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Network Topology Inference From Heterogeneous Incomplete Graph Signals IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2020-12-02 Xiao Yang; Min Sheng; Yanli Yuan; Tony Q.S. Quek
Inferring network topologies from observed graph-structured data (also known as graph signals) is a crucial task in many applications of network science. Existing papers on network topology inference typically assume that the observations at all nodes are available. However, there are many situations where only partial observations can be collected due to application-specific constraints. To handle
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Distributed Auxiliary Particle Filtering With Diffusion Strategy for Target Tracking: A Dynamic Event-Triggered Approach IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2020-12-09 Weihao Song; Zidong Wang; Jianan Wang; Fuad E. Alsaadi; Jiayuan Shan
This paper investigates the particle filtering problem for a class of nonlinear/non-Gaussian systems under the dynamic event-triggered protocol. In order to avert frequent data transmission and reduce the communication overhead, a dynamic event-triggered transmission mechanism is adopted to decide whether the data should be transmitted or not. We first consider a scenario where all sensor nodes selectively
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Time Encoding of Bandlimited Signals: Reconstruction by Pseudo-Inversion and Time-Varying Multiplierless FIR Filtering IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2020-12-10 Nguyen T. Thao; Dominik Rzepka
We propose an entirely redesigned framework of bandlimited signal reconstruction for the time encoding machine (TEM) introduced by Lazar and Tóth. As the encoding part of TEM consists in obtaining integral values of a bandlimited input over known time intervals, it theoretically amounts to applying a known linear operator on the input. We then approach the general question of signal reconstruction
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Robust Variational-Based Kalman Filter for Outlier Rejection With Correlated Measurements IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2020-12-08 Haoqing Li; Daniel Medina; Jordi Vilà-Valls; Pau Closas
State estimation is a fundamental task in many engineering fields, and therefore robust nonlinear filtering techniques able to cope with misspecified, uncertain and/or corrupted models must be designed for real-life applicability. In this contribution we explore nonlinear Gaussian filtering problems where measurements may be corrupted by outliers, and propose a new robust variational-based filtering
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Cascaded Spline-Based Models for Complex Nonlinear Systems: Methods and Applications IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2020-12-23 Pablo Pascual Campo; Lauri Anttila; Dani Korpi; Mikko Valkama
In this paper, we present a class of cascaded nonlinear models for complex-valued system identification, aimed at baseband modeling of nonlinear radio systems. The proposed models consist of serially connected elementary linear and nonlinear blocks, with the nonlinear blocks implemented as uniform spline-interpolated look-up tables (LUT) and the linear blocks as FIR filters. Wiener, Hammerstein, and
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Subspace-Based Estimation of Rapidly Varying Mobile Channels for OFDM Systems IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2020-12-17 Habib Şenol; Cihan Tepedelenlioğlu
It is well-known that time-varying channels can provide time diversity and improve error rate performance compared to time-invariant fading channels. However, exploiting time diversity requires very accurate channel estimates at the receiver. In order to reduce the number of unknown channel coefficients while estimating the time-varying channel, basis expansion models can be used along with long transmission
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Task Offloading for Large-Scale Asynchronous Mobile Edge Computing: An Index Policy Approach IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2020-12-22 Yizhen Xu; Peng Cheng; Zhuo Chen; Ming Ding; Yonghui Li; Branka Vucetic
Mobile-edge computing (MEC) offloads computational tasks from wireless devices to network edge, and enables real-time information transmission and computing. Most existing work concerns a small-scale synchronous MEC system. In this paper, we focus on a large-scale asynchronous MEC system with random task arrivals, distinct workloads, and diverse deadlines. We formulate the offloading policy design
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Remote sensing image retrieval by integrating automated deep feature extraction and handcrafted features using curvelet transform J. Appl. Remote Sens. (IF 1.36) Pub Date : 2021-01-01 Sudheer Devulapalli; Rajakumar Krishnan
Deep learning techniques have become increasingly popular for classifying large-scale image and video data. Remote sensing applications require robust search engines to retrieve similar information dependent on an example-based query instead of a tag-based query. Deep features can be extracted automatically by training raw data without having any domain-specific knowledge. However, the training time
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2020 List of Reviewers J. Appl. Remote Sens. (IF 1.36) Pub Date : 2021-01-01
This is a list of reviewers who served the Journal of Applied Remote Sensing in 2020.
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Knowledge-aided covariance estimation and radar adaptive detection J. Appl. Remote Sens. (IF 1.36) Pub Date : 2021-01-01 Ke Jin; Hongmin Zhang; Jizhou Wu; Tao Lai; Yongjun Zhao
We address the covariance matrix estimation problem for radar adaptive detection in a non-Gaussian clutter environment. We first propose an estimation method based on α log-determinant divergence, which estimates the true covariance accurately by solving the geometric mean of the sample covariance matrix (SCM). Since the estimation performance would be seriously degraded when the number of secondary
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Convolutional Neural Network-Aided Tree-Based Bit-Flipping Framework for Polar Decoder Using Imitation Learning IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2020-11-27 Chieh-Fang Teng; An-Yeu Andy Wu
Known for their capacity-achieving abilities and low complexity for both encoding and decoding, polar codes have been selected as the control channel coding scheme for 5G communications. To satisfy the needs of high throughput and low latency, belief propagation (BP) is chosen as the decoding algorithm. However, it suffers from worse error performance than that of cyclic redundancy check (CRC)-aided
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Nonlocal weighted sparse unmixing based on global search and parallel optimization J. Appl. Remote Sens. (IF 1.36) Pub Date : 2021-01-01 Yongxin Li; Wenxing Bao; Kewen Qu; Xiangfei Shen
Sparse unmixing (SU) can represent an observed image using pure spectral signatures and corresponding fractional abundance from a large spectral library and is an important technique in hyperspectral unmixing. However, the existing SU algorithms mainly exploit spatial information from a fixed neighborhood system, which is not sufficient. To solve this problem, we propose a nonlocal weighted SU algorithm
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Maneuvering platform high-squint SAR imaging method based on perturbation KT and subregion phase filtering J. Appl. Remote Sens. (IF 1.36) Pub Date : 2021-01-01 Gen Li; Yanheng Ma; Xuying Xiong
The imaging parameters of high-squint synthetic aperture radar (SAR) mounted on maneuvering platforms have obvious spatial variability, which cannot be effectively solved by traditional SAR imaging algorithms and limits the focus depth. To extend the focus depth of maneuvering SAR, an imaging method is proposed based on perturbation keystone transform (KT) and subregion phase filtering (SRPF). In the
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Remote sensing for assessing vegetated roofs with a new replicable method in Paris, France J. Appl. Remote Sens. (IF 1.36) Pub Date : 2021-01-01 Tanguy Louis-Lucas; Flavie Mayrand; Philippe Clergeau; Nathalie Machon
Vegetated roofs provide many ecosystem services and support urban biodiversity. While it would be interesting to study the contribution of vegetated roofs to ecological corridors, vegetated roofs are listed in no French databases. Because of their intrinsic nature as roofs, their small number, their small size, and the type of vegetation planted on them, vegetated roofs seem to be very difficult to
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Toward high-performance SPAD arrays for space-based atmosphere and ocean profiling LiDARs J. Appl. Remote Sens. (IF 1.36) Pub Date : 2021-01-01 Giulia Acconcia; Andrea Giudici; John A. Smith; Ivan Labanca; Rich J. Hare; Massimo Ghioni; Ivan Rech
Space-based light detection and ranging (LiDAR) sensors have provided valuable insight into the global, vertical distribution of aerosol and cloud layers in Earth’s atmosphere, and, more recently, of the distribution of phytoplankton in the ocean. However, the photodetectors in these sensors lack the performance necessary to capture the vertical structure of cloud tops and ocean phytoplankton to a
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Learning to Demodulate From Few Pilots via Offline and Online Meta-Learning IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2020-12-10 Sangwoo Park; Hyeryung Jang; Osvaldo Simeone; Joonhyuk Kang
This paper considers an Internet-of-Things (IoT) scenario in which devices sporadically transmit short packets with few pilot symbols over a fading channel. Devices are characterized by unique transmission non-idealities, such as I/Q imbalance. The number of pilots is generally insufficient to obtain an accurate estimate of the end-to-end channel, which includes the effects of fading and of the transmission-side
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Reduced-Rank L1-Norm Principal-Component Analysis With Performance Guarantees IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2020-11-23 Hossein Kamrani; Alireza Zolghadr Asli; Panos P. Markopoulos; Michael Langberg; Dimitris A. Pados; George N. Karystinos
Standard Principal-Component Analysis (PCA) is known to be sensitive to outliers among the processed data. On the other hand, L1-norm-based PCA (L1-PCA) exhibits sturdy resistance against outliers, while it performs similar to standard PCA when applied to nominal or smoothly corrupted data [1] . Exact calculation of the $K$ L1-norm Principal Components (L1-PCs) of a rank- $r$ data matrix $\mathbf X
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Shrinking the Eigenvalues of M-Estimators of Covariance Matrix IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2020-12-14 Esa Ollila; Daniel P. Palomar; Frédéric Pascal
A highly popular regularized (shrinkage) covariance matrix estimator is the shrinkage sample covariance matrix (SCM) which shares the same set of eigenvectors as the SCM but shrinks its eigenvalues toward the grand mean of the eigenvalues of the SCM. In this paper, a more general approach is considered in which the SCM is replaced by an M-estimator of scatter matrix and a fully automatic data adaptive
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Optimal Opponent Stealth Trajectory Planning Based on an Efficient Optimization Technique IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2020-12-04 Augusto Aubry; Paolo Braca; Enrica d’Afflisio; Antonio De Maio; Leonardo M. Millefiori; Peter Willett
In principle, the Automatic Identification System (AIS) makes covert rendezvous at sea, such as smuggling and piracy, impossible; in practice, AIS can be spoofed or simply disabled. Previous work showed a means whereby such deviations can be spotted. Here we play the opponent's side, and describe the least-detectable trajectory that the elusive vessel can take. The opponent's route planning problem
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Decentralized Online Convex Optimization With Event-Triggered Communications IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2020-12-17 Xuanyu Cao; Tamer Başar
Decentralized multi-agent optimization usually relies on information exchange between neighboring agents, which can incur unaffordable communication overhead in practice. To reduce the communication cost, we apply event-triggering technique to the decentralized multi-agent online convex optimization problem, where each agent is associated with a time-varying local loss function and the goal is to minimize
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Hedgerow object detection in very high-resolution satellite images using convolutional neural networks J. Appl. Remote Sens. (IF 1.36) Pub Date : 2021-01-01 Steve Ahlswede; Sarah Asam; Achim Röder
Hedgerows are one of the few remaining natural landscape features within European agricultural areas. To facilitate hedgerow monitoring, cost-effective and accurate mapping of hedgerows across large spatial scales is required. Current methods used for automatic hedgerow detection are overly complicated and generalize poorly to larger areas. We examine the application of transfer learning using two
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Online Topology Identification From Vector Autoregressive Time Series IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2020-12-08 Bakht Zaman; Luis Miguel Lopez Ramos; Daniel Romero; Baltasar Beferull-Lozano
Causality graphs are routinely estimated in social sciences, natural sciences, and engineering due to their capacity to efficiently represent the spatiotemporal structure of multi-variate data sets in a format amenable for human interpretation, forecasting, and anomaly detection. A popular approach to mathematically formalize causality is based on vector autoregressive (VAR) models and constitutes
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Edge-guided multispectral image fusion algorithm J. Appl. Remote Sens. (IF 1.36) Pub Date : 2020-12-01 Guihui Li; Jinjiang Li; Hui Fan
Most existing multispectral fusion algorithms often suffer from spectral or spatial information distortion. Driven by this motivation, we propose an edge-guided multispectral (MS) image fusion algorithm. In particular, it combines the advantages of generative adversarial networks and improved fusion frameworks, so the merged image can better preserve the spectral information of the original multispectral
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Hyperspectral anomaly detection using a background endmember signature J. Appl. Remote Sens. (IF 1.36) Pub Date : 2020-12-01 Hongwei Chang; Tao Wang; Aihua Li; Yihe Jiang
Due to lacking use of prior information, the anomaly detection results are not always satisfactory. However, with the establishment of the spectral library, it becomes possible to obtain one or more spectra of the background in the image to be detected. If we can make use of such background information that is always ignored or discarded, the detection result is very likely to be improved. Hence, we
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Destriping and evaluating FY-3D MERSI-2 data with the moment matching method based on synchronous reference image J. Appl. Remote Sens. (IF 1.36) Pub Date : 2020-12-01 Kai Tang; Hongchun Zhu; Yu Cheng; Lin Zhang
Medium Resolution Spectral Imager II (MERSI-2) is a payload of the China meteorological satellite FY-3D. The sensor bands 24 (10.3 to 11.3 μm) and 25 (11.5 to 12.5 μm) images, which are most suitable for land surface temperature (LST) retrieval, have higher spatial resolution than that of similar sensors. However, bands 24/25 images with spatial resolution of 250 m (hereafter bands 24/25 250-m images)
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Statistical Inference for the Expected Utility Portfolio in High Dimensions IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2020-11-16 Taras Bodnar; Solomiia Dmytriv; Yarema Okhrin; Nestor Parolya; Wolfgang Schmid
In this paper, using the shrinkage-based approach for portfolio weights and modern results from random matrix theory we construct an effective procedure for testing the efficiency of the expected utility (EU) portfolio and discuss the asymptotic behavior of the proposed test statistic under the high-dimensional asymptotic regime, namely when the number of assets $p$ increases at the same rate as the
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Particle Filtering for Nonlinear/Non-Gaussian Systems With Energy Harvesting Sensors Subject to Randomly Occurring Sensor Saturations IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2020-12-09 Weihao Song; Zidong Wang; Jianan Wang; Fuad E. Alsaadi; Jiayuan Shan
In this paper, the particle filtering problem is investigated for a class of nonlinear/non-Gaussian systems with energy harvesting sensors subject to randomly occurring sensor saturations (ROSSs). The random occurrences of the sensor saturations are characterized by a series of Bernoulli distributed stochastic variables with known probability distributions. The energy harvesting sensor transmits its
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Waveform Design for Collocated MIMO Radar With High-Mix-Low-Resolution ADCs IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2020-11-23 Ziyang Cheng; Shengnan Shi; Lingyun Tang; Zishu He; Bin Liao
Adopting low-resolution analog-to-digital converters (ADCs) for receive antennas of a multiple-input multiple-output (MIMO) system can remarkably reduce the hardware cost, circuit power consumption as well as amount of data to be transferred from RF components and the baseband-processing unit. However, an obvious performance loss is also expected. Towards this end, in this work we introduce a new architecture
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CPGD: Cadzow Plug-and-Play Gradient Descent for Generalised FRI IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2020-11-27 Matthieu Simeoni; Adrien Besson; Paul Hurley; Martin Vetterli
Finite rate of innovation (FRI) is a powerful reconstruction framework enabling the recovery of sparse Dirac streams from uniform low-pass filtered samples. An extension of this framework, called generalised FRI (genFRI), has been recently proposed for handling cases with arbitrary linear measurement models. In this context, signal reconstruction amounts to solving a joint constrained optimisation
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A Low-Cost Algorithm for Adaptive Sampling and Censoring in Diffusion Networks IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2020-11-16 Daniel G. Tiglea; Renato Candido; Magno T. M. Silva
Distributed signal processing has attracted widespread attention in the scientific community due to its several advantages over centralized approaches. Recently, graph signal processing has risen to prominence, and adaptive distributed solutions have also been proposed in the area. Both in the classical framework and in graph signal processing, sampling and censoring techniques have been topics of
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Bayesian Reconstruction of Fourier Pairs IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2020-11-16 Felipe Tobar; Lerko Araya-Hernández; Pablo Huijse; Petar M. Djurić
In a number of data-driven applications such as detection of arrhythmia, interferometry or audio compression, observations are acquired indistinctly in the time or frequency domains: temporal observations allow us to study the spectral content of signals (e.g., audio), while frequency-domain observations are used to reconstruct temporal/spatial data (e.g., MRI). Classical approaches for spectral analysis
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Generalized Multiplexed Waveform Design Framework for Cost-Optimized MIMO Radar IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2020-11-27 Christian Hammes; Bhavani Shankar M. R.; Björn Ottersten
Cost-optimization through the minimization of hardware and processing costs with minimal loss in performance is an interesting design paradigm in evolving and emerging Multiple-Input-Multiple-Output (MIMO) radar systems. This optimization is a challenging task due to the increasing Radio Frequency (RF) hardware complexity as well as the signal design algorithm complexity in applications requiring high
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On the Solvability of the Peak Value Problem for Bandlimited Signals With Applications IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2020-12-02 Holger Boche; Ullrich J. Mönich
In this paper we study from an algorithmic perspective the problem of finding the peak value of a bandlimited signal. This problem plays an important role in the design and optimization of communication systems. We show that the peak value problem, i.e., computing the peak value of a bandlimited signal from its samples, can be solved algorithmically if oversampling is used. Without oversampling this
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Optimality Conditions of Performance-Guaranteed Power Minimization in MIMO Networks: A Distributed Algorithm and Its Feasibility IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2020-11-06 Guojun Xiong; Taejoon Kim; David J. Love; Erik Perrins
A distributed approach is proposed to the problem of signal-to-interference-plus-noise-ratio (SINR)-guaranteed power minimization (SGPM) for multicell multiuser (MCMU) multiple-input multiple-output (MIMO) systems. Unlike prior SGPM approaches, the proposed technique is based on solving necessary and sufficient optimality conditions, which are derived by decomposing the original problem into forward
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Ridge-Aware Weighted Sparse Time-Frequency Representation IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2020-11-24 Chaowei Tong; Shibin Wang; Ivan Selesnick; Ruqiang Yan; Xuefeng Chen
The ideal time-frequency (TF) representation which distributes the total energy along the instantaneous frequency (IF) of a signal is essentially sparse. Motivated by the weighted sparse representation of the signal, we propose the ridge-aware weighted sparse TF representation (RWSTF) which involves some properties an ideal TF representation should satisfy, such as, highly concentrated TF representation
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Fast Graph Filters for Decentralized Subspace Projection IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2020-11-18 Daniel Romero; Siavash Mollaebrahim; Baltasar Beferull-Lozano; César Asensio-Marco
A number of inference problems with sensor networks involve projecting a measured signal onto a given subspace. In existing decentralized approaches, sensors communicate with their local neighbors to obtain a sequence of iterates that asymptotically converges to the desired projection. In contrast, the present paper develops methods that produce these projections in a finite and approximately minimal
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Adaptive Superresolution in Deconvolution of Sparse Peaks IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2020-11-16 Alexandra Koulouri; Pia Heins; Martin Burger
This paper investigates superresolution in deconvolution driven by sparsity priors. The observed signal is a convolution of an original signal with a continuous kernel. With the prior knowledge that the original signal can be considered as a sparse combination of Dirac delta peaks, we seek to estimate the positions and amplitudes of these peaks by solving a finite dimensional convex problem on a computational
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Expectation Propagation-Based Sampling Decoding: Enhancement and Optimization IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2020-11-25 Zheng Wang; Shanxiang Lyu; Yili Xia; Qihui Wu
In this paper, the paradigm of expectation propagation (EP) algorithm in large-scale MIMO detection is extended by the sampling decoding in an Markov chain Monte Carlo way to boost the approximation of the target posterior distribution. The proposed EP-based sampling decoding scheme not only theoretically addresses the inherent convergence problem of EP, but also is able to achieve the near-optimal
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Two-stage ship detection in synthetic aperture radar images based on attention mechanism and extended pooling J. Appl. Remote Sens. (IF 1.36) Pub Date : 2020-12-01 Chenchen Wang; Weimin Su; Hong Gu
Object detection in synthetic aperture radar (SAR) images remains a challenging problem due to the particular imaging mechanism of SAR systems. The sizes of targets are relatively small and the scenes are large, indicating that the intersection over union value between the targets and anchors is probably small. In addition, SAR images are severely polluted by speckles under normal conditions. The edges
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Stochastic radiation field optimization for microwave staring correlated imaging via spatial correlation minimization J. Appl. Remote Sens. (IF 1.36) Pub Date : 2020-12-01 Zheng Jiang; Jianlin Zhang; Bo Yuan; Yuanyue Guo; Dongjin Wang
Microwave staring correlated imaging (MSCI) is a staring high-resolution microwave imaging technique, employing the temporal–spatial stochastic radiation field (TSSRF). The imaging capability of MSCI depends on the spatial correlation of the TSSRF, which is equivalent to the incoherence of the sensing matrix in discrete form. A waveform design method for MSCI using multifrequency signals to reduce
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Characterizing ecosystem functional type patterns based on subtractive fuzzy cluster means using Sentinel-2 time-series data J. Appl. Remote Sens. (IF 1.36) Pub Date : 2020-12-01 Rong Liu; Fang Huang; Yue Ren; Ping Wang; Jing Zhang
The characteristics of ecosystem functions are of great significance for biodiversity conservation and ecosystem services. Ecosystem functional types (EFTs) are land surface areas similar in carbon dynamics that are not defined by the structure and composition of vegetation and represent the spatial heterogeneity of ecosystem functions. However, identification of EFTs based on low-resolution remote
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Front Cover IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-12-23
Presents the front cover for this issue of the publication.
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IEEE Transactions on Geoscience and Remote Sensing publication information IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-12-23
Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
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Table of contents IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-12-23
Presents the table of contents for this issue of the publication.
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TechRxiv: Share Your Preprint Research with the World! IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-12-23
Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.
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IEEE Transactions on Geoscience and Remote Sensing information for authors IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-12-23
These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.
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IEEE Transactions on Geoscience and Remote Sensing institutional listings IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-12-23
Presents a listing of institutions relevant for this issue of the publication.
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Differential synthetic aperture radar interferometric phase map despeckling in discrete Riesz wavelets domain J. Appl. Remote Sens. (IF 1.36) Pub Date : 2020-12-01 Jamila Fathi; Khalid Ghzala; Elhoucaine Elkharrouba; Yassine Tounsi; Ahmed Siari; Hamid Bioud; Abdelkrim Nassim
Phase extraction in differential synthetic aperture radar (SAR) interferometry (DInSAR) is an important tool used for detecting subcentimeter-level change in ground deformation. The evaluated phase map processing is conducted via two important and successive steps: phase denoising and phase unwrapping. We attack the first step and propose the performance of discrete Riesz wavelets transform to reduce
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A Grant-Free Method for Massive Machine-Type Communication With Backward Activity Level Estimation IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2020-11-24 Han Xiao; Wei Chen; Jun Fang; Bo Ai; Ian J. Wassell
Massive machine type communications (mMTC) is one of the three major scenarios of the fifth generation (5G) communication system, and raises new challenges for the development of new radio access technology. Unlike human type communications (HTC), mMTC is typically characterised by a massive number of devices, small-sized packets, low or no mobility, low energy consumption and sporadic transmission
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A Closed-Form Estimator for Bearings-Only Fusion of Heterogeneous Passive Sensors IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2020-11-09 Sanjeev Arulampalam; Laleh Badriasl; Branko Ristic
Bearings-only target motion analysis with fusion of heterogeneous passive sensors with and without propagation delay attempts to estimate a target trajectory by fusing bearings from two sensors: a sensor whose measurements have negligible propagation delay and a sensor with measurements that have non-negligible propagation delay. This problem is highly nonlinear and challenging, particularly due to
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Approximation Algorithms for Training One-Node ReLU Neural Networks IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2020-11-24 Santanu S. Dey; Guanyi Wang; Yao Xie
Training a one-node neural network with the ReLU activation function via optimization, which we refer to as the ON-ReLU problem, is a fundamental problem in machine learning. In this paper, we begin by proving the NP-hardness of the ON-ReLU problem. We then present an approximation algorithm to solve the ON-ReLU problem, whose running time is $\mathcal {O}(n^k)$ where $n$ is the number of samples,
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GoSPo: a goniospectropolarimeter to assess reflectance, transmittance, and surface polarization as related to leaf optical properties J. Appl. Remote Sens. (IF 1.36) Pub Date : 2020-12-01 Reisha D. Peters; Simone R. Hagey; Scott D. Noble
Visible-near infrared (VIS-NIR) spectral data are widely used for remotely estimating a number of crop health metrics. In general, these indices and models do not explicitly account for leaf surface characteristics, which themselves can be indicators of plant status or environmental responses. To explicitly include leaf surface characteristics, data are required linking optical properties to surface
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Airborne radiometric validation of the geostationary lightning mapper using the Fly’s Eye GLM Simulator J. Appl. Remote Sens. (IF 1.36) Pub Date : 2020-12-01 Mason G. Quick; Hugh J. Christian; Katrina S. Virts; Richard J. Blakeslee
The Fly’s Eye GLM Simulator (FEGS) is a multi-spectral array of radiometers designed to provide a validation dataset for the geostationary lightning mapper (GLM). The main component of FEGS is a 5 × 5 grid of radiometers, each with a square 18 deg field of view, that are sensitive to a 10-nm wide spectral band centered on 780 nm to observe a neutral atomic oxygen emission triplet at 777.4 nm. FEGS
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Defining Fundamental Frequency for Almost Harmonic Signals IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2020-11-06 Filip Elvander; Andreas Jakobsson
In this work, we consider the modeling of signals that are almost, but not quite, harmonic, i.e., composed of sinusoids whose frequencies are close to being integer multiples of a common frequency. Typically, in applications, such signals are treated as perfectly harmonic, allowing for the estimation of their fundamental frequency, despite the signals not actually being periodic. Herein, we provide
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Optimal Local Differentially Private Quantization IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2020-10-23 Ruochi Zhang; Parv Venkitasubramaniam
Information sanitization to suppress sensitive information expressed through diverse sensor measurements is investigated in this work. Specifically, this work aims to integrate privacy preservation into the data compression phase of the sensing system streamline. The problem is posed as finding an optimal mapping from a collection of underlying raw distributions that reveal sensitive information for
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Lossless Dimension Reduction for Integer Least Squares With Application to Sphere Decoding IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2020-11-16 Mohammad Neinavaie; Mostafa Derakhtian; Sergiy A. Vorobyov
Minimum achievable complexity (MAC) for a maximum likelihood (ML) performance-achieving detection algorithm is derived. Using the derived MAC, we prove that the conventional sphere decoding (SD) algorithms suffer from an inherent weakness at low SNRs. To find a solution for the low SNR deficiency, we analyze the effect of zero-forcing (ZF) and minimum mean square error (MMSE) linearly detected symbols
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Joint Features Extraction for Multiple Moving Targets Using (Ultra-)Wideband FMCW Signals in the Presence of Doppler Ambiguity IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2020-11-23 Shengzhi Xu; Alexander Yarovoy
This article addresses the joint estimation of range, velocity and azimuth for multiple fast-moving targets using (ultra-)wideband (UWB) frequency-modulated continuous-wave (FMCW) radar with a phased array in the presence of Doppler ambiguities. The range migration of moving targets is described with the coupling of the fast-time and slow-time (chirp index), leading to the smearing of the target Doppler
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Cooperative Activity Detection: Sourced and Unsourced Massive Random Access Paradigms IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2020-11-23 Xiaodan Shao; Xiaoming Chen; Derrick Wing Kwan Ng; Caijun Zhong; Zhaoyang Zhang
This paper investigatesthe issue of cooperative activity detection for grant-free random access in the sixth-generation (6G) cell-free wireless networks with sourced and unsourced paradigms. First, we propose a cooperative framework for solving the problem of device activity detection in sourced random access. In particular, multiple access points (APs) cooperatively detect the device activity via
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Newton-Step-Based Hard Thresholding Algorithms for Sparse Signal Recovery IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2020-11-16 Nan Meng; Yun-Bin Zhao
Sparse signal recovery or compressed sensing can be formulated as certain sparse optimization problems. The classic optimization theory indicates that the Newton-like method often has a numerical advantage over the classic gradient method for nonlinear optimization problems. In this paper, we propose the so-called Newton-step-based iterative hard thresholding (NSIHT) and the Newton-step-based hard
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Target Localization by Unlabeled Range Measurements IEEE Trans. Signal Process. (IF 5.028) Pub Date : 2020-11-16 Guanyu Wang; Stefano Marano; Jiang Zhu; Zhiwei Xu
In this paper, the unlabeled target localization problem in a wireless sensor network is addressed. Sensors of the network are deployed in a two-dimensional surveyed area and measure their distance to a target. A central unit processes the data delivered by the sensors and is tasked to infer the position of a target, but it must do so without knowing the association between the received data and the
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