Parametric inverse synthetic aperture radar manoeuvring target motion compensation based on particle swarm optimiser

Owing to the unknown high-order unstable motion, it is difficult to realise the motion compensation, that is, envelope alignment and phase auto-focusing, for inverse synthetic aperture radar (ISAR) manoeuvring targets. By modelling the envelope shifting and phase modulation into two different high-order polynomial functions against observation time, a novel parametric ISAR motion compensation method is proposed based on polynomial coefficients estimation via particle swarm optimiser (PSO). The average range profile energy and the image contrast are chosen as the fitness functions for envelope alignment and phase auto-focusing, respectively. Furthermore, the polynomial order of phase auto-focusing is chosen higher than that of envelope alignment to meet the accuracy need of phase compensation. Besides, in order to speed up the convergence and ensure that the estimated parameters converge to the global optimisation, a least-square fitting pre-processing is also proposed to determine the target motion order and initialise the best particle at first. Finally, the results based on both numerical experiments and real data are all provided to demonstrate the effectiveness of the proposed PSO-based parametric methods.

[1]  Ljubisa Stankovic,et al.  Motion compensation in ISAR imaging using the registration-restoration-fusion approach , 2008 .

[2]  F. Pérez-Martínez,et al.  Uniform rotational motion compensation for inverse synthetic aperture radar with non-cooperative targets , 2008 .

[3]  Y. Rahmat-Samii,et al.  Particle swarm optimization in electromagnetics , 2004, IEEE Transactions on Antennas and Propagation.

[4]  Haiqing Wu,et al.  Moving target imaging and trajectory computation using ISAR , 1994 .

[5]  Charles V. Jakowatz,et al.  Phase gradient autofocus-a robust tool for high resolution SAR phase correction , 1994 .

[6]  Dale A. Ausherman,et al.  Developments in Radar Imaging , 1984, IEEE Transactions on Aerospace and Electronic Systems.

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

[8]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[9]  Shie Qian,et al.  Joint time-frequency transform for radar range-Doppler imaging , 1998 .

[10]  Ljubisa Stankovic,et al.  Real-time motion compensation, image formation and image enhancement of moving targets in ISAR and SAR using S-methodbased approach , 2008 .

[11]  M. J. Gerry,et al.  A parametric model for synthetic aperture radar measurements , 1999 .

[12]  J J Miller,et al.  Aberration correction by maximizing generalized sharpness metrics. , 2003, Journal of the Optical Society of America. A, Optics, image science, and vision.

[13]  Junfeng Wang,et al.  Global range alignment for ISAR , 2003 .

[14]  J. Keller,et al.  Geometrical theory of diffraction. , 1962, Journal of the Optical Society of America.

[15]  LJubisa Stankovic,et al.  Adaptive Local Polynomial Fourier Transform in ISAR , 2006, EURASIP J. Adv. Signal Process..

[16]  Marco Martorella,et al.  Use of Genetic Algorithms for Contrast and Entropy Optimization in ISAR Autofocusing , 2006, EURASIP J. Adv. Signal Process..

[17]  Hao Ling,et al.  Use of genetic algorithms in ISAR imaging of targets with higher order motions , 2003 .

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

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

[20]  F. Berizzi,et al.  Autofocusing of inverse synthetic aperture radar images using contrast optimization , 1996, IEEE Transactions on Aerospace and Electronic Systems.

[21]  Junfeng Wang,et al.  Improved Global Range Alignment for ISAR , 2007, IEEE Transactions on Aerospace and Electronic Systems.

[22]  Lin Luo,et al.  Inverse synthetic aperture radar imaging of maneuvering targets , 1998 .

[23]  Sang-Hong Park,et al.  Segmentation of ISAR Images of Targets Moving in Formation , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[24]  Angelo Aprile,et al.  Translational rotational motion compensation: a single algorithm for different radar maging applications , 2008 .