ISAR imaging with random missing observations based on non-iterative signal reconstruction algorithm

Random missing observations in real-world inverse synthetic aperture radar (ISAR) imaging may appear due to the instability of the radar system. Under this circumstance, the signal of one range bin over the slow-time has limited samples. Using traditional range-Doppler algorithm, high-quality ISAR images for random missing observations cannot be obtained. Recently, a new non-iterative algorithm based on the combined robust statistics and compressive sensing (CS) theory has been proposed to efficiently recover a complete signal from a small random set of samples, showing robustness in the presence of noise [16]. It is also important to emphasize that non-iterative method is computationally simpler than the iterative signal reconstruction solutions, and thus more amenable to practical applications. Therefore, based on the non-iterative robust signal reconstruction method, a new algorithm for ISAR imaging with random missing observations is proposed in this paper. The efficiency of the proposed approach is demonstrated on the examples with simulated and real data.

[1]  Emmanuel J. Candès,et al.  Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies? , 2004, IEEE Transactions on Information Theory.

[2]  Zheng Bao,et al.  High-Resolution ISAR Imaging by Exploiting Sparse Apertures , 2012, IEEE Transactions on Antennas and Propagation.

[3]  Ljubisa Stankovic,et al.  Time-Frequency Signal Analysis With Applications , 2013 .

[4]  P. Stoica,et al.  High-resolution SAR imaging with angular diversity , 2001 .

[5]  B. Ripley,et al.  Robust Statistics , 2018, Encyclopedia of Mathematical Geosciences.

[6]  Yong Wang,et al.  A Novel Algorithm for Estimating the Rotation Angle in ISAR Imaging , 2008, IEEE Geoscience and Remote Sensing Letters.

[7]  S. Frick,et al.  Compressed Sensing , 2014, Computer Vision, A Reference Guide.

[8]  Marco Martorella,et al.  Range Doppler and Image Autofocusing for FMCW Inverse Synthetic Aperture Radar , 2009, IEEE Transactions on Aerospace and Electronic Systems.

[9]  Igor Djurovic,et al.  Robust L-estimation based forms of signal transforms and time-frequency representations , 2003, IEEE Trans. Signal Process..

[10]  J. C. Kirk,et al.  Interrupted synthetic aperture radar (SAR) , 2002 .

[11]  Emmanuel J. Candès,et al.  Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.

[12]  Marco Martorella,et al.  Target Recognition by Means of Polarimetric ISAR Images , 2011, IEEE Transactions on Aerospace and Electronic Systems.

[13]  Marco Diani,et al.  High-resolution ISAR imaging of maneuvering targets by means of the range instantaneous Doppler technique: modeling and performance analysis , 2001, IEEE Trans. Image Process..

[14]  Mengdao Xing,et al.  Achieving Higher Resolution ISAR Imaging With Limited Pulses via Compressed Sampling , 2009, IEEE Geoscience and Remote Sensing Letters.

[15]  William J. Emery,et al.  Urban Mapping Using Coarse SAR and Optical Data: Outcome of the 2007 GRSS Data Fusion Contest , 2008, IEEE Geoscience and Remote Sensing Letters.

[16]  Irena Orovic,et al.  Relationship between the robust statistics theory and sparse compressive sensed signals reconstruction , 2014, IET Signal Process..

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

[18]  Richard Bamler,et al.  Tomographic SAR Inversion by $L_{1}$ -Norm Regularization—The Compressive Sensing Approach , 2010, IEEE Transactions on Geoscience and Remote Sensing.