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Translational Motion Compensation for ISAR Imaging Based on Range Joint Fast Orthogonal Matching Pursuit Algorithm

IEEE ACCESS 2022年 10卷 37382-37395页

作者： Wei, Xu Yang, Jun Lv, Mingjiu Chen, Wenfeng Ma, Xiaoyan Air Force Early Warning Acad Wuhan 430019 Peoples R China

Accurate translational motion compensation (TMC) is the key procedure of inverse synthetic aperture radar (ISAR) imaging. Under the condition of sparse aperture, the correlation of adjacent pulses is severely destroyed, which brings a few challenges to TMC and imaging. Thus, this paper proposes a novel TMC method for ISAR imaging. In this technique, the signal model of joint phase adjustment and ISAR imaging is established first. Then, the process of TMC is designed. That is, after range alignment with improved global minimum entropy (IGME), the range joint fast orthogonal matching pursuit (RJFOMP) algorithm is applied to ISAR imaging, and the minimum entropy method (MEM) is adopted to estimate the phase error. Meanwhile, the joint optimization of phase adjustment and ISAR imaging is realized through the cyclic iteration. Extensive experiments based on both simulated and measured data demonstrate that the proposed TMC method is effective for two modes of motion errors, which are named as coherent mode (CM) and non-coherent mode (NCM), respectively. When RJFOMP is used for joint phase adjustment and ISAR imaging, it has excellent imaging performance under noisy and sparse conditions.

关键词： Imaging Radar imaging Matching pursuit algorithms Entropy Apertures signal processing algorithms Scattering Inverse synthetic aperture radar (ISAR) translational motion compensation (TMC) improved global minimum entropy (IGME) range joint fast orthogonal matching pursuit (RJFOMP) algorithm minimum entropy method (MEM)

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Time Domain IRCI-Free Pulse Compression for OQAM-OFDM Radar System

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IEEE SYSTEMS JOURNAL 2022年第4期16卷 6480-6489页

作者： Shi, Qiao Zhang, Tianxian Yu, Xianxiang Liu, Xinyu Lee, Inkyu Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Sichuan Peoples R China Korea Univ Sch Elect Engn Seoul 02841 South Korea

In this article, a novel majorization-minimization (MM)-based time domain pulse compression method (MM-based TDPCM) is proposed for offset quadrature amplitude modulation-based orthogonal frequency division multiplexing (OQAM-OFDM) radar system. We first propose a TDPCM by constructing a general linear model from the time domain with the estimated weights of the OQAM-OFDM signal and solving the model with a whitening operation to complete the pulse compression. The proposed TDPCM greatly reduces the required number of subcarriers for the given range cells and achieves inter range cell interference (IRCI)-free pulse compression. Furthermore, the MM-based TDPCM is proposed to maximize the signal-to-noise ratio (SNR) of the TDPCM. By theoretically analyzing the SNR performance of the TDPCM, we develop an optimization model to design the transmitted weights of the OQAM-OFDM signal. Then, the MM algorithm is proposed to solve the optimization model, realizing the maximum SNR output. Finally, numerical simulation results verify the superior performance of the proposed MM-based TDPCM.

关键词： signal to noise ratio Radar OFDM signal processing algorithms Covariance matrices Correlation Spaceborne radar Inter-range-cell interference (IRCI)-free majorization-minimization (MM) offset quadrature amplitude modulation-based orthogonal frequency division multiplexing (OQAM-OFDM) radar system time domain pulse compression method (TDPCM)

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LoRa signal Synchronization and Detection at Extremely Low signal-to-Noise Ratios

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IEEE INTERNET OF THINGS JOURNAL 2022年第11期9卷 8869-8882页

作者： Ameloot, Thomas Rogier, Hendrik Moeneclaey, Marc Van Torre, Patrick Univ Ghent Dept Informat Technol IDLab IMEC B-9052 Ghent Belgium Univ Ghent Dept Telecommun & Informat Proc B-9000 Ghent Belgium

In recent years, LoRa has been deployed in countless Internet of Things (IoT) applications across the globe. However, as LoRa is a proprietary technology, research into its physical-layer performance has been challenging. Implementing LoRa on software-defined radio (SDR) platforms yields valuable insight into the physical layer of the LoRa standard and paves the way for improvements in packet reception capabilities for LoRa receivers. This article presents an independently developed packet reception algorithm, which drastically improves the physical performance of LoRa communication links. The advanced signal presence detection, synchronization, and symbol detection strategies are shown to significantly increase packet reception ratios in extremely adverse noise conditions. Multiple algorithm variations are presented and compared in terms of bit error rate (BER) performance and computational cost. In comparison to a theoretical system with perfect channel state information, the simulated BER performance of the best performing algorithm only requires an increase of 1.6 dB in signal-to-noise ratio (SNR) to exhibit the same performance. Finally, SDR implementations of the algorithms exhibit average SNR performance gains up to 4.7 dB when compared to commercially available hardware.

关键词： Modulation signal to noise ratio Internet of Things Synchronization Receivers Wireless communication signal processing algorithms Detection Internet of Things (IoT) LoRa software-defined radio (SDR) wireless sensor networks

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A High-Efficiency Optimized Detection Algorithm for Non-Stationary Marine Acoustic signals in the Time-Frequency Domain

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IEEE ACCESS 2022年 10卷 64085-64094页

作者： Cao, Tianyu Bian, Mengqi Yang, Yichen Xu, Zhongwei Zhao, Xiaoqun Tongji Univ Coll Elect & Informat Engn Dept Informat & Commun Engn Shanghai 201804 Jiading Peoples R China

As the amount of data generated by marine acoustic observation signals grows, efficient information acquisition of non-stationary observation signals has become a major challenge in marine observation platform technology. In this paper, an optimized algorithm is proposed for the non-stationary marine acoustic signals. This algorithm can increase the effective data acquisition rate while decreasing the observation platform's algorithm energy consumption. To constantly enhance the processing of the observation signal through the self-feedback, the optimized algorithm is based on the sign function, the adjustable coefficient, the adaptive step size, and the frequency domain threshold. The simulation verification experiment and the application experiment based on the optimized algorithm are shown in this study. The experimental results indicate that the optimized algorithm efficiency is 78.16% in the simulation conditions and reaches 89.89% in the application experiment. And the data compression rates for the simulation conditions and the application experiment are 74.65% and 69.32%, respectively. As a result, the optimized algorithm's performance has significantly improved.

关键词： signal processing algorithms Acoustics Time-frequency analysis Background noise signal processing Classification algorithms Memory Non-stationary marine acoustic signal self-feedback signal processing efficiency time-frequency data compression

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Spatial Entropy Quartiles-Based Texture-Aware Fractional-Order Unsharp Masking for Visibility Enhancement of Remotely Sensed Images

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2022年第4期52卷 2275-2288页

作者： Singh, Himanshu Kumar, Anil Balyan, L. K. Lee, H. N. Indian Inst Informat Technol Design & Mfg Jabalpu Jabalpur 482005 India Gwangju Inst Sci & Technol Gwangju South Korea

A noniterative fractional-order (FO) two-dimensional (2-D) adaptive filtering mechanism is proposed in this article. Quartiles-based adaptive textural segmentation is employed for the calculation of texture-dependent FO. Statistically, the quartiles/quintiles/quantiles are cut points for dividing the range of a probability distribution into continuous intervals with equal probabilities. For the purpose of texture-based isolation of the spatial regions, a 2-D textural map is framed by evaluating spatial entropy by considering a pixel-wise local circular neighborhood. A novel end-to-end framework is proposed for FO texture-dependent image sharpening in an independent manner without influencing the other classes of textural regions. A novel inclusion of texture-wise adaptive gamma correction is also proposed in this article by drafting a mechanism where different kinds of textural regions can be separately processed in an independent manner. A novel quintiles-based multiscale Retinex (MSR) inspired approach for reflectance computation is coined in this article for suppressing environmental artifacts and unbalanced/nonuniform illumination. In this context, various scales required for MSR are themselves computed through quartiles-based intensity levels. The proposed model is highly modular. So, it can also be pipelined in a parallel manner, along with any well-established state-of-the-art contrast enhancement approach. Also, this approach is noniterative and highly robust. It can be proposed as an add-on for several possible (as well as pre-existing) image processing procedures. Rigorous comparative evaluations are performed so that the excellence of the proposed approach can be underlined.

关键词： Histograms Image segmentation Proposals Image enhancement Image color analysis Digital images signal processing algorithms Fractional-order calculus (FOC) image texture multiscale retinex (MSR) optimal filter designing quality enhancement quantiles quantiles-based data segmentation quartiles quintiles texture-based segmentation two-dimensional (2-D) adaptive filtering

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Federated Learning Under Importance Sampling

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 5381-5396页

作者： Rizk, Elsa Vlaski, Stefan Sayed, Ali H. Ecole Polytech Federalede Lausanne Sch Engn CH-1015 Lausanne Switzerland

Federated learning encapsulates distributed learning strategies that are managed by a central unit. Since it relies on using a selected number of agents at each iteration, and since each agent, in turn, taps into its local data, it is only natural to study optimal sampling policies for selecting agents and their data in federated learning implementations. Usually, only uniform sampling schemes are used. However, in this work, we examine the effect of importance sampling and devise schemes for sampling agents and data non-uniformly guided by a performance measure. We find that in schemes involving sampling without replacement, the performance of the resulting architecture is controlled by two factors related to data variability at each agent, and model variability across agents. We illustrate the theoretical findings with experiments on simulated and real data and show the improvement in performance that results from the proposed strategies.

关键词： Federated learning Monte Carlo methods Convergence signal processing algorithms Process control Distributed databases Distance learning distributed learning importance sampling asynchronous SGD non-IID data heterogeneous agents

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Robust One-Dimensional Phase Unwrapping Algorithm Based on LSTM Network With Reduced Parameter Number

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JOURNAL OF LIGHTWAVE TECHNOLOGY 2022年第19期40卷 6560-6567页

作者： Kong, Lei Pan, Xianglei Ren, Zhongjie Cui, Ke Nanjing Univ Sci & Technol Sch Elect & Opt Engn Nanjing 210094 Peoples R China Nantong Univ Sch Sci Nantong 226019 Peoples R China

For interferometric optical fiber sensor applications, one-dimensional (1D) phase unwrapping is one of the most intractable steps. It is generally more vulnerable to noise impact compared with two-dimensional (2D) phase unwrapping, since the data correlation for the 1D phase unwrapping is very limited. In a recent work, we proposed the 1D phase unwrapping algorithm by combining quasi-Gramian matrix and deep convolutional neural network (DCNN). The obtained result is very robust and can work very stably under low signal-to-noise ratio (SNR) level. But the required parameter amount is large being about 10(7) making itself inadequate for resource limited computation platforms. In this work, a lightweight phase unwrapping algorithm based on long short-term memory (LSTM) network is proposed, which utilizes only 10(6) parameter amounts, being 1 order smaller than the DCNN. Simulation results demonstrate that in the SNR range of 0 to 16 dB, the LSTM-based method shows comparable or better performance compared with the DCNN method, and for the very low SNR of -2 dB, only small performance decrease is observed. The generalization ability of the proposed method is also verified by using the experimental data collected from an actual phase-sensitive optical time domain reflectometry (phi-OTDR) system.

关键词： signal to noise ratio Training signal processing algorithms Machine learning algorithms Optical fiber sensors Mathematical models Prediction algorithms Deep learning interferometric fiber sensors LSTM network phase unwrapping

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General Tensor Least-Mean-Squares Filter for Multi-Channel Multi-Relational signals

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 6257-6271页

作者： Chang, Shih Yu Wu, Hsiao-Chun San Jose State Univ Dept Appl Data Sci San Jose CA 95192 USA Louisiana State Univ Sch Elect Engn & Comp Sci Baton Rouge LA 70803 USA

Least-mean-squares (LMS) algorithms constitute a prevalent approach to implement the linear adaptive filters whose coefficients can be updated sample by sample so as to track time-varying dynamics. As the memory and computational complexities required for the realization of LMS filters are very low, they have been widely adopted in many real-time signal processing applications. The input of any conventional LMS filter has to be a sequence of scalar samples (one-dimensional time series), whereas such assumption is too restrictive nowadays for multi-channel (high-dimensional) signals and multi-relational data in the rise of a big-data era. It is crucial to deal with high-dimensional data-arrays, a.k.a. tensors, to manifest the variety and complex interrelations of data. Owing to lack of a sufficient mathematical framework to govern relevant tensor operations, the general tensor LMS filter, whose input is allowed to be an arbitrary tensor, has never been established for realization to the best of our knowledge. In this work, we will dedicate a new mathematical framework for tensors to establish the general tensor least-mean-squares (TLMS) filter theory and propose two novel TLMS algorithms with update rules based on stochastic gradient-descent and Newton's methods, respectively. Furthermore, as we establish the tensor calculus theory, the performance evaluation on convergence-rate and misadjustment for our proposed TLMS filters can be conducted. Finally, the memory and computational complexities of the new TLMS algorithms are also studied in this paper.

关键词： Tensors Adaptive filters Filtering theory Filtering algorithms signal processing algorithms Time-varying systems Time-domain analysis Multi-channel and multi-relational signal processing tensor least-mean-squares (TLMS) filter tensor calculus stochastic gradient-descent Newton's method

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Low Computational Complexity for Optimizing Energy Efficiency in mm-wave Hybrid Precoding System for 5G

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IEEE ACCESS 2022年 10卷 4714-4727页

作者： Salh, Adeb Audah, Lukman Abdullah, Qazwan Aydogdu, Omer Alhartomi, Mohammed A. Alsamhi, Saeed Hamood Almalki, Faris A. Shah, Nor Shahida M. Univ Tun Hussein Onn Malaysia Fac Elect & Elect Engn Wireless & Radio Sci Ctr WARAS Batu Pahat 86400 Johor Malaysia Konya Tech Univ Fac Engn & Nat Sci TR-42250 Konya Turkey Selcuk Univ Fac Elect & Elect Engn TR-42250 Konya Turkey Univ Tabuk Dept Elect Engn Tabuk 47512 Saudi Arabia Technol Univ Shannon Midlands Midwest SRI Athlone N37 F6D7 Westmeath Ireland IBB Univ Fac Engn Ibb Yemen Taif Univ Coll Comp & Informat Technol Dept Comp Engn At Taif 21944 Saudi Arabia

Millimeter-wave (mm-wave) communication is the spectral frontier to meet the anticipated significant volume of high data traffic processing in next-generation systems. The primary challenges in mm-wave can be overcome by reducing complexity and power consumption by large antenna arrays for massive multiple-input multiple-output (mMIMO) systems. However, the circuit power consumption is expected to increase rapidly. The precoding in mm-wave mMIMO systems cannot be successfully achieved at baseband using digital precoders, owing to the high cost and power consumption of signal mixers and analog-to-digital converters. Nevertheless, hybrid analog-digital precoders are considered a cost-effective solution. In this work, we introduce a novel method for optimizing energy efficiency (EE) in the upper-bound multiuser (MU) - mMIMO system and the cost efficiency of quantized hybrid precoding (HP) design. We propose effective alternating minimization algorithms based on the zero gradient method to establish fully-connected structures (FCSs) and partially-connected structures (PCSs). In the alternating minimization algorithms, low complexity is proposed by enforcing an orthogonal constraint on the digital precoders to realize the joint optimization of computational complexity and communication power. Therefore, the alternating minimization algorithm enhances HP by improving the performance of the FCS through advanced phase extraction, which involves high complexity. Meanwhile, the alternating minimization algorithm develops a PCS to achieve low complexity using HP. The simulation results demonstrate that the proposed algorithm for MU - mMIMO systems improves EE. The power-saving ratio is also enhanced for PCS and FCS by 48.3% and 17.12%, respectively.

关键词： Radio frequency Precoding Antenna arrays Minimization Hardware Baseband signal processing algorithms mm-wave MIMO energy efficiency fully connected structure partially connected structure

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Accelerating Tensor Contraction Products via Tensor-Train Decomposition [Tips & Tricks]

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IEEE signal processing MAGAZINE 2022年第5期39卷 63-70页

作者： Kisil, Ilya Calvi, Giuseppe G. Konstantinidis, Kriton Xu, Yao Lei Mandic, Danilo P. Crypton AI London SW7 2AZ England Maniyar Capital London SW7 2AZ England Imperial Coll London Dept Elect & Elect Engn London SW7 2AZ England

Tensors (multiway arrays) and tensor decompositions (TDs) have recently received tremendous attention in the data analytics community, due to their ability to mitigate the curse of dimensionality associated with modern large-dimensional big data [1], [2]. Indeed, TDs allow for data volume (e.g., the parameter complexity) to be reduced from scaling exponentially to scaling linearly in the tensor dimensions, which facilitates applications in areas including the compression and interpretability of neural networks [1], [3], multimodal learning [1], and completion of knowledge graphs [4], [5]. At the heart of TD techniques is the tensor contraction product (TCP), an operator used for representing even the most unmanageable higher-order tensors through a set of small-scale core tensors that are interconnected via TCP operations [2].

关键词： Complexity theory Tensors Data analysis Software libraries Neural networks Market research Mathematical models Big Data Machine learning Notch filters Computational efficiency Approximation error signal processing algorithms

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