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Sidelobe Mitigation in Noise Radar Using Sparse signal processing

IEEE AEROSPACE AND ELECTRONIC SYSTEMS MAGAZINE 2020年第9期35卷 32-40页

作者： de Martin, Lilian van Rossum, Wim Ribeiro, Diogo Anitori, Laura TNO Radar Technol The Hague Netherlands

It is well known that noise-like waveforms have the inherent property of suppressing ambiguities due to their nonrepetitive nature, as opposed to the more classical linear frequency modulated (LFM) pulsed waveforms. This aperiodicity also improves the robustness against jammers, making these waveforms harder to detect and classify by an enemy. However, noise waveforms are Doppler sensitive, requiring the use of Doppler-dependent matched filters for proper range compression. Also, when processed with classical matched filters, noise waveforms have sidelobes that spread out across the whole range-Doppler plane, potentially masking weak targets in the presence of strong ones. A possible solution to reduce the sidelobe interference is to use sparse signal processing (SSP). In this article, we demonstrate with simulated and experimental data how noise waveforms can be used in combination with SSP for unambiguous detection of strong as well as weak targets, improving upon the performance of more conventional LFM pulsed waveforms.

关键词： Doppler effect Radar Matching pursuit algorithms Radar signal processing signal processing algorithms Computational modeling Matched filters Frequency modulation Jamming

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Convex Optimization in signal processing

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IEEE signal processing MAGAZINE 2010年第3期27卷 19-+页

作者： Eldar, Yonina C. Luo, Zhi-Quan Ma, Wing-Kin Palomar, Daniel P. Sidiropoulos, Nicholas D. Spanish University for Distance Education Ciudad Universitaria Madrid Spain Technical University of Valencia Alcoi Spain Burgos University Burgos Spain DMR Consulting Madrid Spain Spanish University for Distance Education Ciudad Úniversitaria Madrid Spain

In recent years, we have witnessed technical breakthroughs in a wide variety of topics where the key to success is the use of convex optimization. In fact, convex optimization has now emerged as a major signal processing tool that has made a significant impact on numerous problems previously considered intractable. Considering the foundational nature and potential impact of convex optimization in signal processing, we have put together this special issue that aims to provide tutorials of convex optimization techniques (including available software) and various successful signal processing applications. Our goal is not only to contribute to the diffusion of recent developments in this research area within the signal processing community, but also to spur further advances in and applications of convex optimization for signal processing.

关键词： signal processing Finite impulse response filter signal processing algorithms Array signal processing Optimization methods Game theory Application software Design optimization Iterative algorithms MIMO

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Kernel-Based Learning for Statistical signal processing in Cognitive Radio Networks

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IEEE signal processing MAGAZINE 2013年第4期30卷 126-136页

作者： Ding, Guoru Wu, Qihui Yao, Yu-Dong Wang, Jinlong Chen, Yingying PLA Univ Sci & Technol Coll Commun Engn Nanjing Jiangsu Peoples R China Southeast Univ Nanjing Jiangsu Peoples R China PLA Univ Sci & Technol Nanjing Jiangsu Peoples R China Stevens Inst Technol Hoboken NJ 07030 USA Carleton Univ Ottawa ON K1S 5B6 Canada Spar Aerosp Ltd Montreal PQ Canada Qualcomm Inc San Diego CA USA IEEE Nanjing Sect Nanjing Jiangsu Peoples R China Stevens Inst Technol Dept Elect & Comp Engn Hoboken NJ USA Alcatel Lucent Holmdel NJ USA

Kernel-based learning (KBL) methods have recently become prevalent in many engineering applications, notably in signal processing and communications. The increased interest is mainly driven by the practical need of being able to develop efficient nonlinear algorithms, which can obtain significant performance improvements over their linear counterparts at the price of generally higher computational complexity. In this article, an overview of applying various KBL methods to statistical signal processing-related open issues in cognitive radio networks (CRNs) is presented. It is demonstrated that KBL methods provide a powerful set of tools for CRNs and enable rigorous formulation and effective solutions to both long-standing and emerging design problems.

关键词： Machine learning Learning systems Kernel Cognitive radio signal processing algorithms Nonlinear algorithms

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Advanced signal processing Methods for Characterization of Schizophrenia

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2021年第4期68卷 1123-1130页

作者： Masychev, Kirill Ciprian, Claudio Ravan, Maryam Reilly, James P. MacCrimmon, Duncan New York Inst Technol Dept Comp Sci New York NY USA New York Inst Technol Dept Elect & Comp Engn New York NY 10023 USA McMaster Univ Dept Elect & Comp Engn Hamilton ON L8S 4L8 Canada McMaster Univ Dept Psychiat Hamilton ON Canada

Objective: Schizophrenia is a severe mental disorder associated with nerobiological deficits. Auditory oddball P300 have been found to be one of the most consistent markers of schizophrenia. The goal of this study is to find quantitative features that can objectively distinguish patients with schizophrenia (SCZs) from healthy controls (HCs) based on their recorded auditory odd-ball P300 electroencephalogram (EEG) data. Methods: Using EEG dataset, we develop a machine learning (ML) algorithm to distinguish 57 SCZs from 66 HCs. The proposed ML algorithm has three steps. In the first step, a brain source localization (BSL) procedure using the linearly constrained minimum variance (LCMV) beamforming approach is employed on EEG signals to extract source waveforms from 30 specified brain regions. In the second step, a method for estimating effective connectivity, referred to as symbolic transfer entropy (STE), is applied to the source waveforms. In the third step the ML algorithm is applied to the STE connectivity matrix to determine whether a set of features can be found that successfully discriminate SCZ from HC. Results: The findings revealed that the SCZs have significantly higher effective connectivity compared to HCs and the selected STE features could achieve an accuracy of 92.68%, with a sensitivity of 92.98% and specificity of 92.42%. Conclusion: The findings imply that the extracted features are from the regions that are mainly affected by SCZ and can be used to distinguish SCZs from HCs. Significance: The proposed ML algorithm may prove to be a promising tool for the clinical diagnosis of schizophrenia.

关键词： Entropy Electroencephalography Feature extraction Brain Standards signal processing algorithms Location awareness Effective connectivity electroencephalography (EEG) EEG beamforming machine learning odd-ball paradigm schizophrenia symbolic transfer entropy

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signal processing and Optimization Tools for Conference Review and Session Assignment

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IEEE signal processing MAGAZINE 2015年第3期32卷 141-155页

作者： Sidiropoulos, Nicholas D. Tsakonas, Efthymios E. Univ Minnesota Minneapolis MN 55455 USA IEEE Signal Proc Commun & Networking Tech Comm Santa Clara CA USA

Anyone who has served as a technical program committee (TPC) chair for a conference (or program manager for a funding agency) understands that paper (or proposal panel) review assignment is a demanding job that takes a lot of time, and reviewers are rarely satisfied with the end results. This article presents signal processing tools for two critical ?mass assignment? tasks: assigning papers (or proposals) to reviewers in a way that matches reviewing expertise to scientific content while respecting the reviewers? capacity constraints and splitting accepted papers (or submitted proposals) to sessions (panels) while adhering to session (panel) capacity constraints. The basic idea is to use feature vectors to represent papers and reviewers. Features can be key words or phrases (e.g., optimization or sensor networks) or other types of attributes (e.g., timeliness). This viewpoint enables optimal assignment problem formulations that make sense from a scientific and practical point of view. While optimal solutions are hard to compute for a large number of papers and reviewers, high-quality approximate solutions of moderate complexity are developed here using familiar signal processing and optimization tools. These algorithmic solutions easily outperform days of expert manual work as demonstrated in experiments with real conference data.

关键词： Meetings Complexity theory Optimization Publishing signal processing algorithms Approximation methods Approximation algorithms Professional communication

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Algorithm Unrolling: Interpretable, Efficient Deep Learning for signal and Image processing

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IEEE signal processing MAGAZINE 2021年第2期38卷 18-44页

作者： Monga, Vishal Li, Yuelong Eldar, Yonina C. Penn State Univ Sch Elect Engn University Pk PA 16802 USA Penn State Univ Elect Engn & Comp Sci Fac University Pk PA 16802 USA Amazon Lab 126 San Jose CA 94089 USA Weizmann Inst Sci Dept Math & Comp Sci IL-7610001 Rehovot Israel Weizmann Inst Sci Ctr Biomed Engn & Signal Proc IL-7610001 Rehovot Israel MIT Cambridge MA 02139 USA Broad Inst Cambridge MA USA Duke Univ Durham NC 27706 USA Israel Acad Sci & Humanities Jerusalem Israel

Deep neural networks provide unprecedented performance gains in many real-world problems in signal and image processing. Despite these gains, the future development and practical deployment of deep networks are hindered by their black-box nature, i.e., a lack of interpretability and the need for very large training sets. An emerging technique called algorithm unrolling, or unfolding, offers promise in eliminating these issues by providing a concrete and systematic connection between iterative algorithms that are widely used in signal processing and deep neural networks. Unrolling methods were first proposed to develop fast neural network approximations for sparse coding. More recently, this direction has attracted enormous attention, and it is rapidly growing in both theoretic investigations and practical applications. The increasing popularity of unrolled deep networks is due, in part, to their potential in developing efficient, high-performance (yet interpretable) network architectures from reasonably sized training sets.

关键词： Training data Systematics Neural networks signal processing algorithms Performance gain Network architecture Deep learning Machine learning

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signal-processing USING IMPLICIT PHASE

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS 1985年第2期32卷 150-159页

作者： MANRY, MT Department of Electrical Engineering University of Texas Arlington Arlington TX USA

A frequency-domain technique is presented for measuring the average location of the energy of, a sequence. Several properties of the measure are listed and proved. A simple time-domain approximation to the measure is derived and an upper bound on the error of approximation is found. The effects of noise on the measure are thoroughly analyzed. The measure can be used to find and remove the linear part of a Fourier transform's phase without any arctangent operations. it can be used to locate individual signal components which overlap in time but which are disjoint in frequency. The measure may be used in the estimation of time delays or time constants. The algorithms described in this paper require no inverse DFT's and, therefore, run twice as fast as algorithms which utilize FFT convolution.

关键词： signal processing Frequency measurement Time measurement signal processing algorithms Energy measurement Time domain analysis Upper bound Noise measurement Phase measurement Delay estimation

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Integrated Sensing and Communication signal processing Based on Compressed Sensing Over Unlicensed Spectrum Bands

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IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 2024年第5期10卷 1801-1816页

作者： Liu, Haotian Wei, Zhiqing Li, Fengyun Lin, Yuewei Qu, Hanyang Wu, Huici Feng, Zhiyong Beijing Univ Posts & Telecommun Sch Informat & Commun Beijing 100088 Peoples R China Qingdao Univ Sci & Technol Coll Informat Sci & Technol Qingdao 266061 Shandong Peoples R China ChinaMobile Zijin Innovat Inst Wireless Cloud Network R&D Dept Nanjing 210000 Jiangsu Peoples R China

As a promising key technology of 6th generation (6G) mobile communication system, integrated sensing and communication (ISAC) technology aims to make full use of spectrum resources to enable the functional integration of communication and sensing. The ISAC-enabled mobile communication system regularly operate in non-continuous spectrum bands due to crowded licensed frequency bands. However, the conventional sensing algorithms over non-continuous spectrum bands have disadvantages such as reduced peak-to-sidelobe ratio (PSLR) and degraded anti-noise performance. Facing this challenge, we propose a high-precision ISAC signal processing algorithm based on compressed sensing (CS) in this paper. By integrating the resource block group (RBG) configuration information in 5th generation new radio (5G NR) and channel information matrices, we can dynamically and accurately obtain power estimation spectra. Moreover, we employ the fast iterative shrinkage-thresholding algorithm (FISTA) to address the reconstruction problem and utilize K-fold cross validation (KCV) to obtain optimal parameters. Simulation results show that the proposed algorithm has lower sidelobes or even zero sidelobes compared with conventional sensing algorithms. Meanwhile, compared with the improved 2D FFT algorithm and conventional 2D FFT algorithm, the proposed algorithms in this paper have a maximum improvement of 54.66% and 84.36% in range estimation accuracy, and 41.54% and 97.09% in velocity estimation accuracy, respectively.

关键词： Sensors signal processing algorithms OFDM Symbols Time-frequency analysis 5G mobile communication Radar Compressed sensing (CS) integrated sensing and communication (ISAC) machine learning (ML) non-continuous spectrum non-continuous OFDM (NC-OFDM) signal processing unlicensed spectrum bands

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Low-power signal processing system design for wireless applications

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IEEE PERSONAL COMMUNICATIONS 1998年第3期5卷 20-31页

作者： Meng, TH Hung, AC Tsern, EK Gordon, BM Stanford Univ Dept Elect Engn Stanford CA 94305 USA Chromat Res Inc Sunnyvale CA USA Rambus Inc Mountain View CA USA Silicon Real Federal Way WA USA

Our present ability to work with most multimedia data has been confined to a wired environment, requiring both the data source and the receiver to be physically connected to a power supply and a wired communication link. This article describes the design principles applicable to wireless signal processing systems, using a portable video-on-demand system as an example. The discussion will focus on both the algorithm and circuit design techniques developed for implementing a low-power video compression/decompression system at power levels that are two orders of magnitude below existing solutions. This low-power video compression system not only provides a compression efficiency similar to industry standards, but also maintains a high degree of error tolerance to guard against the transmission errors often encountered in wireless communication.

关键词： signal processing signal processing algorithms Video compression Power supplies signal design Video signal processing Circuit synthesis Communication industry Communication standards Wireless communication

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HIDDEN MARKOV MODEL signal-processing IN PRESENCE OF UNKNOWN DETERMINISTIC INTERFERENCES

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 1993年第1期38卷 146-152页

作者： KRISHNAMURTHY, V MOORE, JB CHUNG, SH AUSTRALIAN NATL UNIV DEPT CHEMCANBERRAACT 2601AUSTRALIA

In this note, expectation maximization (EM) algorithms are used to extract discrete-time, finite-state Markov signals imbedded in a mixture of Gaussian white-noise and deterministic signals of known functional form with unknown parameters. We obtain maximum likelihood estimates of the Markov state levels, state estimates, transition probabilities, and also of the parameters of the deterministic signals. Specifically, we consider two important types of deterministic signals: periodic, or almost periodic signals with unknown frequency components, amplitudes, and phases;polynomial drift in the states of the Markov process with the coefficients of the polynomial unknown. The techniques developed here along with the supporting theory appear more elegant and powerful than ad hoc heuristic alternatives. An illustrative biophysical application for extracting ionic channel currents in cell membranes in the presence of white Gaussian noise and alternating current (AC) ''hum'' is included.

关键词： Hidden Markov models signal processing signal processing algorithms State estimation Polynomials Maximum likelihood estimation Parameter estimation Frequency Markov processes Cells (biology)

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