IEEE open journal of signal processing最新文献_第9页

Optimum Waveform Selection for Target State Estimation in the Joint Radar-Communication System 雷达-通信联合系统中目标状态估计的最佳波形选择

IEEE open journal of signal processing Pub Date : 2024-01-29 DOI: 10.1109/OJSP.2024.3359997

Ashoka Chakravarthi Mahipathi;Bethi Pardha Pardhasaradhi;Srinath Gunnery;Pathipati Srihari;John d'Souza;Paramananda Jena

{"title":"Optimum Waveform Selection for Target State Estimation in the Joint Radar-Communication System","authors":"Ashoka Chakravarthi Mahipathi;Bethi Pardha Pardhasaradhi;Srinath Gunnery;Pathipati Srihari;John d'Souza;Paramananda Jena","doi":"10.1109/OJSP.2024.3359997","DOIUrl":"https://doi.org/10.1109/OJSP.2024.3359997","url":null,"abstract":"The widespread usage of the Radio Frequency (RF) spectrum for wireless and mobile communication systems generated a significant spectrum scarcity. The Joint Radar-Communication System (JRCS) provides a framework to simultaneously utilize the allocated radar spectrum for sensing and communication purposes. Generally, a Successive Interference Cancellation (SIC) based receiver is applied to mitigate mutual interference in the JRCS configuration. However, this SIC receiver model introduces a communication residual component. In response to this issue, the article presents a novel measurement model based on communication residual components for various radar waveforms. The radar system's performance within the JRCS framework is then evaluated using the Fisher Information Matrix (FIM). The radar waveforms considered in this investigation are rectangular pulse, triangular pulse, Gaussian pulse, Linear Frequency Modulated (LFM) pulse, LFM-Gaussian pulse, and Non-Linear Frequency Modulated (NLFM) pulse. After that, the Kalman filter is deployed to estimate the target kinematics (range and range rate) of a single linearly moving target for different waveforms. Additionally, range and range rate estimation errors are quantified using the Root Mean Square Error (RMSE) metric. Furthermore, the Posterior Cramer-Rao Lower Bound (PCRLB) is derived to validate the estimation accuracy of various waveforms. The simulation results show that the range and range rate estimation errors are within the PCRLB limit at all time instants for all the designated waveforms. The results further reveal that the NLFM pulse waveform provides improved range and range rate error performance compared to all other waveforms.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"459-477"},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10416352","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139987097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust and Simple ADMM Penalty Parameter Selection 稳健而简单的 ADMM 惩罚参数选择

IEEE open journal of signal processing Pub Date : 2024-01-10 DOI: 10.1109/OJSP.2023.3349115

MICHAEL T. MCCANN;Brendt Wohlberg

{"title":"Robust and Simple ADMM Penalty Parameter Selection","authors":"MICHAEL T. MCCANN;Brendt Wohlberg","doi":"10.1109/OJSP.2023.3349115","DOIUrl":"https://doi.org/10.1109/OJSP.2023.3349115","url":null,"abstract":"We present a new method for online selection of the penalty parameter for the alternating direction method of multipliers (ADMM) algorithm. ADMM is a widely used method for solving a range of optimization problems, including those that arise in signal and image processing. In its standard form, ADMM includes a scalar hyperparameter, known as the penalty parameter, which usually has to be tuned to achieve satisfactory empirical convergence. In this work, we develop a framework for analyzing the ADMM algorithm applied to a quadratic problem as an affine fixed point iteration. Using this framework, we develop a new method for automatically tuning the penalty parameter by detecting when it has become too large or small. We analyze this and several other methods with respect to their theoretical properties, i.e., robustness to problem transformations, and empirical performance on several optimization problems. Our proposed algorithm is based on a theoretical framework with clear, explicit assumptions and approximations, is theoretically covariant/invariant to problem transformations, is simple to implement, and exhibits competitive empirical performance.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"402-420"},"PeriodicalIF":0.0,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10387940","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139749914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dagma-DCE: Interpretable, Non-Parametric Differentiable Causal Discovery Dagma-DCE：可解释的非参数差异因果发现

IEEE open journal of signal processing Pub Date : 2024-01-09 DOI: 10.1109/OJSP.2024.3351593

Daniel Waxman;Kurt Butler;Petar M. Djurić

引用次数: 0

Face Reflection Removal Network Using Multispectral Fusion of RGB and NIR Images 利用多光谱融合 RGB 和近红外图像的人脸反射去除网络

IEEE open journal of signal processing Pub Date : 2024-01-09 DOI: 10.1109/OJSP.2024.3351472

Hui Lan;Enquan Zhang;Cheolkon Jung

{"title":"Face Reflection Removal Network Using Multispectral Fusion of RGB and NIR Images","authors":"Hui Lan;Enquan Zhang;Cheolkon Jung","doi":"10.1109/OJSP.2024.3351472","DOIUrl":"https://doi.org/10.1109/OJSP.2024.3351472","url":null,"abstract":"Images captured through glass are usually contaminated by reflections, and the removal of them from images is a challenging task. Since the primary concern on photos is face, the face images with reflections annoy viewers severely. In this article, we propose a face reflection removal network using multispectral fusion of color (RGB) and near infrared (NIR) images, called FRRN. Due to the different spectral wavelengths of visible light [380 nm, 780 nm] and near infrared [780 nm, 2526 nm], NIR cameras are not sensitive to the visible light and thus NIR images are less corrupted by reflections. NIR images preserve structure information well and can guide the restoration process from reflections on the RGB images. Thus, we adopt multispectual fusion of RGB and NIR images for reflection removal from a face image. FRRN consists of one encoder model (contextual encoder model (CEM)) and two decoder models (NIR inference decoder model (NIDM) and image inference decoder model (IIDM)). CEM captures features from shallow to deep layers on the scene information while suppressing the sparse reflection component. NIDM infers NIR image to facilitate multi-scale guidance for reflection removal, while IIDM estimates the transmission layer with the guidance of NIDM. Besides, we present the reflection confidence generation module (RCGM) based on Laplacian convolution and channel attention-based residual block (CARB) to represent the reflection confidence in a region for reflection removal. To train FRRN, we construct a large-scale training dataset with face image and reflection layer (RGB and NIR images) and its corresponding test dataset using JAI AD-130 GE camera. Various experiments demonstrate that FRRN outperforms state-of-the-art methods for reflection removal in terms of visual quality and quantitative measurements.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"383-392"},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10384724","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139676115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adversarial Representation Learning for Robust Privacy Preservation in Audio 逆向表征学习实现稳健的音频隐私保护

IEEE open journal of signal processing Pub Date : 2024-01-01 DOI: 10.1109/OJSP.2023.3349113

Shayan Gharib;Minh Tran;Diep Luong;Konstantinos Drossos;Tuomas Virtanen

{"title":"Adversarial Representation Learning for Robust Privacy Preservation in Audio","authors":"Shayan Gharib;Minh Tran;Diep Luong;Konstantinos Drossos;Tuomas Virtanen","doi":"10.1109/OJSP.2023.3349113","DOIUrl":"https://doi.org/10.1109/OJSP.2023.3349113","url":null,"abstract":"Sound event detection systems are widely used in various applications such as surveillance and environmental monitoring where data is automatically collected, processed, and sent to a cloud for sound recognition. However, this process may inadvertently reveal sensitive information about users or their surroundings, hence raising privacy concerns. In this study, we propose a novel adversarial training method for learning representations of audio recordings that effectively prevents the detection of speech activity from the latent features of the recordings. The proposed method trains a model to generate invariant latent representations of speech-containing audio recordings that cannot be distinguished from non-speech recordings by a speech classifier. The novelty of our work is in the optimization algorithm, where the speech classifier's weights are regularly replaced with the weights of classifiers trained in a supervised manner. This increases the discrimination power of the speech classifier constantly during the adversarial training, motivating the model to generate latent representations in which speech is not distinguishable, even using new speech classifiers trained outside the adversarial training loop. The proposed method is evaluated against a baseline approach with no privacy measures and a prior adversarial training method, demonstrating a significant reduction in privacy violations compared to the baseline approach. Additionally, we show that the prior adversarial method is practically ineffective for this purpose.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"294-302"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10379095","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139488162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Signal Processing in the Retina: Interpretable Graph Classifier to Predict Ganglion Cell Responses 视网膜中的信号处理：预测神经节细胞反应的可解释图形分类器

IEEE open journal of signal processing Pub Date : 2024-01-01 DOI: 10.1109/OJSP.2023.3349111

Yasaman Parhizkar;Gene Cheung;Andrew W. Eckford

{"title":"Signal Processing in the Retina: Interpretable Graph Classifier to Predict Ganglion Cell Responses","authors":"Yasaman Parhizkar;Gene Cheung;Andrew W. Eckford","doi":"10.1109/OJSP.2023.3349111","DOIUrl":"10.1109/OJSP.2023.3349111","url":null,"abstract":"It is a popular hypothesis in neuroscience that ganglion cells in the retina are activated by selectively detecting visual features in an observed scene. While ganglion cell firings can be predicted via data-trained deep neural nets, the networks remain indecipherable, thus providing little understanding of the cells' underlying operations. To extract knowledge from the cell firings, in this paper we learn an interpretable graph-based classifier from data to predict the firings of ganglion cells in response to visual stimuli. Specifically, we learn a positive semi-definite (PSD) metric matrix \u0000<inline-formula><tex-math>${mathbf {M}}succeq 0$</tex-math></inline-formula>\u0000 that defines Mahalanobis distances between graph nodes (visual events) endowed with pre-computed feature vectors; the computed inter-node distances lead to edge weights and a combinatorial graph that is amenable to binary classification. Mathematically, we define the objective of metric matrix \u0000<inline-formula><tex-math>${mathbf {M}}$</tex-math></inline-formula>\u0000 optimization using a graph adaptation of large margin nearest neighbor (LMNN), which is rewritten as a semi-definite programming (SDP) problem. We solve it efficiently via a fast approximation called Gershgorin disc perfect alignment (GDPA) linearization. The learned metric matrix \u0000<inline-formula><tex-math>${mathbf {M}}$</tex-math></inline-formula>\u0000 provides interpretability: important features are identified along \u0000<inline-formula><tex-math>${mathbf {M}}$</tex-math></inline-formula>\u0000’s diagonal, and their mutual relationships are inferred from off-diagonal terms. Our fast metric learning framework can be applied to other biological systems with pre-chosen features that require interpretation.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"303-311"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10379097","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139388541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bilinear Expectation Propagation for Distributed Semi-Blind Joint Channel Estimation and Data Detection in Cell-Free Massive MIMO 用于无小区大规模多输入输出（MIMO）中分布式半盲联合信道估计和数据检测的双线性期望传播

IEEE open journal of signal processing Pub Date : 2024-01-01 DOI: 10.1109/OJSP.2023.3348343

Alexander Karataev;Christian Forsch;Laura Cottatellucci

引用次数: 0

Detection and Estimation of Gas Sources With Arbitrary Locations Based on Poisson's Equation 基于泊松方程的任意位置气体源检测与估算

IEEE open journal of signal processing Pub Date : 2023-12-21 DOI: 10.1109/OJSP.2023.3344076

Dmitriy Shutin;Thomas Wiedemann;Patrick Hinsen

引用次数: 0

Group Conversations in Noisy Environments (GiN) – Multimedia Recordings for Location-Aware Speech Enhancement 嘈杂环境中的群组对话（GiN）--用于位置感知语音增强的多媒体录音

IEEE open journal of signal processing Pub Date : 2023-12-19 DOI: 10.1109/OJSP.2023.3344379

Emilie d'Olne;Alastair H. Moore;Patrick A. Naylor;Jacob Donley;Vladimir Tourbabin;Thomas Lunner

{"title":"Group Conversations in Noisy Environments (GiN) – Multimedia Recordings for Location-Aware Speech Enhancement","authors":"Emilie d'Olne;Alastair H. Moore;Patrick A. Naylor;Jacob Donley;Vladimir Tourbabin;Thomas Lunner","doi":"10.1109/OJSP.2023.3344379","DOIUrl":"https://doi.org/10.1109/OJSP.2023.3344379","url":null,"abstract":"Recent years have seen a growing interest in the use of smart glasses mounted with microphones to solve the cocktail party problem using beamforming techniques or machine learning. Many such approaches could bring substantial advances in hearing aid or Augmented Reality (AR) research. To validate these methods, the EasyCom [Donley et al., 2021] dataset introduced high-quality multi-modal recordings of conversations in noise, including egocentric multi-channel microphone array audio, speech source pose, and headset microphone audio. While providing comprehensive data, EasyCom lacks diversity in the acoustic environments considered and the degree of overlapping speech in conversations. This work therefore presents the Group in Noise (GiN) dataset of over 2 hours of group conversations in noisy environments recorded using binaural microphones and a pair of glasses mounted with 5 microphones. The recordings took place in 3 rooms and contain 6 seated participants as well as a standing facilitator. The data also include close-talking microphone audio and head-pose data for each speaker, an audio channel from a fixed reference microphone, and automatically annotated speaker activity information. A baseline method is used to demonstrate the use of the data for speech enhancement. The dataset is publicly available in d'Olne et al. [2023].","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"374-382"},"PeriodicalIF":0.0,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10365406","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139573273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sparse Representation With Gaussian Atoms and Its Use in Anomaly Detection 高斯原子稀疏表示法及其在异常检测中的应用

IEEE open journal of signal processing Pub Date : 2023-12-19 DOI: 10.1109/OJSP.2023.3344313

Denis C. Ilie-Ablachim;Andra Băltoiu;Bogdan Dumitrescu

引用次数: 0