entropy phase adjustment for ISAR , 2004 .