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9th IEEE International Workshop on Advances in Sensors and Interfaces (IWASI)

作者： Pugliese, Luigi Guagnano, Michele Groppo, Sara Violante, Massimo Groppo, Riccardo Politecn Torino Turin Italy Sleep Advice Technol SAT Turin Italy

ISBN: (纸本)9798350336948

Obstructive Sleep Apnea Syndrome (OSAS) is a common sleep disorder characterized by repeated episodes of breathing cessation during sleep. It affects the quality of life and can lead to severe health complications. Continuous monitoring of Heart Rate Variability (HRV) and oxygen saturation (SpO2) can provide valuable insights into the presence and severity of sleep apnea. The algorithm herein proposed aims to identify the presence of OSAS and then to highly accurately differentiate (Severe, Moderate or Low) its severity level. The algorithm was evaluated on an online dataset;at the end of the algorithm assessment, a correlation coefficient of 98.65% was reached.

关键词： OSAS OSA Obstructive Sleep Apnea Hypopnea IOT Smartwatch Algorithm Classification algorithms Sensors Electrocardiography signal processing algorithms Hardware

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Non-Coherent DOA Estimation via Majorization-Minimization Using Sign Information

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IEEE signal processing LETTERS 2022年 29卷 892-896页

作者： Delbari, Mohamadreza Javaheri, Amirhossein Zayyani, Hadi Marvasti, Farrokh Sharif Univ Technol Dept Elect Engn Tehran *** Iran Qom Univ Technol QUT Dept Elect & Comp Engn Qom 12345 Iran

In this letter, the problem of non-coherent direction of arrival (DOA) estimation is investigated exploiting the sign of the measurements in order to resolve the inherent ambiguity of the problem. Although the phase values are inaccurate, the sign of real and imaginary parts of the measurements will most likely remain correct under limited phase errors. Furthermore, a new approach for solving the problem is proposed employing a modified version of the Majorization-Minimization (MM) technique, without any prior information about the number of incident signals. Some theoretical analyses of our proposed algorithm are also provided in the paper. Finally, the simulation results are presented, demonstrating the efficiency of the proposed algorithm in comparison to some state-of-the-art methods in the literature.

关键词： Direction-of-arrival estimation Estimation signal processing algorithms signal to noise ratio signal resolution Simulation Sensor arrays DOA estimation majorization-minimization non-coherent sign measurements sparse recovery

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A Variable Step Size Adaptive Algorithm With Simple Parameter Selection

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IEEE signal processing LETTERS 2022年 29卷 1774-1778页

作者： Tiglea, Daniel G. Candido, Renato Silva, Magno T. M. Univ Sao Paulo Elect Syst Engn Dept Escola Politecn BR-05508010 Sao Paulo Brazil

We propose a normalized least mean squares algorithm with variable step size. Unlike other solutions, it has low computational cost, only three parameters that are simple to choose, and its steady-state performance can be easily predicted. Simulations show a competitive performance in comparison with other solutions, and validate our theoretical analysis.

关键词： Steady-state signal processing algorithms Convergence Adaptive filters Filtering algorithms Prediction algorithms Transient analysis Adaptive filtering normalized least mean squares steady-state analysis variable step size

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Adaptive Kernel Kalman Filter Based Belief Propagation Algorithm for Maneuvering Multi-Target Tracking

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IEEE signal processing LETTERS 2022年 29卷 1452-1456页

作者： Sun, Mengwei Davies, Mike E. Proudler, Ian K. Hopgood, James R. Univ Edinburgh Inst Digital Commun Edinburgh EH93FG Midlothian Scotland Univ Strathclyde Dept Elect & Elect Engn Ctr Signal & Image Proc CeSIP Glasgow G1 1XW Lanark Scotland

This letter incorporates the adaptive kernel Kalman filter (AKKF) into the belief propagation (BP) algorithm for Multi-target tracking (MTT) in single-sensor systems. The algorithm is capable of tracking an unknown and time-varying number of targets, in the presence of false alarms, clutter and measurement-to-target association uncertainty. Experiment results reveal that the proposed method has a favourable tracking performance using the generalized optimal sub-patten assignment (GOSAP) metrics at substantially less computation cost than the particle filter (PF) based Multi-target tracking (MTT) BP algorithm.

关键词： signal processing algorithms Target tracking Probability density function Kalman filters Kernel Proposals Covariance matrices Adaptive kernel Kalman filter belief propagation data association multi-target tracking

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Full Mean-Square Analysis of Affine Combination of Two Complex-Valued LMS Filters for Second-Order Non-Circular Inputs

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IEEE signal processing LETTERS 2022年 29卷 1157-1161页

作者： Peng, Yishu Zhang, Sheng Zhou, Zhengchun Xia, Yili Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu 611756 Sichuan Peoples R China Southeast Univ Sch Informat Sci & Engn Nanjing 210096 Peoples R China

The affine combination of two complex-valued least-mean-squares filters (aff-CLMS) addresses the trade-off between fast convergence rate and small steady-state misadjustment error. However, a rigorous analysis of the aff-CLMS algorithm for second-order non-circular inputs is still under investigation. To this end, the focus in this letter is on the full mean-square analysis of the aff-CLMS algorithm, in which the transient analyses of the mixing parameter, as well as the standard and complementary weight-error covariance matrices, are completed. In addition, we derive the closed-form solutions of the steady-state weight-error power and its complementary version of the aff-CLMS. Finally, the effectiveness of the theoretical analysis is supported by computer simulations.

关键词： Steady-state signal processing algorithms Performance analysis Covariance matrices Convergence Transient analysis Standards Adaptive filter affine combination non-circularity full mean-square convergence analysis

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Sparse Graphic Equalizer Design

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IEEE signal processing LETTERS 2022年 29卷 1659-1663页

作者： Antonelli, Mario Liski, Juho Valimaki, Vesa MBDA Syst Italy I-00131 Rome Italy Aalto Univ Dept Signal Proc & Acoust Acoust Lab FI-00076 Espoo Finland

A typical graphic equalizer frequency resolution is one-third octave comprising 31 bands. A previous design based on a least-squares optimization of the band-filter gains with a single second-order section per band has an accuracy of 1 dB. However, the design always uses all the band filters even when a small number of gains is adjusted. This letter proposes a sparse design of a one-third-octave graphic equalizer, where the number of active bands is minimized using orthogonal matching pursuit and linear programming before the least-squares gain optimization. In addition, the bandwidths of the high-frequency band filters are gain dependent in order to optimize their shape relative to an analog prototype. The optimal bandwidth is obtained with linear interpolation during the filter design. The proposed design achieves approximately the same or better accuracy in comparison to the state-of-the-art non-sparse design and can be used to automatically reduce the computational load of equalization when only some band filters are active.

关键词： Gain IIR filters Digital filters Matching pursuit algorithms Matched filters Bandwidth signal processing algorithms Audio systems digital filters optimization methods sparse matrices

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A Convex Optimization Approach For NLOS Error Mitigation in TOA-Based Localization

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IEEE signal processing LETTERS 2022年 29卷 677-681页

作者： Wu, Huafeng Liang, Linian Mei, Xiaojun Zhang, Yuanyuan Shanghai Maritime Univ Merchant Marine Coll Shanghai 201306 Peoples R China Shanghai Maritime Univ Coll Informat Engn Shanghai 201306 Peoples R China Univ Victoria Dept Elect & Comp Engn Victoria BC V8W 2Y2 Canada

This paper addresses the target localization problem using time-of-arrival (TOA)-based technique under the non-line-of-sight (NLOS) environment. To alleviate the adverse effect of the NLOS error on localization, a total least square framework integrated with a regularization term (RTLS) is utilized, and with which the localization problem can get rid of the ill-posed issue. However, it is challenging to figure out the exact solution for the considered localization problem. In this case, we convert the RTLS problem into a semidefinite program (SDP), and then obtain the solution of the original problem by solving a generalized trust region subproblem (GTRS). The proposed method has a relatively good robustness in localization even under the circumstance that the prior knowledge of the NLOS links or its distribution does not know. The outperformance of the proposed method is demonstrated in the simulations compared with other state-of-the-art techniques.

关键词： Location awareness Mathematical models Computational complexity Optimization Noise measurement signal processing algorithms Measurement uncertainty Non-line-of-sight (NLOS) time-of-arrive (TOA) target localization regularized total least square (RTLS) wireless sensor network

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Subspace Decomposition Based Adaptive Density Peak Clustering for Radar signals Sorting

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IEEE signal processing LETTERS 2022年 29卷 424-428页

作者： Lang, Ping Fu, Xiongjun Cui, Zongding Feng, Cheng Chang, Jiayun Beijing Inst Technol Sch Integrated Circuits & Elect Beijing 100081 Peoples R China

Radar signal sorting (RSS) plays an important role in the electronic support measurement system. However, the existing clustering-based RSS methods depend heavily on prior knowledge to achieve excellent performance, which may bring severe challenges to RSS in actual scenarios. This letter proposes a novel subspace decomposition based adaptive density peak clustering (SD-ADPC) method to address the problems of low accuracy and high computational cost in RSS. First, the original complex radar signal data is directly decomposed into two-dimensional (2D) subspace by t-distributed random neighborhood embedding (t-SNE). Then, based on the outlier detection of the products between the peak density and the distance of the data points, ADPC is used to adaptively determine the optimal clustering centers of the original data in 2D subspace. Finally, the reminding data is assigned to its nearest cluster with Euclidean distance in one step. The experimental results of the simulated RSS dataset and the open baselines show that our proposed method does not require any knowledge and can achieve better or competitive performance in terms of accuracy and computational cost, compared to existing state-of-the-art methods.

关键词： Radar Sorting Anomaly detection Computational efficiency signal processing algorithms Reconnaissance Radio frequency Electronic warfare density peak clustering electronic support measurement radar signal sorting

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Fast First-Order Algorithm for Large-Scale Max-Min Fair Multi-Group Multicast Beamforming

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IEEE WIRELESS COMMUNICATIONS LETTERS 2022年第8期11卷 1560-1564页

作者： Zhang, Chong Dong, Min Liang, Ben Univ Toronto Dept Elect & Comp Engn Toronto ON M5S 1A1 Canada Ontario Tech Univ Dept Elect Comp & Software Engn Oshawa ON L1G 0C5 Canada

We propose a first-order fast algorithm for the weighted max-min fair (MMF) multi-group multicast beamforming problem in large-scale systems. Utilizing the optimal multicast beamforming structure obtained recently, we convert the nonconvex MMF problem into a min-max weight minimization problem and show that it is a weakly convex problem. We propose using the projected subgradient algorithm (PSA) to solve the problem directly, instead of the conventional method that requires iteratively solving its inverse problem. We show that PSA for our problem has closed-form updates and thus is computationally cheap. Furthermore, PSA converges to a near-stationary point of our problem within finite time. Simulation results show that our PSA-based algorithm offers near-optimal performance with considerably lower computational complexity than existing methods for large-scale systems.

关键词： Array signal processing signal to noise ratio Multicast algorithms Interference Quality of service Optimization signal processing algorithms Multicast beamforming optimal beamforming structure large scale projected subgradient weakly convex optimization

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A Modified EKF for Vehicle State Estimation With Partial Missing Measurements

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IEEE signal processing LETTERS 2022年 29卷 1594-1598页

作者： Yuan, Hongwei Song, Xinmin Shandong Normal Univ Dept Informat Sci & Engn Jinan 250358 Peoples R China

During vehicle driving, all aspects of data monitorings are not accurate enough for the vehicle, and there may be packet loss of measurement data. In addition, the accuracy of vehicle data is more difficult to guarantee when the vehicle state is continuously changing, which may lead to some potential safety hazards during driving. Consequently, many algorithms, which only use the statistical characteristics of packet loss information, have been proposed to improve the accuracy. However, with the rapid development of technology, the time-stamp technique in sensor networks can obtain packet loss information at the current moment. In contrast, although the time-stamp technique can effectively improve the filter performance, it cannot analyze the convergence of the Riccati equation. Therefore, this paper proposes a modified EKF algorithm for balancing these two algorithms, and meanwhile, simulation experiments test and verify the effectiveness and feasibility of our proposed algorithm.

关键词： signal processing algorithms Packet loss Kalman filters State estimation Heuristic algorithms Wheels Mathematical models Kalman fliter packet dropping time-stamp vehicle estimation

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