Abstract-High-resolution wide-swath (HRWS) imaging with spaceborne synthetic aperture radar (SAR) can be achieved by using azimuth displacement phase center antenna (DPCA) technique. However, it will consequently leads to extremely high data rate on satellite downlink system. A novel sparse sampling scheme based on compressed sensing (CS) theory for azimuth DPCA SAR was proposed, by which only a small proportion of radar echoes are utilized for imaging to reduce data rate. The corresponding image formation algorithm for the proposed scheme was presented in the paper. The SAR echo signal of each channel can be reconstructed with high probability by using orthogonal matching pursuit (OMP) algorithm in Doppler frequency domain. The reconstructed echo signals of each channel are jointly processed by means of spectrum reconstructing filter for compensating Doppler spectrum aliasing resulting from non-uniform sampling in azimuth direction. The high quality SAR image can be obtained by using chirp scaling algorithm. The effectiveness of the proposed approach was validated by computer simulations using both point targets and distributed targets.
Abstract-Sidelobes of strong targets substantially impact image quality of synthetic aperture radar (SAR) using linear frequency modulation (LFM) waveform, especially in urban areas. A novel space-borne azimuth multi-channel SAR scheme with ultra-low range sidelobe-ratio (RSLR) performance was proposed, employing complete complementary sequence (CC-S) coding waveform.The CC-S waveform was utilized to acquire ultra-low RSLR performance in range direction. Azimuth multi-channel scheme was introduced, to compensate reduction of effective PRF due to employing CC-S, and to mitigate azimuth resolution lost resulted from strong azimuth weighting, in order to implement low side-lobe performance in both range and azimuth direction. The method for pre-processing the CC-S based multi-channel SAR data was proposed, which would both compensate receiving time difference of sub-sequences and reconstruct azimuth spectrum of multi-channel SAR data. Furthermore, the corresponding image formation algorithm for accurately focusing raw data of the SAR system was also proposed. Computer simulation results were presented, which demonstrated the validity of the proposed SAR scheme and image formation algorithm.
Classical rough set theory is a technique of granular computing for handling the uncertainty, vagueness, and granularity in information systems. Covering-based rough sets are proposed to generalize this theory for dealing with covering data. By introducing a concept of misclassification rate functions, an extended variable precision covering-based rough set model is proposed in this paper. In addition, we define the f-lower and f-upper approximations in terms of neighborhoods in the extended model and study their properties. Particularly, two coverings with the same reductions are proved to generate the same f-lower and f-upper approximations. Finally, we discuss the relationships between the new model and some other variable precision rough set models.
Convolutional Neural Network (CNN) models applied to synthetic aperture radar automatic target recognition (SAR ATR) universally focus on two important issues: overfitting caused by lack of sufficient training data and independent variations like worse estimates of the aspect angle, etc. To this end, we developed a lightweight CNN-based method named SARNet to accomplish the classification task. Firstly, a clockwise data amplification approach is presented to generate adequate SAR images without requiring many raw SAR images, effectively avoiding overfitting in the course of training. Then a SARNet is devised to process the extracted features from SAR target images and work on classification tasks with parameters fine-tuning under comparative models. To enhance and structurally organize the representation of learned proposed model, various activation functions are explored in this paper. Furthermore, due to the pioneering conducted experiments, training samples in the MSTAR and extended MSTAR database are utilized to demonstrate the robustness and effectiveness of the lightweight model. Experimental results have shown that our proposed model has achieved a 98.30% state-of-the-art accuracy.
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