Pub Date : 2025-01-29DOI: 10.1109/tsp.2025.3536101
Benedikt Böck, Dominik Semmler, Benedikt Fesl, Michael Baur, Wolfgang Utschick
{"title":"Gohberg-Semencul Toeplitz Covariance Estimation via Autoregressive Parameters","authors":"Benedikt Böck, Dominik Semmler, Benedikt Fesl, Michael Baur, Wolfgang Utschick","doi":"10.1109/tsp.2025.3536101","DOIUrl":"https://doi.org/10.1109/tsp.2025.3536101","url":null,"abstract":"","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"40 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143056375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-29DOI: 10.1109/tsp.2025.3535556
Ángel F. García-Fernández, Simo Särkkä
{"title":"Gaussian multi-target filtering with target dynamics driven by a stochastic differential equation","authors":"Ángel F. García-Fernández, Simo Särkkä","doi":"10.1109/tsp.2025.3535556","DOIUrl":"https://doi.org/10.1109/tsp.2025.3535556","url":null,"abstract":"","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"15 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143056376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-27DOI: 10.1109/tsp.2025.3531373
Xinhui Rong, Victor Solo
{"title":"Asymptotic Error Rates for Point Process Classification","authors":"Xinhui Rong, Victor Solo","doi":"10.1109/tsp.2025.3531373","DOIUrl":"https://doi.org/10.1109/tsp.2025.3531373","url":null,"abstract":"","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"25 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143050362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-27DOI: 10.1109/TSP.2025.3533275
Chang Gao;Qingfu Zhang;Pramod K. Varshney;Xi Lin;Hongwei Liu
Distributed detection, which fuses the preprocessed observations of the same area from local sensors, can generally improve target detection performance. For scenarios in practical applications where sensors cannot obtain the target signal-to-noise ratio (SNR) parameters in advance, non-coherent integration is mostly used for distributed detection. However, this detector is equivalent to the optimal detector only under the condition that the target SNRs of all the sensors are exactly the same. This condition is quite stringent for the observation of non-cooperative targets. This paper first compares the performance of traditional optimal detectors, the non-coherent integration (NCI) detector, and the single-sensor detector from a unified perspective based on the concept of Pareto optimality. Then, from the perspective of multi-objective optimization, the fusion rule and corresponding parameter learning method are designed. Theoretical analysis shows that the proposed non-identical SNR detection fusion rule possesses weak Pareto optimality. Simulation experiments demonstrate that the proposed method effectively achieves a trade-off between the optimal detection performance across sensors with multiple SNRs. Compared to the optimal detector in the presence of mismatch between the assumed and actual SNR of the target, the proposed method can achieve a significant improvement in detection performance. Additionally, the proposed method outperforms the NCI detector in scenarios where the SNR distributions of target observations across different sensors exhibit greater diversity.
{"title":"A Distributed Multi-Objective Detection Method for Multi-Sensor Systems With Unknown Local SNR","authors":"Chang Gao;Qingfu Zhang;Pramod K. Varshney;Xi Lin;Hongwei Liu","doi":"10.1109/TSP.2025.3533275","DOIUrl":"10.1109/TSP.2025.3533275","url":null,"abstract":"Distributed detection, which fuses the preprocessed observations of the same area from local sensors, can generally improve target detection performance. For scenarios in practical applications where sensors cannot obtain the target signal-to-noise ratio (SNR) parameters in advance, non-coherent integration is mostly used for distributed detection. However, this detector is equivalent to the optimal detector only under the condition that the target SNRs of all the sensors are exactly the same. This condition is quite stringent for the observation of non-cooperative targets. This paper first compares the performance of traditional optimal detectors, the non-coherent integration (NCI) detector, and the single-sensor detector from a unified perspective based on the concept of Pareto optimality. Then, from the perspective of multi-objective optimization, the fusion rule and corresponding parameter learning method are designed. Theoretical analysis shows that the proposed non-identical SNR detection fusion rule possesses weak Pareto optimality. Simulation experiments demonstrate that the proposed method effectively achieves a trade-off between the optimal detection performance across sensors with multiple SNRs. Compared to the optimal detector in the presence of mismatch between the assumed and actual SNR of the target, the proposed method can achieve a significant improvement in detection performance. Additionally, the proposed method outperforms the NCI detector in scenarios where the SNR distributions of target observations across different sensors exhibit greater diversity.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"649-663"},"PeriodicalIF":4.6,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143050363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-27DOI: 10.1109/tsp.2025.3535099
Xiaochuan Ke, K. C. Ho
{"title":"An Investigation of Using Rigid Body Receivers for Locating a Non-Cooperative Object By Pseudo-Ranges in the Absence of Synchronization","authors":"Xiaochuan Ke, K. C. Ho","doi":"10.1109/tsp.2025.3535099","DOIUrl":"https://doi.org/10.1109/tsp.2025.3535099","url":null,"abstract":"","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"99B 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143050365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-27DOI: 10.1109/tsp.2025.3534685
Zhan Gao, Deniz Gündüz
{"title":"Graph Neural Networks over the Air for Decentralized Tasks in Wireless Networks","authors":"Zhan Gao, Deniz Gündüz","doi":"10.1109/tsp.2025.3534685","DOIUrl":"https://doi.org/10.1109/tsp.2025.3534685","url":null,"abstract":"","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"29 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143050364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-24DOI: 10.1109/tsp.2025.3532953
An Liu, Yufan Zhou, Wenkang Xu
{"title":"Subspace Constrained Variational Bayesian Inference for Structured Compressive Sensing with a Dynamic Grid","authors":"An Liu, Yufan Zhou, Wenkang Xu","doi":"10.1109/tsp.2025.3532953","DOIUrl":"https://doi.org/10.1109/tsp.2025.3532953","url":null,"abstract":"","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"49 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143030953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-24DOI: 10.1109/tsp.2025.3533208
Tongle Wu, Zhize Li, Ying Sun
{"title":"The Effectiveness of Local Updates for Decentralized Learning under Data Heterogeneity","authors":"Tongle Wu, Zhize Li, Ying Sun","doi":"10.1109/tsp.2025.3533208","DOIUrl":"https://doi.org/10.1109/tsp.2025.3533208","url":null,"abstract":"","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"58 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143030952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-21DOI: 10.1109/TSP.2025.3531779
Minhua Ding;Italo Atzeni;Antti Tölli;A. Lee Swindlehurst
This paper focuses on the minimum mean squared error (MMSE) channel estimator for multiple-input multiple-output (MIMO) systems with one-bit quantization at the receiver side. Despite its optimality and significance in estimation theory, the MMSE estimator has not been fully investigated in this context due to its general nonlinearity and computational complexity. Instead, the typically suboptimal Bussgang linear MMSE (BLMMSE) channel estimator has been widely adopted. In this work, we develop a new framework to compute the MMSE channel estimator that hinges on the computation of the orthant probability of a multivariate normal distribution. Based on this framework, we determine a necessary and sufficient condition for the BLMMSE channel estimator to be optimal and thus equivalent to the MMSE estimator. Under the assumption of specific channel correlation or pilot symbols, we further utilize the framework to derive analytical expressions for the MMSE estimator that are particularly convenient for the computation when certain system dimensions become large, thereby enabling a comparison between the BLMMSE and MMSE channel estimators in these cases.
{"title":"On Optimal MMSE Channel Estimation for One-Bit Quantized MIMO Systems","authors":"Minhua Ding;Italo Atzeni;Antti Tölli;A. Lee Swindlehurst","doi":"10.1109/TSP.2025.3531779","DOIUrl":"10.1109/TSP.2025.3531779","url":null,"abstract":"This paper focuses on the minimum mean squared error (MMSE) channel estimator for multiple-input multiple-output (MIMO) systems with one-bit quantization at the receiver side. Despite its optimality and significance in estimation theory, the MMSE estimator has not been fully investigated in this context due to its general nonlinearity and computational complexity. Instead, the typically suboptimal Bussgang linear MMSE (BLMMSE) channel estimator has been widely adopted. In this work, we develop a new framework to compute the MMSE channel estimator that hinges on the computation of the orthant probability of a multivariate normal distribution. Based on this framework, we determine a necessary and sufficient condition for the BLMMSE channel estimator to be optimal and thus equivalent to the MMSE estimator. Under the assumption of specific channel correlation or pilot symbols, we further utilize the framework to derive analytical expressions for the MMSE estimator that are particularly convenient for the computation when certain system dimensions become large, thereby enabling a comparison between the BLMMSE and MMSE channel estimators in these cases.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"617-632"},"PeriodicalIF":4.6,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10848316","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142992794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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