SAR Target Detection in Complex Scene Based on 2-D Singularity Power Spectrum Analysis

The synthetic aperture radar (SAR) target detection method in the background of a complex scene and extremely low signal-to-noise ratio (EL-SNR) is studied in this paper. With theoretical derivation and analysis, the singularity power spectrum (SPS) is developed into two-dimensional SPS (2D-SPS). Developed from the 2-D Holder exponent and the SPS, the 2D-SPS can be applied for the singularity power analysis of images. Furthermore, a novel SAR target detection method based on 2D-SPS is proposed. The proposed methodology is tested on sea clutters, both with and without SAR ship target, from the OpenSAR data sets. The experimental results indicate that the SAR target detection based on 2D-SPS performs better than conventional multifractal spectrum (MFS) and constant false alarm rate (CFAR) methods, and can achieve more than 95% and 98% detection probability, respectively, under EL-SNR with SNR = −30 dB, SNR = −10 dB, and $P_{f} = 10^{-2}$ , and almost 100% detection probability for weak and multiple targets within sea clutters. The proposed method can be applied to target detection under the background of fractal noise and provides a reference for target detection in related fields.

[1]  J. Álvarez-Ramírez,et al.  Detrending fluctuation analysis based on moving average filtering , 2005 .

[2]  Ji Ren,et al.  Analysis of multifractality for sea clutter , 2011, Proceedings of 2011 IEEE CIE International Conference on Radar.

[3]  Jose M. Redondo,et al.  Self-similar distribution of oil spills in European coastal waters , 2009 .

[4]  Zhu Xiaohua,et al.  Multifractal cross-correlation analysis of sea clutter and target detection based on Q-MMSPF , 2013 .

[5]  Feng Luo,et al.  The Fractal Properties of Sea Clutter and Their Applications in Maritime Target Detection , 2013, IEEE Geoscience and Remote Sensing Letters.

[6]  Wei-Xing Zhou,et al.  Detrending moving average algorithm for multifractals. , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.

[7]  Wenxian Yu,et al.  Singularity power spectrum distribution , 2015 .

[8]  Yongliang Wang,et al.  General signal model of MIMO radar for moving target detection , 2017 .

[9]  Gang Xiong,et al.  Wavelet leaders-based multifractal spectrum distribution , 2014 .

[10]  Gang Xiong,et al.  Multifractal analysis of sea clutter and target detection based on the Wavelet Leaders method , 2016, 2016 IEEE International Conference on Digital Signal Processing (DSP).

[11]  H. Stanley,et al.  Multifractal Detrended Fluctuation Analysis of Nonstationary Time Series , 2002, physics/0202070.

[12]  Mateusz Malanowski,et al.  Detection and parameter estimation of manoeuvring targets with passive bistatic radar , 2012 .

[13]  Ying Li,et al.  Target Detection in Sea Clutter Based on Multifractal Characteristics After Empirical Mode Decomposition , 2017, IEEE Geoscience and Remote Sensing Letters.

[14]  Xiaozhang Zhu,et al.  Moving target detection of OFDM-MIMO radar based on ST-DFT , 2016 .

[15]  Huanxin Zou,et al.  A Bilateral CFAR Algorithm for Ship Detection in SAR Images , 2015, IEEE Geoscience and Remote Sensing Letters.

[16]  Gang Xiong,et al.  The time-singularity multifractal spectrum distribution , 2012 .

[17]  Gang Xiong,et al.  Cross Correlation Singularity Power Spectrum Theory and Application in Radar Target Detection Within Sea Clutters , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[18]  Lanqing Huang,et al.  OpenSARShip: A Dataset Dedicated to Sentinel-1 Ship Interpretation , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.