IEEE Transactions on Signal Processing最新文献_第5页

Domain Adaptation for DoA Estimation in Multipath Channels with Interferences 干扰下多径信道DoA估计的域自适应

IF 5.4 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-06-06 DOI: 10.1109/tsp.2025.3577257

Amitay Bar, Joseph S. Picard, Israel Cohen, Ronen Talmon

引用次数: 0

Detection of a Living Person With Unknown Reflection and Respiration Using MIMO Radar 用MIMO雷达探测未知反射和呼吸的活人

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-06-06 DOI: 10.1109/TSP.2025.3577198

Peichao Wang;Aiming Bian;Bingqian Yu;Qian He

{"title":"Detection of a Living Person With Unknown Reflection and Respiration Using MIMO Radar","authors":"Peichao Wang;Aiming Bian;Bingqian Yu;Qian He","doi":"10.1109/TSP.2025.3577198","DOIUrl":"10.1109/TSP.2025.3577198","url":null,"abstract":"Consider the presence of a human body, which could possibly be fake/dead, in the area under monitoring. The purpose of this paper is to determine whether the human body is a living person or not, using multiple-input multiple-output (MIMO) radar. Taking into account the oscillatory characteristics of human respiration, the MIMO radar received signal model for a living person is developed, assuming that the chest displacement, respiration frequency, and reflection coefficients are deterministic but unknown. The living person detection problem can be formulated as a binary composite hypothesis testing, for which the generalized likelihood ratio test (GLRT)-based detector is derived. Further consider that the human respiration could be very weak, making the two hypotheses too close to be well distinguished. Tailored for deciding between two close hypotheses with unknown parameters, the generalized locally most powerful test (GLMPT) for MIMO radar living person detection is proposed. Theoretical performance analyses are provided for both the GLRT-based and GLMPT-based MIMO radar living person detectors. The closed-form expressions for the detection and false alarm probabilities of the GLMPT-based detector are derived for a given respiration frequency. Numerical examples are presented to evaluate the performance of the GLRT and GLMPT-based detectors in detecting living persons with weak respiration. The impacts of system parameters on detector performance are investigated.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"2603-2615"},"PeriodicalIF":4.6,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144237273","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}

引用次数: 0

Sampling and Uniqueness Sets in Graphon Signal Processing 石墨信号处理中的采样和唯一性集

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-06-06 DOI: 10.1109/TSP.2025.3577112

Alejandro Parada-Mayorga;Alejandro Ribeiro

{"title":"Sampling and Uniqueness Sets in Graphon Signal Processing","authors":"Alejandro Parada-Mayorga;Alejandro Ribeiro","doi":"10.1109/TSP.2025.3577112","DOIUrl":"10.1109/TSP.2025.3577112","url":null,"abstract":"In this work, we study the properties of sampling sets on families of large graphs by leveraging the theory of graphons and graph limits. We extend to graphon signals the notion of removable and uniqueness sets, which was developed originally for the analysis of signals on graphs. We state the formal definition of a <inline-formula><tex-math>$Lambda-$</tex-math></inline-formula>removable set and conditions under which a bandlimited graphon signal can be represented uniquely when its samples are obtained from the complement of a <inline-formula><tex-math>$Lambda-$</tex-math></inline-formula>removable set in the graphon. By leveraging such results we show that graphon representations of graph signals can be used as a common framework to compare sampling sets between graphs with different numbers of nodes and node labelings. Additionally, given a sequence of graphs that converges to a graphon, we show that the sequences of sampling sets whose graphon representation is identical in <inline-formula><tex-math>$[0,1]$</tex-math></inline-formula> are convergent as well. We exploit the convergence results to provide an algorithm that obtains approximately close to optimal sampling sets in large graphs where traditional methods are intractable. Performing a set of numerical experiments, we evaluate the quality of these sampling sets. Our results open the door for the efficient computation of optimal sampling sets in large graphs relying on existing methods that can be applied in small graphs.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"2480-2495"},"PeriodicalIF":4.6,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144237261","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}

引用次数: 0

QuantFactor REINFORCE: Mining Steady Formulaic Alpha Factors With Variance-Bounded REINFORCE 定量因子强化：用方差有界强化挖掘稳定公式α因子

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-06-04 DOI: 10.1109/TSP.2025.3576781

Junjie Zhao;Chengxi Zhang;Min Qin;Peng Yang

{"title":"QuantFactor REINFORCE: Mining Steady Formulaic Alpha Factors With Variance-Bounded REINFORCE","authors":"Junjie Zhao;Chengxi Zhang;Min Qin;Peng Yang","doi":"10.1109/TSP.2025.3576781","DOIUrl":"10.1109/TSP.2025.3576781","url":null,"abstract":"Alpha factor mining aims to discover investment signals from the historical financial market data, which can be used to predict asset returns and gain excess profits. Powerful deep learning methods for alpha factor mining lack interpretability, making them unacceptable in the risk-sensitive real markets. Formulaic alpha factors are preferred for their interpretability, while the search space is complex and powerful explorative methods are urged. Recently, a promising framework is proposed for generating formulaic alpha factors using deep reinforcement learning, and quickly gained research focuses from both academia and industries. This paper first argues that the originally employed policy training method, i.e., Proximal Policy Optimization (PPO), faces several important issues in the context of alpha factors mining. Herein, a novel reinforcement learning algorithm based on the well-known REINFORCE algorithm is proposed. REINFORCE employs Monte Carlo sampling to estimate the policy gradient—yielding unbiased but high variance estimates. The minimal environmental variability inherent in the underlying state transition function, which adheres to the Dirac distribution, can help alleviate this high variance issue, making REINFORCE algorithm more appropriate than PPO. A new dedicated baseline is designed to theoretically reduce the commonly suffered high variance of REINFORCE. Moreover, the information ratio is introduced as a reward shaping mechanism to encourage the generation of steady alpha factors that can better adapt to changes in market volatility. Evaluations on real assets data indicate the proposed algorithm boosts correlation with returns by 3.83%, and a stronger ability to obtain excess returns compared to the latest alpha factors mining methods, which meets the theoretical results well.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"2448-2463"},"PeriodicalIF":4.6,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144218670","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}

引用次数: 0

Fourth-Order Sparse Array Design From a Sum-Difference Co-Array Perspective 和差分共阵视角下的四阶稀疏阵列设计

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-06-04 DOI: 10.1109/TSP.2025.3575079

Hua Chen;Haodong Guo;Wei Liu;Qing Shen;Gang Wang;Hing Cheung So

引用次数: 0

Robust Parameter Estimation of Non-linear State Space Models Using a Divergence-Based Estimator 基于散度估计的非线性状态空间模型鲁棒参数估计

IF 5.4 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-06-04 DOI: 10.1109/tsp.2025.3576241

Haruya Ishizuka, Hironori Fujisawa

引用次数: 0

Unlabeled Compressed Sensing from Multiple Measurement Vectors 多测量向量的无标记压缩感知

IF 5.4 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-06-03 DOI: 10.1109/tsp.2025.3575919

Mohamed Akrout, Amine Mezghani, Faouzi Bellili

引用次数: 0

Parametric Joint Target Detection and Localization Using Message Passing for Distributed Passive MIMO Radar 基于消息传递的分布式无源MIMO雷达参数联合目标检测与定位

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-06-02 DOI: 10.1109/TSP.2025.3572903

Bin Li;Jun Li;Qinghua Guo;Zehua Yu;Yuntao Wu

{"title":"Parametric Joint Target Detection and Localization Using Message Passing for Distributed Passive MIMO Radar","authors":"Bin Li;Jun Li;Qinghua Guo;Zehua Yu;Yuntao Wu","doi":"10.1109/TSP.2025.3572903","DOIUrl":"10.1109/TSP.2025.3572903","url":null,"abstract":"This paper addresses the challenge of joint target detection and localization with a distributed dual-channel passive multiple-input multiple-output (MIMO) radar, which comprises multiple non-cooperative illuminators of opportunity and receivers. In conventional approaches, the target position is typically obtained by conducting exhaustive grid search for the peak of the test statistics. To reduce the errors due to grid mismatch, the number of grid cells needs to be large, leading to high computational complexity and hampering real-time processing capabilities. Moreover, these approaches are ineffective under strong direct-path interference. In this paper, an efficient factor graph approach for joint target detection and localization is proposed, which detects and localizes the target through parameter estimation, significantly reducing the computational complexity while maintaining high robustness. In particular, we introduce a binary target existence variable to represent the presence or absence of a target and reformulate the problem as the computation of the marginal posterior probabilities of unknown parameters. Then an efficient message passing algorithm is developed to solve the reformulated problem. Extensive simulation results demonstrate that the proposed approach outperforms state-of-the-art approaches in various scenarios, while with much lower computational complexity.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"2200-2215"},"PeriodicalIF":4.6,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144201435","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}

引用次数: 0

Robust Multidimensional Graph Neural Networks for Signal Processing in Wireless Communications with Edge-Graph Information Bottleneck 面向边缘图信息瓶颈无线通信信号处理的鲁棒多维图神经网络

IF 5.4 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-05-27 DOI: 10.1109/tsp.2025.3574005

Ziheng Liu, Jiayi Zhang, Yiyang Zhu, Enyu Shi, Bo Ai

引用次数: 0

Symplectic Wigner Distribution in the Linear Canonical Transform Domain: Theory and Application 线性正则变换域的辛Wigner分布：理论与应用

IF 5.4 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-05-26 DOI: 10.1109/tsp.2025.3572739

Yangfan He, Zhichao Zhang

引用次数: 0