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Spotlight Synthetic Aperture Radar Signal Processing Algorithms

We propose in this letter a new subspace learning method, called uncorrelated discriminant nearest feature line analysis (UDNFLA), for face recognition. Motivated by the fact that existing nearest feature line (NFL) can effectively characterize the geometrical information of face samples, and uncorrelated features are desirable for many pattern analysis applications, we propose using the NFL metric to seek a feature subspace such that the within-class feature line (FL) distances are minimized and between-class FL distances are maximized simultaneously in the reduced subspace, and impose an uncorrelated constraint to make the extracted features statistically uncorrelated. Experimental results on two widely used face databases demonstrate the efficacy of the proposed method.

Uncorrelated Discriminant Nearest Feature Line Analysis for Face Recognition

Infrared and visible image fusion is an important problem in the field of image fusion which has been applied widely in many fields. To better preserve the useful information from source images, in this paper, we propose a novel image fusion method based on latent low-rank representation(LatLRR) which is simple and effective. Firstly, the source images are decomposed into low-rank parts(global structure) and saliency parts(local structure) by LatLRR. Then, the lowrank parts are fused by weighted-average strategy to preserve more contour information. Then, the saliency parts are simply fused by sum strategy which is a efficient operation in this fusion framework. Finally, the fused image is obtained by combining the fused low-rank part and the fused saliency part. Compared with other fusion methods experimentally, the proposed method has better fusion performance than stateof-the-art fusion methods in both subjective and objective evaluation. The Code of our fusion method is available at this https URL Infrared visible latlrr

Infrared and visible image fusion using Latent Low-Rank Representation.

Nearest feature line-based subspace analysis is first proposed in this paper. Compared with conventional methods, the newly proposed one brings better generalization performance and incremental analysis. The projection point and feature line distance are expressed as a function of a subspace, which is obtained by minimizing the mean square feature line distance. Moreover, by adopting stochastic approximation rule to minimize the objective function in a gradient manner, the new method can be performed in an incremental mode, which makes it working well upon future data. Experimental results on the FERET face database and the UCI satellite image database demonstrate the effectiveness.

Iterative Subspace Analysis Based on Feature Line Distance

Radio frequency interference (RFI) is a major issue in accurate remote sensing by a synthetic aperture radar (SAR) system, which poses a great hindrance to raw data collection, image formation, and subsequent interpretation process. This paper provides a comprehensive study of the RFI mitigation techniques applicable for an SAR system. From the view of spectrum allocation, possible terrestrial and spaceborne RFI sources to SAR system and their geometry are analyzed. Typical signal models for various RFI types are provided, together with many illustrative examples from real measured data. Then, advanced signal processing techniques for removing RFI are reviewed. Advantages and drawbacks of each approach are discussed in terms of their applicability. Discussion on the future trends are provided from the perspective of cognitive, integrated, and adaptive. This review serves as a reference for future work on the implementation of the most suitable RFI mitigation scheme for an air-borne or space-borne SAR system.

Mitigation of Radio Frequency Interference in Synthetic Aperture Radar Data: Current Status and Future Trends

A new feature extraction method, called nearest neighbour line nonparametric discriminant analysis (NNL-NDA), is proposed. The previous nonparametric discriminant analysis methods only use point-to-point distance to measure the class difference. In NNL-NDA, point-to-line distance with nearest neighbour line (NNL) theory is adopted, and thereby more intrinsic structure information of training samples is preserved in the feature space. NNL-NDA does not assume that the class densities belong to any particular parametric family nor encounter the singularity difficulty of the within-class scatter matrix. Experimental results on ORL face database demonstrate the effectiveness of the proposed method.

Nearest neighbour line nonparametric discriminant analysis for feature extraction

Radio frequency interference (RFI) can severely degrade the image quality for synthetic aperture radar (SAR). Traditional sparse recovery-based RFI suppression methods construct a joint dictionary to represent both the time-domain RFI and the useful target echo (UTE), thus, transforming the task of RFI suppression into a signal recovery problem. However, these methods generally have poor performance when the one-dimensional (1-D) range profiles are nonsparse. To overcome this problem, two RFI suppression algorithms are proposed based on a modified block sparse Bayesian learning (BSBL). In the first algorithm, the vector to be recovered is divided into several blocks of identical size. The Bayesian hyper-parameters corresponding to the RFI and UTE are learned separately by exploiting the temporal intrablock correlation, and then the nonsparse vector can be successfully recovered. In the second algorithm, the dictionary is adaptively tuned during the iteration process of BSBL, thus reducing the computational load. After recovering the UTE, a well-focused 2-D image can be obtained by traditional SAR imaging algorithms. With the use of properties of both the RFI and UTE, and by exploiting the intrablock correlation, the proposed RFI suppression methods outperform other sparse recovery methods, especially when the range profile is nonsparse. Simulation results demonstrate the superior performance of the proposed algorithms.

Radio Frequency Interference Suppression for SAR via Block Sparse Bayesian Learning

The performance of synthetic aperture radar is vulnerable to radio frequency interference (RFI). In many situations, the RFI has a low-rank property, since the frequency bands occupied by RFI usually remain stable during a short slow time period. Therefore, low-rank representation (LRR)-based methods can be applied to separate RFI and signal of interest (SOI), by minimizing the rank of RFI components with a regularization constraint to protect SOI. However, traditional methods use the sparsity of the raw data or range profile to formulate the regularization term, which fails to describe the properties of SOI accurately. In addition to the sparse property of range profiles, this article explores the common patterns hidden in the range profiles and proposes two new LRR-based RFI suppression optimization models with a well-designed regularization term to describe such common sparsity to protect the SOI. Four methods are proposed to solve the optimization problems based on the alternating direction multiplier (ADM) method, which provides tradeoff between efficiency and accuracy. Compared with traditional LRR-based RFI suppression methods, the proposed methods make a more precise description of the features of SOI, therefore can better protect the information of SOI during the RFI suppression process and improves the imaging quality. The superior performance of the proposed method is validated by measured data in both sparse and nonsparse scenes.

Enhanced LRR-Based RFI Suppression for SAR Imaging Using the Common Sparsity of Range Profiles for Accurate Signal Recovery

Deceptive jamming can severely degrade synthetic aperture radar (SAR) image quality by introducing high-fidelity false targets. In this letter, a simultaneous deceptive jamming suppression and target reconstruction method for a single-channel SAR system is proposed. The signal model is formulated by constructing a joint dictionary based on different time–frequency distributions of the actual targets and false targets. Then, an efficient algorithm is proposed based on the alternating direction method of multipliers (ADMMs) to simultaneously recover the actual and false targets. Several strategies are also proposed to accelerate the computation. Compared with other existing single-channel SAR deceptive jamming suppression methods, the proposed method has lower reconstruction error and computational load. Simulation results demonstrate the superior performance of the proposed algorithm.

Jianchao Yang

Papers

Nearest neighbour line nonparametric discriminant analysis for feature extraction

Radio Frequency Interference Suppression for SAR via Block Sparse Bayesian Learning

Enhanced LRR-Based RFI Suppression for SAR Imaging Using the Common Sparsity of Range Profiles for Accurate Signal Recovery

An Efficient Method for Single-Channel SAR Target Reconstruction Under Severe Deceptive Jamming

Weak maneuvering target detection in random pulse repetition interval radar