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Multi-TDOA Estimation and Source Direct Position Determination Based on Parallel Factor Analysis

IEEE INTERNET OF THINGS JOURNAL 2023年第8期10卷 7405-7415页

作者： Li, Jianfeng Li, Yingying Jiang, Hang Zhang, Xiaofei Wu, Qihui Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing 210016 Peoples R China

In this article, source localization exploiting time difference of arrival (TDOA) information is discussed, and a parallel factor (PARAFAC) analysis-based method for multi-TDOA estimation and direct position determination (DPD) is proposed. First, signals from the radiation source are received by multiple antennas through synchronous sensing. Then, the data from multiple antennas is fused by extracting the time-delay matrix to construct a PARAFAC model. Thereafter, the time-delay matrix is obtained by fast iterative decomposition using the trilinear alternating least square (TALS) algorithm. In the case of no multipath or weak multipath, where the typical scenario is the antennas being deployed on the airborne platform, the multi-TDOA estimation can be obtained simultaneously according to the time-delay matrix to improve the processing speed. In the case of multipath, such as the antennas being located on the ground, the DPD cost function directly related to the source position can be established based on the time-delay matrix, and the source position estimation can be achieved through grid search. Compared with the other DPD methods, the proposed DPD method has better positioning performance and lower complexity. The effectiveness and superiority of the proposed methods are verified by both simulations and actual scenario tests.

关键词： Estimation Location awareness Delay effects signal processing algorithms Matrix decomposition Cost function Antennas Direct position determination (DPD) multipath propagation parallel factor (PARAFAC) time-delay difference estimation

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Direction Finding in Partly Calibrated Arrays Exploiting the Whole Array Aperture

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2023年第5期59卷 5978-5992页

作者： Zhang, Guangbin Huang, Tianyao Liu, Yimin Wang, Xiqin Eldar, Yonina C. Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Weizmann Inst Sci Dept Comp Sci & Appl Math IL-7610001 Rehovot Israel

We consider the problem of direction finding using partly calibrated arrays, a distributed subarray with position errors between subarrays. The key challenge is to enhance angular resolution in the presence of position errors. To achieve this goal, existing algorithms, such as subspace separation and sparse recovery, have to rely on multiple snapshots, which increases the burden of data transmission and the processing delay. Therefore, we aim to enhance angular resolution using only a single snapshot. To this end, we exploit the orthogonality of the signals of partly calibrated arrays. Particularly, we transform the signal model into a multiple-measurement model, and show that there is approximate orthogonality between the source signals in this model. We then use blind source separation to exploit the orthogonality. Simulation and experiment results both verify that our proposed algorithm achieves high angular resolution as distributed arrays without position errors, inversely proportional to the whole array aperture.

关键词： Apertures signal resolution Array signal processing Sensors Transforms signal processing algorithms Sensor arrays Blind source separation high angular resolution orthogonality partly calibrated array

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Gerchberg-Saxton Based FIR Filter for Electronic Dispersion Compensation in IM/DD Transmission Part II: Experimental Demonstration and Analysis

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JOURNAL OF LIGHTWAVE TECHNOLOGY 2023年第5期41卷 1428-1435页

作者： Wu, Xiong Karar, Abdullah S. Zhong, Kangping Gurkan, Zeynep Nilhan Lau, Alan Pak Tao Lu, Chao Hong Kong Polytech Univ Photon Res Inst Dept Elect & Informat Engn Hong Kong Peoples R China Hong Kong Polytech Univ Shenzhen Res Inst Shenzhen 518057 Peoples R China Amer Univ Middle East Coll Engn & Technol Egaila 15453 Kuwait Poet Technol Inc Shenzhen 518057 Peoples R China

In this article, the first experimental demonstration of a non-iterative electronic dispersion compensation (EDC) solution implemented at the transmitter using a finite impulse response (FIR) filter optimized with the Gerchberg-Saxton (GS) algorithm, is presented, for intensity-modulation and direct-detection (IM/DD) systems. The theoretical framework and preliminary simulations have been presented in Part I of this work. Here, the performance is compared between the GS FIR filter and the iterative GS algorithm in 56-Gb/s on-off keying (OOK) transmissions over 80-km single mode fiber (SMF) with a post feed-forward equalizer (FFE) for combating residual inter-symbol interference. Furthermore, the influence of the pulse shape (raised cosine or rectangular) and modulation format (return-to-zero (RZ) or non-return-to-zero (NRZ)) on the measured bit error ratio (BER) is investigated, while changing the number of FIR taps, post-FFE taps, and post-FFE samples-per-symbol. It is shown that within the range of the target digital extinction ratios (DERs) for which the original iterative GS algorithm offers benefit, both analytical and numerical methods for calculating the optimum FIR taps, outlined in Part I, produce similar BER performance as predicted. Hence, the former method is extended, here, through a non-recursive frequency response formula, which offers insight into the action of the GS filter with different pulse shaping and enables the derivation of the explicit GS impulse response. It is also shown that rectangular RZ exploits the full benefit of GS filtering through a uniform spectrum, achieving BER < 3.8x10(-3), with a 641-tap T/2-spaced pre-EDC FIR filter and a 3-tap adaptive T-spaced post-FFE.

关键词： Finite impulse response filters Modulation Dispersion Filtering algorithms signal processing algorithms Frequency-domain analysis Transfer functions Digital to analog converter digital signal processing electronic dispersion compensation finite impulse response filter Gerchberg-Saxton algorithm intensity modulation and direct detection Mach-Zehnder modulator

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Complex-Domain Pitch Estimation Algorithm for Narrowband Speech signals

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE processing 2023年 31卷 2067-2078页

作者： Hosoda, Yuya Kawamura, Arata Iiguni, Youji Toyohashi Univ Technol Ctr IT based Educ Toyohashi Aichi 4418122 Japan Kyoto Sangyo Univ Sch Fac Informat Sci & Engn Kyoto 6038555 Japan Osaka Univ Grad Sch Engn Sci Osaka 5600043 Japan

We propose a complex-domain pitch estimation algorithm for narrowband speech signals, which utilizes a complex spectrum containing both amplitude and phase spectrum information. Traditional frequency-domain pitch estimation algorithms assume that a speech signal has a harmonic structure;they estimate a pitch by calculating the distance between the adjacent peaks of the amplitude spectrum corresponding to harmonics. However, only a few peaks can be detected from narrowband speech signals because of their limited bandwidth, resulting in pitch estimation errors. In this article, phase differences between harmonics are utilized as an additional cue for pitch estimation. The phase difference between harmonics refers to the two-step phase difference between successive analysis frames and between the lowest-order harmonic and other harmonics, which is theoretically derived using the pitch. When the phase spectrum for the higher-order harmonic is shifted by the theoretical value, it agrees with the phase spectrum for the lowest-order harmonic. Therefore, for each pitch candidate, the proposed method calculates the shifted phase spectra and combines them with the amplitude spectrum to generate complex spectra. When a pitch candidate is correct, the cumulative sum of the complex spectra is added in the same direction, which emphasizes harmonics even for narrowband speech signals. Results of the objective evaluation show that the proposed method accurately estimates the pitch from narrowband speech signals.

关键词： Harmonic analysis Narrowband Speech coding signal processing algorithms Estimation error Speech enhancement Indexes Narrowband speech signal phase difference pitch estimation sinusoidal model

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Adaptive CL-BFGS algorithms for Complex-Valued Neural Networks

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第9期34卷 6313-6327页

作者： Zhang, Yongliang Huang, He Shen, Gangxiang Soochow Univ Sch Elect & Informat Engn Suzhou 215006 Peoples R China Jiangsu Engn Res Ctr Novel Opt Fiber Technol & Co Suzhou 215006 Peoples R China

Complex-valued limited-memory BFGS (CL-BFGS) algorithm is efficient for the training of complex-valued neural networks (CVNNs). As an important parameter, the memory size represents the number of saved vector pairs and would essentially affect the performance of the algorithm. However, the determination of a suitable memory size for the CL-BFGS algorithm remains challenging. To deal with this issue, an adaptive method is proposed in which the memory size is allowed to vary during the iteration process. Basically, at each iteration, with the help of multistep quasi-Newton method, an appropriate memory size is chosen from a variable set {1,2,..., M} by approximating complex Hessian matrix as close as possible. To reduce the computational complexity and ensure desired performance, the upper bound M is adjustable according to the moving average of memory sizes found in previous iterations. The proposed adaptive CL-BFGS (ACL-BFGS) algorithm can be efficiently applied for the training of CVNNs. Moreover, it is suggested to take multiple memory sizes to construct the search direction, which further improves the performance of the ACL-BFGS algorithm. Experimental results on some benchmark problems including the pattern classification, complex function approximation, and nonlinear channel equalization problems are given to illustrate the advantages of the developed algorithms over some previous ones.

关键词： Approximation algorithms Training signal processing algorithms Optimization Neural networks Upper bound Mathematical models Adaptive complex-valued limited-memory BFGS (ACL-BFGS) algorithm complex-valued neural networks (CVNNs) moving average multistep quasi-Newton method variable memory size

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Statistical Stability of Correlation Based Imaging algorithms

IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING

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IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING 2023年 9卷 327-334页

作者： Leibovich, Matan Papanicolaou, George Tsogka, Chrysoula NYU Courant Inst Math Sci New York NY 10012 USA Stanford Univ Dept Math Stanford CA 94305 USA Univ Calif Merced Appl Math Dept Merced CA 95343 USA

We consider imaging of an airborne target and study the stability of correlation based imaging methods to random fluctuations in the target's motion. The imaging system consists of a ground based emitter, and several passive receivers. By migrating the cross correlations of the received signals the two-point interference matrix is obtained. An image is then formed either by taking the diagonal of this matrix or by computing the eigenvector corresponding to the largest eigenvalue of the matrix. We call the latter the rank-1 imaging method. We show that the rank-1 image exhibits exceptional stability with respect to fluctuations in the platform's motion. We relate that to the stability of the first eigenvector of a matrix perturbed by multiplicative random phase noise. We provide simulations as well as an analytical model that demonstrates the robustness of the rank-1 imaging method to random fluctuations in the target's motion.

关键词： Analytical models Fluctuations Correlation Imaging signal processing algorithms Receivers Radar imaging Inverse synthetic aperture radar radar imaging radar signal processing

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Distributed Inference Over Linear Models Using Alternating Gaussian Belief Propagation

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IEEE INTERNET OF THINGS JOURNAL 2023年第22期10卷 19949-19963页

作者： Cosovic, Mirsad Miskovic, Dragisa Delalic, Muhamed Raca, Darijo Vukobratovic, Dejan Univ Sarajevo Fac Elect Engn Sarajevo 71000 Bosnia & Herceg Inst Artificial Intelligence Res & Dev Serbia Novi Sad 21000 Serbia Univ Novi Sad Fac Tech Sci Novi Sad 21000 Serbia

We consider the problem of maximum-likelihood estimation in linear models represented by factor graphs and solved via the Gaussian belief propagation algorithm. Motivated by massive Internet of Things (IoT) networks and edge computing, we set the above problem in a clustered scenario, where the factor graph is divided into clusters and assigned for processing in a distributed fashion across a number of edge computing nodes. For these scenarios, we show that an alternating Gaussian belief propagation (AGBP) algorithm that alternates between inter- and intracluster iterations, demonstrates superior performance in terms of convergence properties compared to the existing solutions in the literature. We present a comprehensive framework and introduce appropriate metrics to analyze the AGBP algorithm across a wide range of linear models characterized by symmetric and nonsymmetric, square, and rectangular matrices. We extend the analysis to the case of dynamic linear models by introducing the dynamic arrival of new data over time. Using a combination of analytical and extensive numerical results, we show the efficiency and scalability of the AGBP algorithm, making it a suitable solution for large-scale inference in massive IoT networks.

关键词： Heuristic algorithms Inference algorithms Maximum likelihood estimation Internet of Things Estimation signal processing algorithms Computational modeling Distributed systems factor graphs Gaussian belief propagation (GBP) Internet of Things (IoT) networks linear models

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Multi-Channel Back-Projection Algorithm for mmWave Automotive MIMO SAR Imaging With Doppler-Division Multiplexing

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IEEE JOURNAL OF SELECTED TOPICS IN signal processing 2023年第2期17卷 445-457页

作者： Zhang, Bangjie Xu, Gang Zhou, Rui Zhang, Hui Hong, Wei Southeast Univ Sch Informat Sci & Engn State Key Lab Millimeter Waves Nanjing 210096 Peoples R China

Due to the low-cost and compact size, millimeter-wave radar is gaining increasing attention in automotive applications. Compared with the array imaging technique, synthetic aperture radar (SAR) takes advantage of high resolution, low system complexity, and is an important powerful tool in automotive radar imaging. In this paper, a high-resolution MIMO SAR imaging algorithm is proposed in the framework of Doppler-division multiplexing (DDM), where a novel multi-channel back-projection (BP) approach is presented for coherent multi-channel image integration. In the scheme, the DDM is applied on transmitter and Doppler filtering is employed to separate echoes of different transmitters on one receiver. Then the proposed multi-channel BP algorithm is divided into two parts. The first step corresponds to the BP SAR imaging on each channel, effectively dealing with the problem of near-field and wide-angle focusing. As follows, digital beamforming (DBF) and beampattern synthesis based on alternating direction method of multipliers (ADMM) is used for multi-channel SAR image coherent fusion. Experiments using real-measured data from an automotive radar system with a two-transmitter-eight-receiver array validate the effectiveness of the proposed algorithm.

关键词： Radar imaging Imaging Radar Synthetic aperture radar MIMO communication Automotive engineering signal processing algorithms Automotive synthetic aperture radar (SAR) Doppler-division multiplexing (DDM) back-projection (BP) digital beamforming (DBF)

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Model-Based Deep Learning

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PROCEEDINGS OF THE IEEE 2023年第5期111卷 465-499页

作者： Shlezinger, Nir Whang, Jay Eldar, Yonina C. Dimakis, Alexandros G. Ben Gurion Univ Negev Sch Elect & Comp Engn ECE IL-8410501 Beer Sheva Israel Univ Texas Austin Dept Comp Sci CS Austin TX 78712 USA Weizmann Inst Sci Fac Math & Comp Sci CS IL-7632706 Rehovot Israel Univ Texas Austin Dept Elect & Comp Engn ECE Austin TX 78712 USA

signal processing, communications, and control have traditionally relied on classical statistical modeling techniques. Such model-based methods utilize mathematical formulations that represent the underlying physics, prior information, and additional domain knowledge. Simple classical models are useful but sensitive to inaccuracies and may lead to poor performance when real systems display complex or dynamic behavior. On the other hand, purely data-driven approaches that are model-agnostic are becoming increasingly popular as datasets become abundant and the power of modern deep learning pipelines increases. Deep neural networks (DNNs) use generic architectures that learn to operate from data and demonstrate excellent performance, especially for supervised problems. However, DNNs typically require massive amounts of data and immense computational resources, limiting their applicability for some scenarios. In this article, we present the leading approaches for studying and designing model-based deep learning systems. These are methods that combine principled mathematical models with data-driven systems to benefit from the advantages of both approaches. Such model-based deep learning methods exploit both partial domain knowledge, via mathematical structures designed for specific problems, and learning from limited data. Among the applications detailed in our examples for model-based deep learning are compressed sensing, digital communications, and tracking in state-space models. Our aim is to facilitate the design and study of future systems at the intersection of signal processing and machine learning that incorporate the advantages of both domains.

关键词： Deep learning Mathematical models Computational modeling Data models signal processing signal processing algorithms Task analysis model-based machine learning signal processing

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A Flexible-Channel MDF Architecture for Pipelined Radix-2 FFT

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IEEE ACCESS 2023年 11卷 38023-38033页

作者： Zhou, Xiao Chen, Xuerong He, Yi Mou, Xingang Wuhan Univ Technol Sch Mech & Elect Engn Wuhan 430070 Hubei Peoples R China Wuhan Univ Technol Intelligent Transportat Syst Res Ctr Wuhan 430070 Hubei Peoples R China

This paper proposes a flexible-channel multiple-path delay feedback (FC-MDF) pipelined fast Fourier transform architecture, which is based on Radix-2 FFT algorithm. The FC-MDF can be dynamically configured to accommodate two levels of FFT size. Different from the bypass structure that prevents the sequence data from passing through the preceding stages when the FFT size is reduced, the FC-MDF can be dynamically configured into single-channel mode when calculating larger size sequences, and into dual-channel mode when calculating smaller size sequences by taking advantage of the first stage 0and sharing of processing elements in subsequent stages. The proposed architecture increases the utilization of computing units, including butterfly and complex multiplier, from 50% to 100%, and provides twice the throughput in dual-channel mode, compared with the traditional Radix-2 single-path delay feedback (R2SDF) architecture. We implemented this structure on Xilinx FPGAs. The experimental results show that, compared with current SDF, the proposed FC-MDF significantly optimizes the proportion of resources when the FFT size is reduced.

关键词： signal processing algorithms Discrete Fourier transforms Periodic structures Delays Computer architecture Hardware Field programmable gate arrays Fast Fourier transform (FFT) pipelined architecture reconfigurable design multi-path delay feedback (MDF)

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