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Acoustic Source Localization Using a Geometrically Sampled Grid SRP-PHAT Algorithm With Max-Pooling Operation

IEEE signal processing LETTERS 2022年 29卷 1828-1832页

作者： Salvati, Daniele Drioli, Carlo Foresti, Gian Luca Univ Udine Dept Math Comp Sci & Phys I-33100 Udine Italy

The steered response power phase transform (SRP-PHAT) is a well-known algorithm for acoustic source localization using microphone arrays. It consists in the computation of the generalized cross-correlation (GCC) between each microphone pair, and in the coherent summation of the GCC values in the grid search space. Several improvements based on the volumetric grid have been proposed in order to achieve spatial resolution scalability and to reduce the computational cost by using a coarser grid. In general, the problem of the volumetric based methods is that the noise and the reverberation are projected into the search space since all GCC information is used to build the acoustic map. It is hence proposed a volumetric grid SRP-PHAT algorithm based on the geometrically sampled grid (GSG) that incorporates a max-pooling (MP) operation in the volume accumulation of the GCC values in order to improve the localization performance. The MP is the solution of a minimization-maximization problem that aims at minimizing the deleterious effect of noise and reverberation and at maximizing the accuracy of the GCC values related to the target sound source. Simulations and real-world experiments demonstrate the efficiency of the proposed SRP-GSG-MP algorithm in adverse conditions.

关键词： Location awareness Reverberation Mathematical models Spatial resolution signal processing algorithms Phased arrays Microphone arrays Acoustic source localization delay-and-sum beamformer geometrically sampled grid max-pooling microphone array phase transform steered response power

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Learning Sparse Graphs via Majorization-Minimization for Smooth Node signals

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IEEE signal processing LETTERS 2022年 29卷 1022-1026页

作者： Fatima, Ghania Arora, Aakash Babu, Prabhu Stoica, Petre Indian Inst Technol Delhi New Delhi 110016 India Univ Luxembourg Interdisciplinary Ctr Secur Reliabil & Trust SnT L-1855 Luxembourg Luxembourg Indian Inst Technol Delhi Ctr Appl Res Elect New Delhi 110016 India Uppsala Univ Dept Informat Technol S-75105 Uppsala Sweden

In this letter, we propose an algorithm for learning a sparse weighted graph by estimating its adjacency matrix under the assumption that the observed signals vary smoothly over the nodes of the graph. The proposed algorithm is based on the principle of majorization-minimization (MM), wherein we first obtain a tight surrogate function for the graph learning objective and then solve the resultant surrogate problem which has a simple closed form solution. The proposed algorithm does not require tuning of any hyperparameter and it has the desirable feature of eliminating the inactive variables in the course of the iterations - which can be used to speed up the algorithm. The numerical simulations conducted using both synthetic and real world (brain-network) data show that the proposed algorithm converges faster, in terms of the average number of iterations, than several existing methods in the literature.

关键词： signal processing algorithms Convergence Laplace equations Sparse matrices signal processing Numerical simulation Minimization Graph signal processing majorization-minimiz- ation sparse graph learning smooth signals

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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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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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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 Coarray Combinations Robust to Sensor Failures on Sparse Arrays

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IEEE signal processing LETTERS 2022年 29卷 1784-1788页

作者： Liu, Chun-Lin Natl Taiwan Univ Dept Elect Engn Grad Inst Commun Engn Taipei 10617 Taiwan

Sparse arrays can resolve more source direction-of-arrivals (DOAs) than sensors in array processing. This property is achieved with DOA estimators based on the difference coarray. However, sensor failures on sparse arrays could make these DOA estimators inapplicable. In the literature, array processing under sensor failures can be coped with array diagnosis. The failed sensors are detected first in array diagnosis, and the data on these detected failed sensors are removed next. However, errors in detecting the failure patterns degrade the performance. This letter presents coarray combinations for sparse arrays under sensor failures. In converting array data from the physical array to the difference coarray, coarray combination matrices (CCMs) aim to preserve the structures of the difference coarray under sensor failures. This condition corresponds to a system of equations with the CCMs. With prior knowledge about the failure patterns, a convex optimization problem is cast for this system of equations. The optimal CCM is pre-computed with closed-form expressions, avoids array diagnosis, and applies to coarray-based DOA estimators. Furthermore, numerical examples demonstrate that the proposed method reduces the errors in DOA estimation in comparison with other methods where the data on the detected failed sensors are removed.

关键词： Sensor arrays Direction-of-arrival estimation Array signal processing Mathematical models Data models signal processing algorithms Geometry Sparse arrays sensor failures difference coarr- ays coarray combination matrices

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One-Bit Compressive Sensing: Can We Go Deep and Blind?

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IEEE signal processing LETTERS 2022年 29卷 1629-1633页

作者： Zeng, Yiming Khobahi, Shahin Soltanalian, Mojtaba Univ Illinois Dept Elect & Comp Engn Chicago IL 60607 USA

One-bit compressive sensing is concerned with the accurate recovery of an underlying sparse signal of interest from its one-bit noisy measurements. The conventional signal recovery approaches for this problem are mainly developed based on the assumption that an exact knowledge of the sensing matrix is available. In this work, however, we present a novel data-driven and model-based methodology that achieves blind recovery;i.e., signal recovery without requiring the knowledge of the sensing matrix. To this end, we make use of the deep unfolding technique and develop a model-driven deep neural architecture which is designed for this specific task. The proposed deep architecture is able to learn an alternative sensing matrix by taking advantage of the underlying unfolded algorithm such that the resulting learned recovery algorithm can accurately and quickly (in terms of the number of iterations) recover the underlying compressed signal of interest from its one-bit noisy measurements. In addition, due to the incorporation of the domain knowledge and the mathematical model of the system into the proposed deep architecture, the resulting network benefits from enhanced interpretability, has a very small number of trainable parameters, and requires very small number of training samples, as compared to the commonly used black-box deep neural network alternatives for the problem at hand.

关键词： Sensors signal processing algorithms Decoding Sparse matrices Neural networks Compressed sensing Training Blind compressive sensing deep-unfolded neural networks interpretable deep learning one-bit sampling

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BECT Spike Detection Algorithm Based on Optimal Template Matching and Morphological Feature Selection

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2022年第4期69卷 2366-2370页

作者： Wu, Duanpo Shi, Haichao Jiang, Lurong Dong, Fang Liu, Junbiao Cao, Jiuwen Jiang, Tiejia Wu, Xunyi Hangzhou Dianzi Univ Sch Commun Engn Hangzhou 310018 Peoples R China Zhejiang Prov Key Lab Informat Proc Commun & Netw Hangzhou 310027 Peoples R China Zhejiang Sci Tech Univ Sch Informat Sci & Technol Hangzhou 310018 Peoples R China Zhejiang Univ City Coll Coll Informat & Elect Engn Hangzhou 310015 Peoples R China Zhejiang Univ City Coll Artist Design & Creat Sch Hangzhou 310015 Peoples R China Hangzhou Dianzi Univ Machine Learning I Hlth Int Cooperat Base Zhejian Hangzhou 310018 Zhejiang Peoples R China Zhejiang Univ Sch Med Childrens Hosp Hangzhou 310052 Peoples R China Fudan Univ Huashan Hosp Dept Neurol Shanghai 200040 Peoples R China

Over 15% of children with epilepsy belong to benign childhood epilepsy with centro-temporal spikes (BECT), facing with educational difficulties. The accurate recognition of spikes in electroencephalogram (EEG) signals collected from BECT patients can help the doctor to effectively make diagnosis and give therapeutic schedule. Traditionally, template matching method can extract spike-like waves from EEG signals and is adopted by many researches. However, the patterns of the spikes appeared in different patients or different time in one patient varies greatly. The brief proposes a spike detection algorithm based on optimal template matching and morphological feature selection, which includes universal template matching, spike clustering, universal template optimization based on particle swarm optimization (PSO) algorithm and false positive spike (FPS) elimination based on spike morphological feature. Based on the testing EEG data set adopted in this brief, the sensitivity (Sen), specificity (Spe) and accuracy (AC) of the proposed algorithm reaches 98.2%, 95.1% and 96.5%, respectively.

关键词： Electroencephalography Feature extraction Clustering algorithms signal processing algorithms Detection algorithms Pediatrics Morphology BECT spikes PSO algorithm FPS elimination spike morphological feature

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q-Renyi Kernel Functioned Kalman Filter for Land Vehicle Navigation

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2022年第11期69卷 4598-4602页

作者： Xing, Jiangnan Jiang, Tao Li, Yingsong Harbin Engn Univ Coll Informat & Commun Engn Harbin 150001 Peoples R China Anhui Univ Key Lab Intelligent Comp & Signal Proc Minist Educ Hefei 230601 Peoples R China

In this brief, a q-Renyi kernel functioned Kalman filter (qRKFKF) is proposed based on the q-Renyi kernel function which is to provide better flexibility and performance in non-Gaussian environment. The concrete realization of the proposed qRKFKF are created and analyzed in detail, and its performance is presented and discussed. Three examples are carried out to verify the performance of the proposed qRKFKF for land vehicle navigation via numerical simulations: large outliers in non-Gaussian noise, alpha-stable noise and diverse noise distributions that combines Gaussian and Laplace noises. Compared with Kalman filter (KF), Maximum Correntropy Kalman Filter (MCKF) and minimum error entropy KF (MEE-KF), the qRKFKF is superior to others for combating non-Gaussian noises.

关键词： Kernel Kalman filters Estimation signal processing algorithms Mathematical models Covariance matrices Computational complexity Kalman filter q-Renyi kernel function non-Gaussian measurement noise large outlier

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