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Millimeter-Wave Radar Cane: A Blind People Aid With Moving Human Recognition Capabilities

IEEE JOURNAL OF ELECTROMAGNETICS RF AND MICROWAVES IN MEDICINE AND BIOLOGY 2022年第2期6卷 204-211页

作者： Cardillo, Emanuele Li, Changzhi Caddemi, Alina Univ Messina Dept Engn I-98100 Messina Italy Texas Tech Univ Dept Elect & Comp Engn Lubbock TX 79409 USA

This contribution presents an electronic travel aid for blind and visually-impaired people based on a millimeter wave radar and a traditional white-cane. It not only represents an advanced system able to warn the user of potential obstacles, but also discerns between human and nonhuman targets. Since real scenarios would likely have moving targets, a new range alignment technique has been proposed with the aim of detecting the tiny chest displacement due to the physiological activity as the key sign of the human presence. The proposed system is able to properly recognize humans in complex environments with multiple moving targets, thus providing to the user a complete set of information, namely presence, position and nature of the available targets. The operating principle and the effectiveness of the system are shown both by simulated case studies and by employing a 122 GHz radar board for carrying out suitable measurements.

关键词： Radar Radar detection Millimeter wave radar Chirp signal processing algorithms Physiology History Breathing rate electronic travel aid (ETA) frequency-modulated continuous wave (FMCW) microwave and millimeter-wave radar range alignment range migration visually impaired and blind people vital sign detection

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Distributed Optimization for Rank-Constrained Semidefinite Programs

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IEEE CONTROL SYSTEMS LETTERS 2022年 7卷 103-108页

作者： Pei, Chaoying You, Sixiong Sun, Chuangchuang Dai, Ran Purdue Univ Sch Aeronaut & Astronaut W Lafayette IN 47907 USA Mississippi State Univ Dept Aerosp Engn Starkville MS 39762 USA

This letter develops a distributed optimization framework for solving the rank-constrained semidefinite programs (RCSPs). Since the rank constraint is non-convex and discontinuous, solving an optimization problem with rank constraints is NP-hard and notoriously time-consuming, especially for large-scale RCSPs. In the proposed approach, by decomposing an unknown matrix into a set of submatrices with much smaller sizes, the rank constraint on the original matrix is equivalently transformed into a set of constraints on the decomposed submatrices. The distributed framework allows parallel computation of subproblems while requiring coordination among them to satisfy the coupled constraints. As the scale of every subproblem solved independently is significantly reduced, the decomposition scheme and the distributed framework can be applied to large-scale RCSPs. Moreover, optimality conditions of the proposed distributed optimization algorithm for RCSPs at the converged point are analyzed. Finally, the efficiency and effectiveness of the proposed method are demonstrated via simulation examples for solving the image denoising problem.

关键词： Matrix decomposition Optimization Minimization Linear matrix inequalities Eigenvalues and eigenfunctions Convex functions signal processing algorithms Distributed optimization rank-constrained optimization

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Graph signal Restoration Using Nested Deep Algorithm Unrolling

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 3296-3311页

作者： Nagahama, Masatoshi Yamada, Koki Tanaka, Yuichi Chan, Stanley H. Eldar, Yonina C. Tokyo Univ Agr & Technol Dept Elect Engn & Comp Sci Koganei Tokyo 1848588 Japan Japan Sci & Technol Agcy PRESTO Kawaguchi Saitama 3320012 Japan Tokyo Univ Sci Dept Elect Engn Tokyo 1258585 Japan Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA Weizmann Inst Sci Fac Math & Comp Sci IL-7610001 Rehovot Israel

Graph signal processing is a ubiquitous task in many applications such as sensor, social, transportation and brain networks, point cloud processing, and graph neural networks. Often, graph signals are corrupted in the sensing process, thus requiring restoration. In this paper, we propose two graph signal restoration methods based on deep algorithm unrolling (DAU). First, we present a graph signal denoiser by unrolling iterations of the alternating direction method of multiplier (ADMM). We then suggest a general restoration method for linear degradation by unrolling iterations of Plug-and-Play ADMM (PnP-ADMM). In the second approach, the unrolled ADMM-based denoiser is incorporated as a submodule, leading to a nested DAU structure. The parameters in the proposed denoising/restoration methods are trainable in an end-to-end manner. Our approach is interpretable and keeps the number of parameters small since we only tune graph-independent regularization parameters. We overcome two main challenges in existing graph signal restoration methods: 1) limited performance of convex optimization algorithms due to fixed parameters which are often determined manually. 2) large number of parameters of graph neural networks that result in difficulty of training. Several experiments for graph signal denoising and interpolation are performed on synthetic and real-world data. The proposed methods show performance improvements over several existing techniques in terms of root mean squared error in both tasks.

关键词： signal processing algorithms Image restoration signal restoration Noise reduction Task analysis Symmetric matrices Optimization Graph signal processing signal restoration deep algorithm unrolling Plug-and-Play ADMM

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When the Closest Targets Make the Difference: An Analysis of Data Association Errors

IEEE OPEN JOURNAL OF SIGNAL PROCESSING

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IEEE OPEN JOURNAL OF signal processing 2022年 3卷 372-386页

作者： Marano, Stefano Braca, Paolo Millefiori, Leonardo Maria Willett, Peter Blair, W. Dale Univ Salerno DIEM I-84084 Fisciano Italy NATO CMRE I-19126 La Spezia Italy Univ Connecticut Storrs CT 06269 USA GTRI SEAL Smyrna GA 30318 USA

Multi-object data fusion - combining measurements or estimates of more than one truth object from more than one observer - requires a first "data association" step of deciding which data have common truth objects. Here we focus on the case of two observers only, with the data association engine powered by a polynomially-complex list-matching algorithm such as of Jonker-Volgenant-Castanon (JVC), auction or Munkres. The paper's purpose is to develop an approximation for the probability of assignment error: How often does the data association engine tell the fuser to combine data from truth objects that do not go together? We assume data with Gaussian errors and a Poisson field of truth objects, and we focus on the low-noise case where errors are infrequent and fusion makes sense. In this article, for isotropic, independent identically distributed errors, a single scalar parameter representative of the scene complexity is identified and, exploiting that, a reasonably simple approximate expression for the association error is derived.

关键词： Switches Noise measurement Measurement uncertainty Costs signal processing algorithms signal processing Sea measurements Radar tracking sensor fusion

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Self-Supervised Learning Based Anomaly Detection in Synthetic Aperture Radar Imaging

IEEE OPEN JOURNAL OF SIGNAL PROCESSING

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IEEE OPEN JOURNAL OF signal processing 2022年 3卷 440-449页

作者： Muzeau, Max Ren, Chengfang Angelliaume, Sebastien Datcu, Mihai Ovarlez, Jean-Philippe Univ Paris Saclay SONDRA CentraleSupelec F-91190 Gif Sur Yvette France Univ Paris Saclay DEMR CentraleSupelec F-91190 Gif Sur Yvette France Univ Politehn Bucharest UPB Romania & German Aerosp Ctr DLR D-82234 Wessling Germany

In this paper, we proposed to investigate unsupervised anomaly detection in Synthetic Aperture Radar (SAR) images. Our approach considers anomalies as abnormal patterns that deviate from their surroundings without prior knowledge of their characteristics. This method deals with the crucial problems related to the presence of speckle, the spatial correlation structures in SAR images, and the lack of annotated data to train a detection algorithm. Our proposed method aims to address these issues through a self-supervised learning algorithm. First, we propose to mitigate the SAR speckle through the deep learning SAR2SAR algorithm. We then develop an Adversarial Autoencoder (AAE) to reconstruct anomaly-free SAR images from despeckled data taking into account potential spatial correlation structures. Finally, a change detection processing step is applied between the input and the output to detect anomalies. Experiments are performed to show the advantages of our method compared to the conventional Reed-Xiaoli algorithm, highlighting the importance of an efficient despeckling pre-processing step.

关键词： Radar polarimetry Speckle Synthetic aperture radar Anomaly detection Correlation Spaceborne radar signal processing algorithms Adversarial autoencoder anomaly detection deep-learning despeckling SAR self-supervised

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Sidelobe Mitigation in Noise Radar Using Sparse signal processing

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IEEE AEROSPACE AND ELECTRONIC SYSTEMS MAGAZINE 2020年第9期35卷 32-40页

作者： de Martin, Lilian van Rossum, Wim Ribeiro, Diogo Anitori, Laura TNO Radar Technol The Hague Netherlands

It is well known that noise-like waveforms have the inherent property of suppressing ambiguities due to their nonrepetitive nature, as opposed to the more classical linear frequency modulated (LFM) pulsed waveforms. This aperiodicity also improves the robustness against jammers, making these waveforms harder to detect and classify by an enemy. However, noise waveforms are Doppler sensitive, requiring the use of Doppler-dependent matched filters for proper range compression. Also, when processed with classical matched filters, noise waveforms have sidelobes that spread out across the whole range-Doppler plane, potentially masking weak targets in the presence of strong ones. A possible solution to reduce the sidelobe interference is to use sparse signal processing (SSP). In this article, we demonstrate with simulated and experimental data how noise waveforms can be used in combination with SSP for unambiguous detection of strong as well as weak targets, improving upon the performance of more conventional LFM pulsed waveforms.

关键词： Doppler effect Radar Matching pursuit algorithms Radar signal processing signal processing algorithms Computational modeling Matched filters Frequency modulation Jamming Noise measurement

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Two Double Recursive Block Macaulay Matrix algorithms to Solve Multiparameter Eigenvalue Problems

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IEEE CONTROL SYSTEMS LETTERS 2022年 7卷 319-324页

作者： Vermeersch, Christof De Moor, Bart Katholieke Univ Leuven Ctr Dynam Syst Signal Proc & Data Analyt STADIUS Dept Elect Engn ESAT B-3001 Leuven Belgium

We present two double recursive block Macaulay matrix algorithms to solve multiparameter eigenvalue problems (MEPs). In earlier work, we have developed a non-recursive approach that finds the solutions of an MEP via a multidimensional realization problem in the null space of the block Macaulay matrix constructed from the coefficient matrices of that MEP. However, this approach requires an iterative increase of the degree of the block Macaulay matrix: in order to determine whether the null space contains all the (affine) solutions of the MEP, we need to compute a basis matrix of the null space for every degree and check its dimension or rank structure. In this letter, we employ a recursive/sparse technique to compute a basis matrix of the null space of the block Macaulay matrix and a recursive technique to perform the necessary rank checks. We provide two system identification examples to show our improvements in computation time and memory usage.

关键词： Eigenvalues and eigenfunctions Null space Mathematical models Sparse matrices signal processing algorithms Indexes Standards Numerical algorithms identification

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Learning Time-Varying Graphs From Online Data

IEEE OPEN JOURNAL OF SIGNAL PROCESSING

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IEEE OPEN JOURNAL OF signal processing 2022年 3卷 212-228页

作者： Natali, Alberto Isufi, Elvin Coutino, Mario Leus, Geert Delft Univ Technol Fac Elect Engn Math & Comp Sci NL-2628 CD Delft Netherlands TNO Radar Technol NL-2509 The Hague Netherlands

This work proposes an algorithmic framework to learn time-varying graphs from online data. The generality offered by the framework renders it model-independent, i.e., it can be theoretically analyzed in its abstract formulation and then instantiated under a variety of model-dependent graph learning problems. This is possible by phrasing (time-varying) graph learning as a composite optimization problem, where different functions regulate different desiderata, e.g., data fidelity, sparsity or smoothness. Instrumental for the findings is recognizing that the dependence of the majority (if not all) data-driven graph learning algorithms on the data is exerted through the empirical covariance matrix, representing a sufficient statistic for the estimation problem. Its user-defined recursive update enables the framework to work in non-stationary environments, while iterative algorithms building on novel time-varying optimization tools explicitly take into account the temporal dynamics, speeding up convergence and implicitly including a temporal-regularization of the solution. We specialize the framework to three well-known graph learning models, namely, the Gaussian graphical model (GGM), the structural equation model (SEM), and the smoothness-based model (SBM), where we also introduce ad-hoc vectorization schemes for structured matrices (symmetric, hollows, etc.) which are crucial to perform correct gradient computations, other than enabling to work in low-dimensional vector spaces and hence easing storage requirements. After discussing the theoretical guarantees of the proposed framework, we corroborate it with extensive numerical tests in synthetic and real data.

关键词： Mathematical models Topology Covariance matrices Network topology signal processing algorithms signal processing Optimization Graph topology identification dynamic graph learning network topology inference graph signal processing

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Community-Aware Graph signal processing: Modularity Defines New Ways of processing Graph signals

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IEEE signal processing MAGAZINE 2020年第6期37卷 150-159页

作者： Miljan, Petrovic Liegeois, Raphael Bolton, Thomas A. W. van de Ville, Dimitri Med Image Proc Lab MIPLab Lausanne Switzerland Ecole Polytech Fed Lausanne Med Image Proc Lab Lausanne Switzerland Ecole Polytech Fed Lausanne Lausanne Switzerland Ecole Polytech Fed Lausanne Bioengn Lausanne Switzerland Univ Geneva Geneva Switzerland

The emerging field of graph signal processing (GSP) allows one to transpose classical signal processing operations (e.g., filtering) to signals on graphs. The GSP framework is generally built upon the graph Laplacian, which plays a crucial role in studying graph properties and measuring graph signal smoothness. Here, instead, we propose the graph modularity matrix as the centerpiece of GSP to incorporate knowledge about graph community structure when processing signals on the graph but without the need for community detection. We study this approach in several generic settings, such as filtering, optimal sampling and reconstruction, surrogate data generation, and denoising. Feasibility is illustrated by a small-scale example and a transportation network data set as well as one application in human neuroimaging where community-aware GSP reveals relationships between behavior and brain features that are not shown by Laplacian-based GSP. This work demonstrates how concepts from network science can lead to new, meaningful operations on graph signals.

关键词： Communities signal processing algorithms Laplace equations Neuroimaging Graphical models

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signal processing Methods to Interpret Polychlorinated Biphenyls in Airborne Samples

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IEEE ACCESS 2020年 8卷 147738-147755页

作者： McCarthy, Ryan A. Gupta, Ananya Sen Kubicek, Bernice Awad, Andrew M. Martinez, Andres Marek, Rachel F. Hornbuckle, Keri C. Univ Iowa Dept Elect & Comp Engn Iowa City IA 52242 USA Univ Iowa Iowa Technol Inst ITI Iowa City IA 52242 USA Univ Iowa Dept Civil & Environm Engn Iowa City IA 52242 USA Univ Iowa IIHR Hydrosci & Engn Iowa City IA 52242 USA

The main contribution of this interdisciplinary work is a robust computational framework to autonomously discover and quantify previously unknown associations between well-known (target) and potentially unknown (non-target) toxic industrial air pollutants. In this work, the variability of polychlorinated biphenyl (PCB) data is evaluated using a combination of statistical, signal processing, and graph-based informatics techniques to interpret the raw instrument signal from gas chromatography-mass spectrometry (GC/MS/MS) data sets. Specifically, minimum mean-squared techniques from the adaptive signal processing literature are extended to detect and separate coeluted (overlapped) peaks in the raw instrument signal. A graph-based visualization is provided which bridges two complementary approaches to quantitative pollution studies: (i) peak-cognizant target analysis (limits data analysis to few well-known compounds) and (ii) chemometric analysis (statistical large-scale data analysis) that is agnostic of specific compounds. Further, peak fitting techniques based on L2 error minimization are employed to autonomously calculate the amount of each PCB present with a normalized mean square error of -18.4851 dB. Graph-based visualization of associations between known and unknown compounds are developed through principal component analysis and both fuzzy c-means (FCM) and k-means clustering techniques are implemented and compared. The efficiency of these methods are compared using 150 air samples analyzed for individual PCBs with GC/MS/MS against traditional target-only techniques that perform analysis across only the known (target) PCBs. Parameter optimization techniques are employed to evaluate the relative contribution of PCB signals against ten potential source signals representing legacy signatures from historical manufacture of Aroclors and modern sources of PCBs produced as byproducts of pigment and polymer manufacturing. Aroclors 1232, 1254, 1016, and 1221 as well as

关键词： Instruments Compounds signal processing Principal component analysis Chemicals Load modeling signal processing algorithms Identifying sources interpreting GC MS MS PCBs signal processing

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