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Asilomar Conference on signals, Systems & Computers

作者： C.S. Marino P.M. Chau ORINCON Corporation San Diego CA USA University of California San Diego La Jolla CA USA

As digital cellular communications transforms personal communication and its interaction within society increases, the complexity of the communications system increases to counter variable SNR environments. One method to reduce system complexity is to replace computationally expensive components with low-complexity ones. By replacing the conventional signal autocorrelation, with combined reduced complexity correlation estimator and variable bit quantization, considerable computational savings are achieved. The accuracy of these algorithms is of concern especially in applications, which are resolution sensitive such as in the direction of arrival estimate produced by beamforming. Simulations show that the reduced complexity quantized correlation estimators can achieve comparable results to the convention method. For the beampattern the quadruplex using 16 and 8 bit wordlengths showed similar beamwidths and were within 3% for 16 bits and 10% for 8 bit wordlength DOA compared to the conventional method full floating point.

关键词： Digital signal processing Digital communication signal processing algorithms Array signal processing Field programmable gate arrays Hardware Antenna arrays Direction of arrival estimation Equations Counting circuits

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Centralized and Distributed Online Learning for Sparse Time-Varying Optimization

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2021年第6期66卷 2542-2557页

作者： Fosson, Sophie M. Politecn Torino Dipartimento Automat & Informat DAUIN I-10129 Turin Italy

The development of online algorithms to track time-varying systems has drawn a lot of attention in the last years, in particular in the framework of online convex optimization. Meanwhile, sparse time-varying optimization has emerged as a powerful tool to deal with widespread applications, ranging from dynamic compressed sensing to parsimonious system identification. In most of the literature on sparse time-varying problems, some prior information on the system's evolution is assumed to be available. In contrast, in this article, we propose an online learning approach, which does not employ a given model and is suitable for adversarial frameworks. Specifically, we develop centralized and distributed algorithms, and we theoretically analyze them in terms of dynamic regret, in an online learning perspective. Furthermore, we propose numerical experiments that illustrate their practical effectiveness.

关键词： Optimization Heuristic algorithms Convex functions Time-varying systems Target tracking signal processing algorithms Distributed iterative soft thresholding (IST) Douglas– Rachford (DR) splitting dynamic regret online learning sparse optimization time-varying systems

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Efficient Coherent Integration Detection for Space-Moving Target With Complex Azimuth Doppler Ambiguity

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IEEE SENSORS JOURNAL 2024年第24期24卷 41174-41192页

作者： Zhang, Xin Wan, Jun Liu, Ruixin Guan, Weisen Zhang, Yangzhou Chen, Zhanye Huang, Yan Li, Qiang Wen, Cai Chongqing Univ Sch Microelect & Commun Engn Chongqing 400044 Peoples R China Chongqing Univ Chongqing Key Lab Space Informat Network & Intelli Chongqing 400044 Peoples R China Xidian Univ Hangzhou Inst Technol Hangzhou 311200 Zhejiang Peoples R China Southeast Univ State Key Lab Millimeter Waves Nanjing 210096 Peoples R China Southeast Univ Inst Electromagnet Space Nanjing 210096 Peoples R China Suzhou Lab Suzhou 215164 Peoples R China Southeast Univ Purple Mt Lab Nanjing 211100 Peoples R China Northwest Univ Sch Informat Sci & Technol Xian 710127 Peoples R China

The space-moving target (SMT) poses a great challenge for coherent integration detection because of the across range cell (ARC) and Doppler frequency broadening (DFB) induced by the relative motions between SMT and radar platform. Moreover, the intricate azimuth Doppler ambiguity complicates the coherent integration of SMT. To tackle these challenges, a proficient method for the coherent integration of SMT detection under complex azimuth Doppler ambiguity is introduced. First, a modified second-order keystone transform (MSoKT) operation based on the new deramp function with regulatory factor is presented to address the quadratic ARC (QARC) and Doppler spectrum ambiguity. Second, the linear ARC (LARC) is eliminated by the proposed novel processing procedure based on the improved range frequency reversal transform (IRFRT) and modified 2-D scaled Fourier transform (2DSCFT) without searching of Doppler ambiguity number. Third, the DFB is removed by the 1-D scaled Fourier transform (1DSCFT) with minimal computational overhead. After that, the well-focused result of SMT is obtained. The proposed method demonstrates computational efficiency, as its implementation involves only the fast Fourier transform (FFT) and complex multiplication operations, while also eliminating the need to search for Doppler ambiguity numbers. The presented method exhibits robustness against complex azimuth Doppler ambiguity and can effectively address the ambiguous speed sidelobe (ASSL). The multitarget scenario is further investigated, and a strategy for identifying spurious peaks caused by cross terms is proposed. Real data results are provided to validate the method's performance in scenarios involving both single and multiple targets.

关键词： Doppler effect Azimuth Fourier transforms Sensors Computational efficiency Spaceborne radar Fast Fourier transforms signal to noise ratio signal processing algorithms Radar detection Coherent integration complex azimuth Doppler ambiguity modified scaled Fourier transform modified second-order keystone transform (MSoKT) operation space-moving target (SMT) detection

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MULTICHANNEL ARMA PROCESSES

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IEEE TRANSACTIONS ON signal processing 1994年第4期42卷 898-913页

作者： SWAMI, A GIANNAKIS, G SHAMSUNDER, S UNOCAL CORP ANAHEIMCA 92807 UNIV VIRGINIA DEPT ELECT ENGNCHARLOTTESVILLEVA 22903 COLORADO STATE UNIV DEPT ELECT ENGNFT COLLINSCO 80523

Parametric modeling of multichannel time series is accomplished by using higher (than second) order statistics (HOS) of the observed nonGaussian data. Cumulants of vector processes are defined using a Kronecker product formulation, and consistency of their sample estimators is addressed. Identifiability results in connection with the HOS-based parameter estimation of causal and noncausal multivariate ARMA processes are established. Estimates of the parameters of causal ARMA models are obtained as the solution to a set of linear equations, whereas those of noncausal ARMA models are obtained as the solution to a cumulant matching algorithm. Conventional approaches based on second-order statistics can identify a multichannel system only to within post multiplication by a unimodular matrix. HOS-based methods yield solutions that are unique to within post-multiplication by an (extended) permutation matrix;additionally, the multiminimum phase assumption can be relaxed, and the observations may be contaminated with colored Gaussian noise. Frequency-domain methods for nonparametric system identification are discussed briefly. Simulations results validating the multichannel parameter estimation algorithms are provided.

关键词： Parameter estimation Parametric statistics Gaussian noise signal processing algorithms signal processing Equations Linear systems Minimization methods System identification

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FREQUENCY TRACKING USING HIDDEN MARKOV-MODELS WITH AMPLITUDE AND PHASE INFORMATION

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IEEE TRANSACTIONS ON signal processing 1993年第10期41卷 2965-2976页

作者： BARRETT, RF HOLDSWORTH, DA Materials Research Laboratory Department of Defence Defence Science and Technology Organisation Salisbury SA Australia

This paper demonstrates how the hidden Markov model (HMM) frequency tracker can be extended by the addition of amplitude and phase information. The HMM tracker as originally formulated uses a gate of spectral bins from FFT processing, and associates each cell with a state of the hidden Markov chain. A measurement sequence based on the output of a simple threshold detector forms the input to the HMM tracker. Two extensions to the original tracker are proposed. The first, the HMM/A tracker, incorporates the FFT amplitudes in the cells of the measurement sequence. The second, HMM/AP tracker, does not use a measurement sequence, but uses instead the FFT amplitude and phase values in all cells within the gate. A comparison of the results obtained in using the three HMM based trackers with simulated data is presented, and reveals that the extended trackers outperform the original HMM tracker. An analysis of the effect of parameter mismatch for the three HMM based trackers is presented. The use of the three HMM based trackers as detectors is also discussed.

关键词： Hidden Markov models signal processing algorithms Detectors Frequency estimation Random variables Phase measurement Background noise Frequency conversion Speech processing

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MULTISCALE AUTOREGRESSIVE PROCESSES .1. SCHUR-LEVINSON PARAMETRIZATIONS

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IEEE TRANSACTIONS ON signal processing 1992年第8期40卷 1915-1934页

作者： BASSEVILLE, M BENVENISTE, A WILLSKY, AS CNRS F-75005 PARISFRANCE INST NATL RECH INFORMAT & AUTOMAT F-78153 LE CHESNAYFRANCE MIT DEPT ELECT ENGN & COMP SCICAMBRIDGEMA 02139 MIT INFORMAT & DECIS SYST LABCAMBRIDGEMA 02139

In many applications (e.g., recognition of geophysical and biomedical signals and multiscale analysis of images), it is of interest to analyze and recognize phenomena occurring at different scales. The recently introduced wavelet transforms provide a time-and-scale decomposition of signals that offers the possibility of such analysis. At present, however, there is no corresponding statistical framework to support the development of optimal, multiscale statistical signal processing algorithms. In this paper we describe such a framework. The theory of multiscale signal representations leads naturally to models of signals on trees, and this provides the framework for our investigation. In particular, in this paper we describe the class of isotropic processes on homogeneous trees and develop a theory of autoregressive models in this context. This leads to generalizations of Schur and Levinson recursions, associated properties of the resulting reflection coefficients, and the initial pieces in a system theory for multiscale modeling.

关键词： Autoregressive processes signal analysis Wavelet transforms signal processing algorithms signal processing Image recognition Image analysis Wavelet analysis signal representations signal resolution

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Identification of Power System Oscillation Modes Using Empirical Wavelet Transform and Yoshida-Bertecco Algorithm

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IEEE ACCESS 2022年 10卷 48927-48935页

作者： Philip, Joice G. Yang, Yejin Jung, Jaesung Ajou Univ Dept Energy Syst Res Suwon 16499 South Korea

Oscillations occurring in the power system are one of the biggest threats to its secure operation. Although they occur rarely, these oscillations can cause severe damage to the power system if they are not detected at the earliest. Hence, this work focuses on identifying the parameters of oscillations in the power system using a combination of Empirical Wavelet Transform and Yoshida-Bertecco algorithm. As these oscillations occur rarely, a preprocessing method based on Teager Kaiser Energy Operator is used to check whether the signal under consideration contains any oscillation modes. The effectiveness of the proposed method is tested using a test signal, simulated power system signal, and PMU data from an actual power system under various levels of noise contamination. Further, the performance of the proposed method is compared with a VMD-Hilbert transform-based, Prony-based, and SSI-based methods in the literature. Results reveal the superiority of the proposed method irrespective of the parameters of the signal under consideration.

关键词： Oscillators Power systems Wavelet transforms Phasor measurement units Wavelet analysis signal to noise ratio signal processing algorithms Empirical wavelet transform power system oscillations Prony's method stochastic subspace identification Teager Kaiser energy operator variational mode decomposition

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Physics-Inspired Compressive Sensing: Beyond deep unrolling

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IEEE signal processing MAGAZINE 2023年第1期40卷 58-72页

作者： Zhang, Jian Chen, Bin Xiong, Ruiqin Zhang, Yongbing Peking Univ Shenzhen Grad Sch Shenzhen 518055 Peoples R China Peking Univ Comp Applicat Technol Shenzhen Grad Sch Shenzhen 518055 Peoples R China Peking Univ Sch Comp Sci Beijing Peoples R China Harbin Inst Technol Shenzhen Comp Sci & Technol Shenzhen 518055 Peoples R China

As an emerging paradigm for signal acquisition and reconstruction, compressive sensing (CS) achieves high-speed sampling and compression jointly and has found its way into many applications. With the fast growth of deep learning in computer vision, various methods of applying neural networks (NNs) in CS imaging tasks have been proposed. One category of them, named the deep unrolling network, is inspired by the physical sampling model and combines the merits of both optimization model- and data-driven methods, becoming the mainstream of this realm. In this review article, we first review the inverse imaging model and optimization algorithms encountered in the CS research and then provide the recent representative developments of CS networks, which are grouped into deep physics-free and physics-inspired approaches with respect to the utilization of sampling matrix and measurement information. Following this, we analyze the conceptual connections and relationships among various existing methods and present our perspectives on recent advances and trends for future research.

关键词： Image coding Computational modeling signal processing algorithms Transforms Market research Image restoration Task analysis

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Enabling High Accuracy Distance Measurements With FMCW Radar Sensors

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IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES 2019年第12期67卷 5360-5371页

作者： Piotrowsky, Lukas Jaeschke, Timo Kueppers, Simon Siska, Jan Pohl, Nils Ruhr Univ Bochum Inst Integrated Syst D-44801 Bochum Germany 2 Labs GmbH D-44801 Bochum Germany

With the integrated radar technology being increasingly common in the automotive segment, it becomes even more cost-effective in other applications as well. Taking into account its price and robustness, radar sensors can be considered as a potential replacement for laser interferometry which is being widely used for accurate contactless sensing. In this paper we describe a phase evaluation algorithm for highly accurate distance measurements using linear frequency modulated continuous wave (FMCW) radar systems, considering hardware dependent effects i.e. frequency responses of the signal paths. In several investigations we show that this novel algorithm is significantly more robust against disturbing radar targets or micro vibrations than typical techniques. Distance measurements were carried out using an 80 GHz wideband FMCW radar sensor on a maximum measurement range of 5.2 m with a movable radar target. For free space measurements the unambiguous measurement accuracy was improved to using phase evaluation techniques in a non-ideal environment over the entire measurement range, which was previously around with frequency evaluation techniques. Due to its robustness and accuracy, the proposed algorithm is well suited for harsh industrial environments such as real time positioning of machine tools.

关键词： Distance measurement estimation error millimeter wave radar radar measurements radar theory signal processing algorithms ultra wideband radar

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Redundancy of signals and transformations and computational complexity of signal and image processing

Redundancy of signals and transformations and computational ...

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International Conference on Pattern Recognition

作者： L.P. Yaroslavsky V. Kober National Institutes of Health BEIP/NCRR Bethesda MD USA

We demonstrate the use of informational redundancy of signals and transforms for reducing the computational costs of signal processing. Four concrete examples of accelerated signal processing algorithms are presented to support the idea of purposive use of signal and transform redundancy for saving the computational costs. These are: an accelerated algorithm for Fourier spectral analysis, an accelerated algorithm for computing the signal local histograms, the quantized discrete Fourier transforms, and recursive implementation of arbitrary digital filters. The former two reduce computation time by exploiting signal redundancy. The latter two save processing time at the expense of the accuracy of representation of the corresponding signal transforms.

关键词： signal processing algorithms signal processing Acceleration Computational efficiency Discrete transforms Concrete Spectral analysis Histograms Discrete Fourier transforms Digital filters

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