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Time-Domain Azimuth-Variant MOCO Algorithm for Airborne SAR Imaging

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2022年 19卷 1页

作者： Meng, Zhichao Zhang, Lei Li, Jun Lu, Jingyue Li, Yachao Sun Yat Sen Univ Sch Elect & Commun Engn Shenzhen 518107 Peoples R China Beijing Inst Radio Measurement Beijing 100854 Peoples R China Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China

Current subaperture-based azimuth-variant motion compensation (MOCO) algorithms for synthetic aperture radar (SAR) imagery usually suffer from the challenge of keeping high precision and efficiency simultaneously. In this letter, a novel MOCO approach is developed to precisely correct the azimuth-variant motion errors. The proposed algorithm applies a time-domain filter to implement the precise subaperture-to-pulse correction, providing a promising azimuth-variant phase correction. Extensive experiments demonstrate the superiorities of the proposal with real-measured high-squint SAR data.

关键词： Doppler effect Synthetic aperture radar Time-domain analysis Azimuth signal processing algorithms Radar polarimetry Geometry Azimuth-variant motion compensation (MOCO) subaperture-to-pulse synthetic aperture radar (SAR)

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PDD-Based Decoder for LDPC Codes With Model-Driven Neural Networks

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IEEE COMMUNICATIONS LETTERS 2022年第11期26卷 2532-2536页

作者： Liu, Yihao Zhao, Ming-Min Wang, Chan Lei, Ming Zhao, Min-Jian Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China IPCAN Zhejiang Prov Key Lab Informat Process ing Commun Hangzhou 310027 Peoples R China

In this work, we develop a double-loop iterative decoding algorithm for low density parity check (LDPC) codes based on the penalty dual decomposition (PDD) framework. We utilize the linear programming (LP) relaxation and the penalty method to handle the discrete constraints and the over-relaxation method is employed to improve convergence. Then, we unfold the proposed PDD decoding algorithm into a model-driven neural network, namely the learnable PDD decoding network (LPDN). We turn the tunable coefficients and parameters in the proposed PDD decoder into layer-dependent trainable parameters which can be optimized by gradient descent-based methods during network training. Simulation results demonstrate that the proposed LPDN with well-trained parameters is able to provide superior error-correction performance with much lower computational complexity as compared to the PDD decoder.

关键词： Decoding Iterative decoding signal processing algorithms Neural networks Computational complexity Convex functions Convergence LDPC codes penalty dual decomposition deep learning deep unfolding model-driven

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Efficient ADMM-Based algorithms for Convolutional Sparse Coding

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IEEE signal processing LETTERS 2022年 29卷 389-393页

作者： Veshki, Farshad Vorobyov, Sergiy Aalto Univ Dept Signal Proc & Acoust Espoo 02150 Finland

Convolutional sparse coding improves on the standard sparse approximation by incorporating a global shift-invariant model. The most efficient convolutional sparse coding methods are based on the alternating direction method of multipliers and the convolution theorem. The only major difference between these methods is how they approach a convolutional least-squares fitting subproblem. In this letter, we present a novel solution for this subproblem, which improves the computational efficiency of the existing algorithms. The same approach is also used to develop an efficient dictionary learning method. In addition, we propose a novel algorithm for convolutional sparse coding with a constraint on the approximation error. Source codes for the proposed algorithms are available online.

关键词： Convolution signal processing algorithms Dictionaries Convolutional codes Approximation algorithms Optimization Image coding Convolutional sparse coding constrained sparse approximation dictionary learning alternating direction method of multipliers

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Measurement Matrix Design for Sample-Efficient Binary Compressed Sensing

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IEEE signal processing LETTERS 2022年 29卷 1307-1311页

作者： Sarangi, Pulak Pal, Piya Univ Calif San Diego Dept Elect & Comp Engn La Jolla CA 92093 USA

This letter investigates the problem of recovering a binary-valued signal from compressed measurements of its convolution with a known finite impulse response filter. We show that it is possible to attain optimum sample complexity for exact recovery (in absence of noise) with a computationally efficient algorithm. We achieve this by adopting an algorithm-measurement co-design strategy where the measurement matrix is designed as a function of the filter, such that the recovery of binary signals with arbitrary sparsity is possible by using a sequential decoding algorithm. Such a filter-dependent sampler design can overcome the computational challenges associated with enforcing binary constraints, and enable us to operate in "extreme compression" regimes, where the number of measurements can be much smaller than the sparsity level.

关键词： Finite impulse response filters signal processing algorithms Filtering algorithms Decoding Computational efficiency Compressed sensing Matching pursuit algorithms Binary signals compressed sensing extreme compression measurement matrix design sequential decoding

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Asymmetric Correntropy for Robust Adaptive Filtering

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2022年第3期69卷 1922-1926页

作者： Chen, Badong Xie, Yuqing Li, Zhuang Li, Yingsong Ren, Pengju Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian 710049 Peoples R China Xian Microelect Technol Inst Xian 710600 Peoples R China Harbin Engn Univ Coll Informat & Commun Engn Harbin 150001 Peoples R China Chinese Acad Sci Natl Space Sci Ctr Beijing 100190 Peoples R China

In recent years, correntropy has been successfully applied to robust adaptive filtering to eliminate adverse effects of impulsive noises or outliers. Correntropy is generally defined as the expectation of a Gaussian kernel between two random variables. This definition is reasonable when the error between the two random variables is symmetrically distributed around zero. For the case of asymmetric error distribution, the symmetric Gaussian kernel is however inappropriate and cannot adapt to the error distribution well. To address this problem, in this brief we propose a new variant of correntropy, named asymmetric correntropy, which uses an asymmetric Gaussian model as the kernel function. In addition, a robust adaptive filtering algorithm based on asymmetric correntropy is developed and its steady-state convergence performance is analyzed. Simulations are provided to confirm the theoretical results and good performance of the proposed algorithm.

关键词： Kernel Adaptation models signal processing algorithms Steady-state Convergence Random variables Probability density function Correntropy asymmetric correntropy maximum asymmetric correntropy criterion (MACC) robust adaptive filtering impulsive noise

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Limited-Memory Receive Filter Design for Massive MIMO Radar in signal-Dependent Interference

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IEEE signal processing LETTERS 2022年 29卷 1536-1540页

作者： Deng, Minglong Cheng, Ziyang He, Zishu Univ Elect Sci & Technol China UESTC Sch Informat & Commun Engn Chengdu 611731 Peoples R China

This letter proposes a limited-memory receive filter design to maximize the output signal-to-interference-plus-noise ratio (SINR) of massive multiple-input multiple-output (MIMO) radar. The associated problem is a minimum variance distortionless response (MVDR) one which can be analytical solved, but the computation of the MVDR solution would be prohibitive in the context of massive MIMO radar, due to the huge memory consumption. To this end, we propose a low-complexity algorithm that exploits a reparameterization method to avoid the significant memory consumption, and solves the resulting problem with several simple algebraic operations. We show that the memory consumption of the proposed algorithm depends on the number of interference sources. Therefore, for the scenario with a large number of interference sources, we extend the proposed algorithm by the alternating direction method of multipliers (ADMM), which promotes a further memory reduction. Finally, numerical simulations are conducted to demonstrate the efficiency of the proposed algorithm.

关键词： Interference Memory management signal to noise ratio Radar signal processing algorithms Massive MIMO MIMO radar Massive MIMO radar SINR maximization receive filter design

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Split-Radix Algorithm for the Discrete Hirschman Transform

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IEEE signal processing LETTERS 2022年 29卷 199-203页

作者： Xue, Dingli DeBrunner, Linda DeBrunner, Victor Huang, Zhen Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Beijing 100084 Peoples R China Florida State Univ Dept Elect & Comp Engn Tallahassee FL 32310 USA

With the best basis function that compactly describes a discrete-time signal, the Discrete Hirschman Transform (DHT) has been proved to perform better than the Discrete Fourier Transform (DFT) in terms of high resolution and computational complexity. It is reasonable to develop fast algorithms for the DHT computation since the DHT has applied to multiple signal processing applications. In this letter, we propose a split-radix DHT (SRDHT) including mathematical decomposition and comparison of computation complexity. The SRDHT is computationally superior to the DFT and performs more efficiently than our previously developed radix-2/-4 DHTs, with further reduced arithmetic operations. We regard this proposed SRDHT as a more attractive candidate to compute the DHT for those existing and future Hirschman-based applications.

关键词： signal processing algorithms Discrete Fourier transforms Shape Arithmetic Hardware Convolution Computational complexity DHT HOT Hirschman split-radix FFT algorithm complexity signal processing convolution

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Robust Blind Equalization Algorithm Using Convolutional Neural Network

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IEEE signal processing LETTERS 2022年 29卷 1569-1573页

作者： Mei, Ruru Wang, Zhugang Hu, Wanru Chinese Acad Sci Key Lab Elect & Informat Technol Space Syst Natl Space Sci Ctr Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Natl Space Sci Ctr Beijing 100190 Peoples R China

In this letter, we introduce a novel blind equalization algorithm based on the convolutional neural network (CNN), to improve the bit error rate (BER) performance of equalizers against the multipath fading effect and nonlinear distortion. In contrast to existing neural network (NN)-based blind equalization algorithms, the proposed algorithm performs equalization and soft demapping jointly, which allows it to obtain soft bits directly from received data. In addition, the input preprocessing module is used to reorganize the received data to fully exploit the feature information of the received signals. Afterwards, the two-dimensional (2D) convolutions can be operated on a three-dimensional (3D) input array obtained by input preprocessing to recover the transmitted bits. Simulation results show that the proposed algorithm outperforms other CNN-based blind equalization algorithms, in terms of BER performance, under a wide range of channel models, signal-to-noise (SNR) levels and modulation schemes. Strikingly, the BER obtained by the proposed algorithm approaches the theoretical BER of multiple quadrature amplitude modulation (M-QAM) in linear channels.

关键词： signal processing algorithms Blind equalizers Symbols Convolutional neural networks Convolution Training Feature extraction Blind equalization convolutional neural network multipath fading effect nonlinear effect

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Implementation of a Passive Acoustic Barrier for Surveillance

Implementation of a Passive Acoustic Barrier for Surveillanc...

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OCEANS Conference

作者： Dias, Andre Rodrigues Santos, Nuno Pessanha Lobo, Victor Portuguese Naval Acad Escola Naval Portuguese Naval Acad Res Ctr CINAV Almada Portugal Portuguese Mil Acad Acad Militar Portuguese Mil Acad Res Ctr CINAMIL Lisbon Portugal Univ Nova Lisboa NOVA Informat Management Sch Nova IMS Lisbon Portugal

ISBN: (纸本)9798350332261

With the end of the cold war, the interest in underwater warfare decreased dramatically. However, recent developments have brought underwater warfare back to center stage. Anti-submarine warfare is always one of the major concerns of a navy since it is difficult to detect an enemy submarine in the vast ocean. Conjugating the recent developments in unmanned vehicles and active and passive acoustic surveillance, we can perform better data fusion and increase our knowledge about the events occurring in our waters. The processed data originating from acoustic surveillance can potentially be an essential source of naval intelligence. An acoustic barrier can perform this detection with success. Still, these systems require highly qualified personnel to operate, present a costly infrastructure, and are hard to implement and maintain. Data fusion from multiple sources, and even from low-cost sensors with noisy measures, are a promising solution, especially if resource optimization is a priority. The implementation described in this paper is intended to be proof of concept of a low-cost implementation in shallow waters that can be easily expanded and evolved to different scenarios. The preliminary results obtained confirm that this is a viable solution.

关键词： Surveillance Acoustics Acoustic applications Multiple signal classification signal processing algorithms

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ENF Based Robust Media Time-Stamping

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IEEE signal processing LETTERS 2022年 29卷 1963-1967页

作者： Vatansever, Saffet Dirik, Ahmet Emir Memon, Nasir Bursa Tech Univ Dept Mechatron Engn TR-16333 Bursa Turkey Bursa Uludag Univ Fac Engn Dept Comp Engn TR-16059 Bursa Turkey NYU Tandon Sch Engn Dept Comp Sci & Engn New York NY 11201 USA

Electric Network Frequency (ENF) continuously fluctuates around a nominal value (50/60 Hz) due to a persistent imbalance between supplied and demanded power. In certain circumstances, ENF gets intrinsically embedded into audio and video recordings and can be extracted from these recordings. Consequently, ENF can be used in a number of media forensic applications, such as verifying the time of recording of the media. In this work, a robust media time-stamping approach is proposed for media whose ENF content is relatively contaminated. It essentially entails two procedures: first, detecting all useful, i.e., considerably accurate, samples of an estimated ENF signal, and then applying an adapted normalized cross-correlation process that is designed for exploiting just the selected ENF portions based on a binary mask of the identified accurate samples. Experimental results show that the proposed approach provides significantly increased performance.

关键词： Media Noise measurement Forensics Recording Harmonic analysis Estimation signal processing algorithms ENF electric network frequency media forensics multimedia forensics time-of-recording time-stamping

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