Compressive Sensing for Radar Target Signal Recovery Based on Block Sparse Bayesian Learning(in English)

Nowadays, high-speed sampling and transmission is a foremost challenge of radar system. In order to solve this problem, a compressive sensing approach is proposed for radar target signals in this study. Considering the block sparse structure of signals, the proposed method uses a simple measurement matrix to sample the signals and employ a Block Sparse Bayesian Learning (BSBL) algorithm to recover the signals. The classical BSBL algorithm is applicable to real signal, while radar signals are complex. Therefore, a Complex Block Sparse Bayesian Learning (CBSBL) is extended for the radar target signal reconstruction. Since the existed radar signal compressive sensing models do not take block structures in consideration, the signal reconstruction of proposed approach is more accurate and robust, and the simple measurement matrix leads to an easy implementation of hardware. The effectiveness of the proposed approach is demonstrated by numerical simulations

Exploiting Multi-View SAR Images for Robust Target Recognition

The exploitation of multi-view synthetic aperture radar (SAR) images can effectively improve the performance of target recognition. However, due to the various extended operating conditions (EOCs) in practical applications, some of the collected views may not be discriminative enough for target recognition. Therefore, each of the input views should be examined before being passed through to multi-view recognition. This paper proposes a novel structure for multi-view SAR target recognition. The multi-view images are first classified by sparse representation-based classification (SRC). Based on the output residuals, a reliability level is calculated to evaluate the effectiveness of a certain view for multi-view recognition. Meanwhile, the support samples for each view selected by SRC collaborate to construct an enhanced local dictionary. Then, the selected views are classified by joint sparse representation (JSR) based on the enhanced local dictionary for target recognition. The proposed method can eliminate invalid views for target recognition while enhancing the representation capability of JSR. Therefore, the individual discriminability of each valid view as well as the inner correlation among all of the selected views can be exploited for robust target recognition. Experiments are conducted on the moving and stationary target acquisition recognition (MSTAR) dataset to demonstrate the validity of the proposed method

Low-Rank and Sparse Matrix Decomposition with Cluster Weighting for Hyperspectral Anomaly Detectiond

Hyperspectral anomaly detection plays an important role in the field of remote sensing. It provides a way to distinguish interested targets from the background without any prior knowledge. The majority of pixels in the hyperspectral dataset belong to the background, and they can be well represented by several endmembers, so the background has a low-rank property. Anomalous targets usually account for a tiny part of the dataset, and they are considered to have a sparse property. Recently, the low-rank and sparse matrix decomposition (LRaSMD) technique has drawn great attention as a method for solving anomaly detection problems. In this letter, a new anomaly detection method based on LRaSMD and cluster weighting is proposed. We concentrate on the sparse part, which contains most of anomaly information, and calculate the initial anomaly matrix based on this part. To suppress background regions and discriminate anomalies from the background more distinctly, a weighting strategy in terms of the clustering result is used, and then the anomaly matrix is updated. The judgement of anomalies is made according to the responses on the matrix. Our proposed method considers the characteristics of anomalies from the spectral dimension and the spatial distribution simultaneously. Experiments on three hyperspectral datasets demonstrate the outstanding performance of the proposed method

Learning Padless Correlation Filters for Boundary-effect Free Tracking

Recently, discriminative correlation filters (DCFs) have achieved enormous popularity in the tracking community due to high accuracy and beyond real-time speed. Among different DCF variants, spatially regularized discriminative correlation filters (SRDCFs) demonstrate excellent performance in suppressing boundary effects induced from circularly shifted training samples. However, SRDCF have two drawbacks which may be the bottlenecks for further performance improvement. First, SRDCF needs to construct an element-wise regularization weight map which can lead to poor tracking performance without careful tunning. Second, SRDCF does not guarantee zero correlation filter values outside the target bounding box. These small but nonzero filter values away from the filter center hardly contribute to target location but induce boundary effects. To tackle these drawbacks, we revisit the standard SRDCF formulation and introduce padless correlation filters (PCFs) which totally remove boundary effects. Compared with SRDCF that penalizes filter values with spatial regularization weights, PCF directly guarantee zero filter values outside the target bounding box with a binary mask. Experimental results on the OTB2013, OTB2015 and VOT2016 data sets demonstrate that PCF achieves real-time frame-rates and favorable tracking performance compared with state-of-the-art trackers

Learning Padless Correlation Filters for Boundary-effect Free Tracking

Recently, discriminative correlation filters (DCFs) have achieved enormous popularity in the tracking community due to high accuracy and beyond real-time speed. Among different DCF variants, spatially regularized discriminative correlation filters (SRDCFs) demonstrate excellent performance in suppressing boundary effects induced from circularly shifted training samples. However, SRDCF have two drawbacks which may be the bottlenecks for further performance improvement. First, SRDCF needs to construct an element-wise regularization weight map which can lead to poor tracking performance without careful tunning. Second, SRDCF does not guarantee zero correlation filter values outside the target bounding box. These small but nonzero filter values away from the filter center hardly contribute to target location but induce boundary effects. To tackle these drawbacks, we revisit the standard SRDCF formulation and introduce padless correlation filters (PCFs) which totally remove boundary effects. Compared with SRDCF that penalizes filter values with spatial regularization weights, PCF directly guarantee zero filter values outside the target bounding box with a binary mask. Experimental results on the OTB2013, OTB2015 and VOT2016 data sets demonstrate that PCF achieves real-time frame-rates and favorable tracking performance compared with state-of-the-art trackers.This work was supported by the National Natural Science Foundation of China (NSFC) under Project 41601487