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A Sum-Difference Expansion Scheme for Sparse Array Construction Based on the Fourth-Order Difference Co-Array

IEEE signal processing LETTERS 2022年 29卷 2647-2651页

作者： Yang, Zixiang Shen, Qing Liu, Wei Cui, Wei Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Univ Sheffield Dept Elect & Elect Engn Sheffield S1 3JD England

A generalized sum-difference expansion scheme is proposed to construct sparse arrays based on the fourth-order difference co-array with increased degrees of freedom (DOFs). Different from existing structures, both the second-order sum and difference co-arrays are exploited in array construction under this scheme, leading to a large consecutive fourth-order difference co-array with its number of uniform DOFs (uDOFs) derived. To optimize the provided uDOFs, required design properties of the initial prototype arrays are discussed. Three examples are then provided to demonstrate its superior performance over existing structures in both resolution capacity and estimation accuracy.

关键词： Prototypes Sensor arrays Estimation White noise Sparse matrices signal resolution signal processing algorithms Difference co-array DOA estimation fourth-order sparse array sum co-array

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Fast Event-Based Optical Flow Estimation by Triplet Matching

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IEEE signal processing LETTERS 2022年 29卷 2712-2716页

作者： Shiba, Shintaro Aoki, Yoshimitsu Gallego, Guillermo Keio Univ Fac Sci & Technol Dept Elect & Elect Engn Kanagawa 1088345 Japan Tech Univ Berlin Dept Elect Engn & Comp Sci D-10623 Berlin Germany Einstein Ctr Digital Future & Sci Intelligence Exc Berlin Germany

Event cameras are novel bio-inspired sensors that offer advantages over traditional cameras (low latency, high dynamic range, low power, etc.). Optical flow estimation methods that work on packets of events trade off speed for accuracy, while event-by-event (incremental) methods have strong assumptions and have not been tested on common benchmarks that quantify progress in the field. Towards applications on resource-constrained devices, it is important to develop optical flow algorithms that are fast, light-weight and accurate. This work leverages insights from neuroscience, and proposes a novel optical flow estimation scheme based on triplet matching. The experiments on publicly available benchmarks demonstrate its capability to handle complex scenes with comparable results as prior packet-based algorithms. In addition, the proposed method achieves the fastest execution time (> 10 kHz) on standard CPUs as it requires only three events in estimation. We hope that our research opens the door to real-time, incremental motion estimation methods and applications in real-world scenarios.

关键词： Estimation Optical flow signal processing algorithms Cameras Benchmark testing Indexes Neuroscience Event cameras optical flow low latency asynchronous sensor neuroscience robotics

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Mobile Robot Circumnavigating an Unknown Target Using Only Range Rate Measurement

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2022年第2期69卷 509-513页

作者： Wang, Jie Ma, Baoli Yan, Kai Beihang Univ Sch Automat Sci & Elect Engn Res Div 7 Beijing 100191 Peoples R China

This brief proposes a bounded continuous control law to steer a mobile robot to approach and maintain a predefined distance from an unknown stationary target in a GPS-denied environment where the positions of the robot and the target are not available. The control algorithm utilizes only the derivative of the distance between the mobile robot and the target, that is, range rate measurement. We demonstrate that the controller can achieve global convergence regardless of the initial states of the mobile robot. Compared with the existing works, the proposed control algorithm is the simplest form (derivative-type controller) and easy to implement. Numerical simulations and real-time experiments are both performed to confirm the effectiveness of the proposed control scheme.

关键词： Robots Mobile robots Extraterrestrial measurements Robot kinematics signal processing algorithms Convergence Dynamical systems Navigation mobile robot range-based measurements target circumnavigation

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STBC Recognition for OFDM Transmissions: Channel Decoder Aided Algorithm

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IEEE COMMUNICATIONS LETTERS 2022年第7期26卷 1658-1662页

作者： Marey, Mohamed Mostafa, Hala Alshebeili, Saleh A. Dobre, Octavia A. Prince Sultan Univ Coll Engn Smart Syst Engn Lab Riyadh 11586 Saudi Arabia Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Technol Riyadh 11671 Saudi Arabia King Saud Univ Dept Elect Engn Riyadh 11451 Saudi Arabia Memorial Univ Dept Elect & Comp Engn St John NL A1C 5S7 Canada

Recently, the process of space-time block coding (STBC) recognition has found applications in a wide range of wireless transmissions. Numerous investigations on this topic have been developed for single-carrier transmissions and a few studies have been introduced for orthogonal frequency division multiplexing (OFDM) transmissions. The previous research work in the literature has focused on exploiting the space-time redundancy to blindly estimate the type of STBC signal. In this work, we propose a new approach to design a STBC recognizer for OFDM transmissions by taking benefits from the a posteriori probabilities provided by a channel decoder. The mathematical treatments have shown that the exact maximum-likelihood (ML) solution to this problem is computationally demanding in the absence of a closed form. Therefore, we settle for the expectation-maximization (EM) algorithm as a low-complexity procedure to perform STBC recognition. In addition, we develop a channel estimation algorithm to be integrated into the proposed recognition algorithm. Simulation results are presented to confirm the theoretical analysis, with exhibiting superior recognition performance over the state-of-the-art algorithms.

关键词： Symbols OFDM signal processing algorithms Codes Channel estimation Wireless communication Maximum likelihood estimation signal recognition STBCs ML algorithm

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Improved CLEAN Algorithm for RFI Mitigation of Aperture Synthesis Radiometer Images

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2022年 19卷

作者： Li, Yinan Zhang, Wenxuan Zhang, Juan Zhang, Linrang Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China

For synthetic aperture radiometer, radio frequency interference (RFI) seriously contaminates the observed data and affects the quality of the brightness temperature (BT) image. CLEAN algorithm is usually used to mitigate the Gibbs effect generated by RFI. However, the closely distributed RFI sources cannot be well mitigated by the original CLEAN algorithm. This letter proposes an improved adaptive CLEAN algorithm to enhance the performance of RFI mitigation, especially the closely distributed RFI sources. The loop factor is forced to synchronize with residual BT of RFI for every iteration to avoid over mitigation. Over mitigation can result in a BT vacancy area or an extra BT point generated at the position of RFI. Soil Moisture and Ocean Salinity (SMOS) L1B real data are applied to verify the effectiveness of the improved CLEAN algorithm. The simulation shows that the improved CLEAN algorithm can be competent in eliminating isolated and closely distributed RFIs better than the classic CLEAN algorithm, and the over mitigation problem is solved by the proposed method.

关键词： Microwave radiometry Receiving antennas signal processing algorithms Histograms Antenna arrays Synchronization Microwave integrated circuits CLEAN algorithm passive imaging radio frequency interference (RFI) synthetic aperture imaging radiometer (SAIR)

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Efficiency and Robustness Improvement of Airborne SAR Motion Compensation With High Resolution and Wide Swath

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

作者： Chen, Jianlai Liang, Buge Zhang, Junchao Yang, De-Gui Deng, Yuhui Xing, Mengdao Cent South Univ Sch Aeronaut & Astronaut Changsha 410083 Peoples R China Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China

For airborne synthetic aperture radar (SAR) imaging with high resolution and wide swath, the atmospheric turbulence may produce serious range-dependent (RD) motion error. To estimate the RD motion error, traditional methods usually first divide the range full-aperture data into multiple range blocks, and then use phase gradient autofocus (PGA) to estimate the phase error of all range blocks one by one, which is inefficient. In addition, the robustness of PGA is also affected by the number of strong scattering points. To solve these two problems, a new motion compensation (MoCo) algorithm is proposed to improve the efficiency and robustness of airborne SAR MoCo. The real data-processing results are given to verify the effectiveness of the algorithm.

关键词： Electronics packaging Robustness Synthetic aperture radar Scattering signal processing algorithms Atmospheric modeling Aircraft Motion compensation (MoCo) phase gradient autofocus (PGA) range-dependent (RD) error synthetic aperture radar (SAR)

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Active User Detection and Channel Estimation for Spatial-Based Random Access in Crowded Massive MIMO Systems via Blind Super-Resolution

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IEEE signal processing LETTERS 2022年 29卷 1072-1076页

作者： Afshar, Abolghasem Vakili, Vahid Tabataba Daei, Sajad Iran Univ Sci & Technol Sch Elect Engn Tehran 16844 Iran

This work presents a novel framework for random access (RA) in crowded scenarios of massive multiple-input multiple-output (MIMO) systems. A huge portion of the system resources is dedicated as orthogonal pilots for accurate channel estimation which imposes a huge training overhead. This overhead can be highly mitigated by exploiting intrinsic angular domain sparsity of massive MIMO channels and the sporadic traffic of users, i.e., few number of users are active to send or receive data in each coherence interval. Besides, the continuous-valued angles of arrival (AoA) corresponding to each active user are alongside each other forming a specific cluster. To exploit these features in this work, we propose a blind clustering algorithm based on super-resolution techniques that not only detects the spatial features of the active users but also provides accurate channel estimation. Specifically, an off-grid atomic norm minimization is proposed to obtain the AoAs and then a clustering-based approach is employed to identify which AoAs correspond to which active users. After active user detection, an alternating-based optimization approach is performed to obtain the channels and transmitted data. Simulation results demonstrate the effectiveness of our approach in AoA detection as well as data recovery which indeed provides a high performance spatial-based RA in crowded massive MIMO systems.

关键词： Massive MIMO Channel estimation signal processing algorithms Superresolution Optimization Task analysis Feature extraction Crowded massive MIMO random access super-resolution atomic norm semi-definite programming convex opti- mization

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Optimal Adaptive Filtering Algorithm by Using the Fractional-Order Derivative

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IEEE signal processing LETTERS 2022年 29卷 399-403页

作者： Zhang, Xiao Ding, Feng Jiangnan Univ Sch Internet Things Engn Key Lab Adv Proc Control Light Ind Minist Educ Wuxi 214122 Jiangsu Peoples R China

The previous work for the filter design considers uncorrelated white measurement noise disturbance. For more complex correlated noise disturbance, the conventional adaptive filter results in biased estimates. To overcome this problem, we introduce a linear prefilter to whiten the correlated noise (i.e., colored noise) for obtaining the unbiased estimate of the filter weight. Moreover, the design of some adaptive filters mainly focuses on the integer-order optimization methods. However, compared with the integer-order-based adaptive algorithms, the fractional-order-based algorithms show better performance. Thus, this letter develops a new gradient approach for the adaptive filter design based on the fractional-order derivative and a linear filter. Finally, the simulation results are provided from the system identification perspective for demonstrating the performance analysis of the proposed algorithms.

关键词： signal processing algorithms Finite impulse response filters Convergence Colored noise Mathematical models Estimation Adaptation models Adaptive filtering gradient search fractional-order derivative

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Joint Topology Learning and Graph signal Recovery Using Variational Bayes in Non-Gaussian Noise

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2022年第3期69卷 1887-1891页

作者： Torkamani, Razieh Zayyani, Hadi Marvasti, Farokh Bu Ali Sina Univ Dept Elect Engn Hamadan Hamadan Iran Qom Univ Technol Dept Elect & Comp Engn Qom Iran Sharif Univ Technol Dept Elect Engn Tehran Iran

This brief proposes a joint graph signal recovery and topology learning algorithm using a Variational Bayes (VB) framework in the case of non-Gaussian measurement noise. It is assumed that the graph signal is Gaussian Markov Random Field (GMRF) and the graph weights are considered statistical with the Gaussian prior. Moreover, the non-Gaussian noise is modeled using two distributions: Mixture of Gaussian (MoG), and Laplace. All the unknowns of the problem which are graph signal, Laplacian matrix, and the (Hyper)parameters are estimated by a VB framework. All the posteriors are calculated in closed forms and the iterative VB algorithm is devised to solve the problem. The efficiency of the proposed algorithm in comparison to some state-of-the-art algorithms in the literature is shown in the simulation results.

关键词： Topology Laplace equations signal processing algorithms Noise measurement Symmetric matrices Heuristic algorithms Computational modeling Graph signal recovery topology learning Laplacian matrix variational Bayes non-Gaussian noise

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Decision-Based Attack to Speaker Recognition System via Local Low-Frequency Perturbation

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IEEE signal processing LETTERS 2022年 29卷 1432-1436页

作者： Deng, Jiacheng Dong, Li Wang, Rangding Yang, Rui Yan, Diqun Ningbo Univ Dept Comp Sci Ningbo 315211 Peoples R China Alibaba Grp Inc Hangzhou 311121 Peoples R China

Despite neural network-based speaker recognition systems (SRS) have enjoyed significant success, they are proved to be quite vulnerable to adversarial examples. In practice, the SRS model parameters are not always available. Attackers have to probe the model only via querying, and such decision-based attacking merely relies on the output label is quite challenging. This letter proposes a two-step query-efficient decision-based attack based on local low-frequency perturbation. Specifically, instead of imposing perturbation on the entire audio sample, a local attacking region is firstly sought, confining the perturbed distortion to a local region. Second, considering that the majority of energy concentrates on the low-frequency bands, the proposed method suggests performing perturbation generation in the low-frequency domain. Experimental results demonstrate that, compared with the recent methods, our method could implement target attacking to SRS with a higher attacking success rate, at the cost of much lower queries and adversarial perturbation.

关键词： Perturbation methods Distortion Speaker recognition Computational modeling signal processing algorithms Task analysis Time-domain analysis Adversarial example decision-based attack speaker recognition system

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