Pub Date : 2025-12-08DOI: 10.1109/LSP.2025.3634660
{"title":"List of Reviewers","authors":"","doi":"10.1109/LSP.2025.3634660","DOIUrl":"https://doi.org/10.1109/LSP.2025.3634660","url":null,"abstract":"","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"4473-4484"},"PeriodicalIF":3.9,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11284689","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145729272","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}
Pub Date : 2025-12-08DOI: 10.1109/LSP.2025.3641506
Yichen Shi;Wenming Yang;Nan Su;Guijin Wang
3D object detection plays an important role in intelligent systems perceiving the world. Although manystudies have been conducted to address this task, the detection accuracy is still limited by the network’s learning capability. Therefore, we propose LVMF3D, a Large Vision Model (LVM) boosted multimodal fusion indoor 3D object detection framework, consisting of two branches. The pre-trained LVM is used as the RGB branch to better extract the image texture feature. The point branch is used to encode the spatial geometric feature. Furthermore, Point Fusion Module (PFM) and Multi-Scale Attention Fusion Module (MS-AFM) are specially designed in the 2D and 3D spaces, respectively, to realize more comprehensive and effective information fusion between the two branches. We conduct experiments on the indoor 3D object detection dataset SUN RGB-D and achieve state-of-the-art results compared to other 3D object detection methods.
{"title":"LVMF3D: Large Vision Model Boosting Multimodal Fusion for Indoor 3D Object Detection","authors":"Yichen Shi;Wenming Yang;Nan Su;Guijin Wang","doi":"10.1109/LSP.2025.3641506","DOIUrl":"https://doi.org/10.1109/LSP.2025.3641506","url":null,"abstract":"3D object detection plays an important role in intelligent systems perceiving the world. Although manystudies have been conducted to address this task, the detection accuracy is still limited by the network’s learning capability. Therefore, we propose LVMF3D, a Large Vision Model (LVM) boosted multimodal fusion indoor 3D object detection framework, consisting of two branches. The pre-trained LVM is used as the RGB branch to better extract the image texture feature. The point branch is used to encode the spatial geometric feature. Furthermore, Point Fusion Module (PFM) and Multi-Scale Attention Fusion Module (MS-AFM) are specially designed in the 2D and 3D spaces, respectively, to realize more comprehensive and effective information fusion between the two branches. We conduct experiments on the indoor 3D object detection dataset SUN RGB-D and achieve state-of-the-art results compared to other 3D object detection methods.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"33 ","pages":"356-360"},"PeriodicalIF":3.9,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886605","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 : 2025-12-05DOI: 10.1109/LSP.2025.3640094
Junyuan Guo;Mingqian Han
Superdirective beamforming methods based on spherical harmonic expansion can achieve higher array gain compared to conventional beamforming methods when the array aperture is very small. However, in the waveguide environment, the array gain of beamforming methods based on spherical harmonic expansion may degrade significantly due to the influence of multipath effects. To address the issue, this letter proposes an improved beamforming method for compact planar acoustic vector sensor arrays to mitigate the negative impact of multipath effects on array gain. First, the form of the steering vector model in the direct arrival zone of the waveguide environment is reasonably simplified. Second, a closed-form beamformer is constructed by utilizing the information of signals’ arriving directions. Subsequently, the theoretical derivation demonstrates the advantages of the proposed beamforming method in the waveguide environment. Finally, simulation analysis substantiates the rationality and feasibility of the proposed method.
{"title":"Superdirective Beamforming Method Based on Spherical Harmonic Expansion in the Waveguide Environment","authors":"Junyuan Guo;Mingqian Han","doi":"10.1109/LSP.2025.3640094","DOIUrl":"https://doi.org/10.1109/LSP.2025.3640094","url":null,"abstract":"Superdirective beamforming methods based on spherical harmonic expansion can achieve higher array gain compared to conventional beamforming methods when the array aperture is very small. However, in the waveguide environment, the array gain of beamforming methods based on spherical harmonic expansion may degrade significantly due to the influence of multipath effects. To address the issue, this letter proposes an improved beamforming method for compact planar acoustic vector sensor arrays to mitigate the negative impact of multipath effects on array gain. First, the form of the steering vector model in the direct arrival zone of the waveguide environment is reasonably simplified. Second, a closed-form beamformer is constructed by utilizing the information of signals’ arriving directions. Subsequently, the theoretical derivation demonstrates the advantages of the proposed beamforming method in the waveguide environment. Finally, simulation analysis substantiates the rationality and feasibility of the proposed method.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"33 ","pages":"416-420"},"PeriodicalIF":3.9,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929601","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 : 2025-12-04DOI: 10.1109/LSP.2025.3640513
Alperen Berber;Berkan Dulek
Kullback–Leibler (KL) divergence plays a central role in hypothesis testing. It gives a measure of the statistical distance between two probability distributions. In the distributed detection problem, it is used as a design criterion in the absence of the information regarding the fusion center's (FC) decision rule: The local sensor decision rules are designed to maximize the KL divergence between the distributions of quantized messages sent to the FC under alternative and null hypotheses. In decision making tasks involving humans, subjective perception of probability values due to behavioral biases needs to be taken into account. In this letter, the notion of behavioral KL divergence is proposed. The statistical distance between two distributions is computed based on the perceived values of the probabilities, which are obtained from the actual probabilities using the probability weighting function employed in prospect theory. It is proved that the behavioral KL divergence between the distributions of the quantized decision at the output of a detector under both hypotheses is maximized by either the Neyman-Pearson (NP) rule or flipped Neyman-Pearson (FNP) rule for any fixed false alarm probability. Based on this result, it is also established that under a constraint on the average perceived false alarm probability, the average behavioral KL divergence is maximized by time-sharing between at most two single-threshold likelihood-ratio tests, each of which is either an NP or an FNP rule. The theoretical results are supported by numerical examples.
{"title":"Optimal Binary Hypothesis Testing Based on the Behavioral Kullback–Leibler Divergence Criterion","authors":"Alperen Berber;Berkan Dulek","doi":"10.1109/LSP.2025.3640513","DOIUrl":"https://doi.org/10.1109/LSP.2025.3640513","url":null,"abstract":"Kullback–Leibler (KL) divergence plays a central role in hypothesis testing. It gives a measure of the statistical distance between two probability distributions. In the distributed detection problem, it is used as a design criterion in the absence of the information regarding the fusion center's (FC) decision rule: The local sensor decision rules are designed to maximize the KL divergence between the distributions of quantized messages sent to the FC under alternative and null hypotheses. In decision making tasks involving humans, subjective perception of probability values due to behavioral biases needs to be taken into account. In this letter, the notion of behavioral KL divergence is proposed. The statistical distance between two distributions is computed based on the perceived values of the probabilities, which are obtained from the actual probabilities using the probability weighting function employed in prospect theory. It is proved that the behavioral KL divergence between the distributions of the quantized decision at the output of a detector under both hypotheses is maximized by either the Neyman-Pearson (NP) rule or flipped Neyman-Pearson (FNP) rule for any fixed false alarm probability. Based on this result, it is also established that under a constraint on the average perceived false alarm probability, the average behavioral KL divergence is maximized by time-sharing between at most two single-threshold likelihood-ratio tests, each of which is either an NP or an FNP rule. The theoretical results are supported by numerical examples.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"33 ","pages":"161-165"},"PeriodicalIF":3.9,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145772020","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 : 2025-12-04DOI: 10.1109/LSP.2025.3640530
Hezhe Jia;Hua Wang;Jun Liu;Kai Zhong;Jinfeng Hu
Unimodular waveform design plays a crucial role in MIMO radar systems. Previous studies have mainly focused on continuous- and discrete-phase coding for single-pulse MIMO radar waveforms, as well as continuous-phase coding for pulse-Doppler MIMO radar waveforms. Although multi-pulse discrete-phase waveforms provide both high resolution and hardware simplicity, their design remains a challenging optimization problem. In this work, we go beyond prior approaches by investigating the design of pulse-Doppler MIMO waveforms under discrete phase constraints. We formulate the problem as optimizing the waveform phase matrix to minimize the weighted integrated sidelobe level (WISL) of the joint ambiguity function. The non-convexity of WISL and the discrete phase constraints make the problem particularly challenging. Noting that the Adam optimizer incorporates both adaptive learning rate and momentum mechanisms, making it suitable for solving non-convex optimization problems, and that nonlinear functions can be used to approximate quantization in a continuously differentiable form, we propose a soft quantization Adam optimization (SQAO) method to solve this problem. Simulations show that SQAO outperforms existing method.
{"title":"Discrete-Phase Waveform Design for Desired Ambiguity Functions in Pulse-Doppler MIMO Radar","authors":"Hezhe Jia;Hua Wang;Jun Liu;Kai Zhong;Jinfeng Hu","doi":"10.1109/LSP.2025.3640530","DOIUrl":"https://doi.org/10.1109/LSP.2025.3640530","url":null,"abstract":"Unimodular waveform design plays a crucial role in MIMO radar systems. Previous studies have mainly focused on continuous- and discrete-phase coding for single-pulse MIMO radar waveforms, as well as continuous-phase coding for pulse-Doppler MIMO radar waveforms. Although multi-pulse discrete-phase waveforms provide both high resolution and hardware simplicity, their design remains a challenging optimization problem. In this work, we go beyond prior approaches by investigating the design of pulse-Doppler MIMO waveforms under discrete phase constraints. We formulate the problem as optimizing the waveform phase matrix to minimize the weighted integrated sidelobe level (WISL) of the joint ambiguity function. The non-convexity of WISL and the discrete phase constraints make the problem particularly challenging. Noting that the Adam optimizer incorporates both adaptive learning rate and momentum mechanisms, making it suitable for solving non-convex optimization problems, and that nonlinear functions can be used to approximate quantization in a continuously differentiable form, we propose a soft quantization Adam optimization (SQAO) method to solve this problem. Simulations show that SQAO outperforms existing method.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"33 ","pages":"421-425"},"PeriodicalIF":3.9,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929335","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 : 2025-12-04DOI: 10.1109/LSP.2025.3640068
Miroslav Kárný
Geometric and linear poolings often serve for the fusion of the knowledge contained in a finite set of probability densities. Their pros and cons are relatively well understood. Many other ways have also been studied. A recent insightful survey letter by Koliander et al. inspects a range of pooling ways based on various axioms, optimisation and supra-Bayesian handling. The gained extensive option set makes the proper choice of the pooling function harder. This letter reduces the extent of unjustified options. It provides the optimisation-based selection among available options. Its steps are justified by well-established, axiomatically supported, minimum relative entropy and approximation principles. The text applies Occam’s razor to its theoretical tools, too. It simplifies the user’s choice of the pooling function and its weights. This weakens the possibility of a bad choice and opens the way to a range of applications.
{"title":"Occam’s Razor in Pooling of Probability Densities","authors":"Miroslav Kárný","doi":"10.1109/LSP.2025.3640068","DOIUrl":"https://doi.org/10.1109/LSP.2025.3640068","url":null,"abstract":"Geometric and linear poolings often serve for the fusion of the knowledge contained in a finite set of probability densities. Their pros and cons are relatively well understood. Many other ways have also been studied. A recent insightful survey letter by Koliander et al. inspects a range of pooling ways based on various axioms, optimisation and supra-Bayesian handling. The gained extensive option set makes the proper choice of the pooling function harder. This letter reduces the extent of unjustified options. It provides the optimisation-based selection among available options. Its steps are justified by well-established, axiomatically supported, minimum relative entropy and approximation principles. The text applies Occam’s razor to its theoretical tools, too. It simplifies the user’s choice of the pooling function and its weights. This weakens the possibility of a bad choice and opens the way to a range of applications.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"33 ","pages":"156-160"},"PeriodicalIF":3.9,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145772051","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 : 2025-12-04DOI: 10.1109/LSP.2025.3640519
Ziyang Jiang;Xueyan Chen;Shuai Wang;Xinyuan Qian;Haizhou Li
In complex multi-speaker scenarios with significant speaker overlap and background noise, extracting the target speaker's speech remains a major challenge. This capability is crucial for dialogue-based applications such as AI speech assistants, where downstream tasks such as speech recognition depend on clean speech. A potential solution to address these challenges is Target Speaker Extraction (TSE), which leverages auxiliary information to extract target speech from mixed and noisy speech, thus overcoming the limitations of Speech Separation (SS) and Speech Enhancement (SE). In particular, we propose a multi-modal TSE network, namely Text Prompt Extractor with echo cue block (TPEech), which uses historical dialogue text as cues for extraction and incorporates the echo cue block (ECB) to further exploit this cue and enhance TSE performance. The experiments show the excellent extraction and denoising capabilities of our proposed network. TPEech achieves an SI-SDRi of 9.632 dB, an SDR of 13.045 dB, a PESQ of 2.814, and a STOI of 0.885, outperforming competitive baselines. Additionally, we experimentally verify that TPEech is robust against semantically incomplete textual prompts.
{"title":"TPEech: Target Speaker Extraction and Noise Suppression With Historical Dialogue Text Cues","authors":"Ziyang Jiang;Xueyan Chen;Shuai Wang;Xinyuan Qian;Haizhou Li","doi":"10.1109/LSP.2025.3640519","DOIUrl":"https://doi.org/10.1109/LSP.2025.3640519","url":null,"abstract":"In complex multi-speaker scenarios with significant speaker overlap and background noise, extracting the target speaker's speech remains a major challenge. This capability is crucial for dialogue-based applications such as AI speech assistants, where downstream tasks such as speech recognition depend on clean speech. A potential solution to address these challenges is Target Speaker Extraction (TSE), which leverages auxiliary information to extract target speech from mixed and noisy speech, thus overcoming the limitations of Speech Separation (SS) and Speech Enhancement (SE). In particular, we propose a multi-modal TSE network, namely Text Prompt Extractor with echo cue block (TPEech), which uses historical dialogue text as cues for extraction and incorporates the echo cue block (ECB) to further exploit this cue and enhance TSE performance. The experiments show the excellent extraction and denoising capabilities of our proposed network. TPEech achieves an SI-SDRi of 9.632 dB, an SDR of 13.045 dB, a PESQ of 2.814, and a STOI of 0.885, outperforming competitive baselines. Additionally, we experimentally verify that TPEech is robust against semantically incomplete textual prompts.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"33 ","pages":"351-355"},"PeriodicalIF":3.9,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886603","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}
Monocular depth estimation is essential for 3D perception in applications such as autonomous driving and robotics. Self-supervised methods avoid depth labels but often rely on shallow pose networks with weak temporal modeling, leading to unstable predictions. We propose EMSP-Net, an Enhanced Multi-Scale PoseNet for self-supervised monocular depth estimation. It introduces a hierarchical feature fusion encoder, a temporal attention-context decoder, and a pose consistency loss to jointly improve feature extraction, temporal stability, and geometric constraints. On the KITTI dataset, EMSP-Net achieved an absolute relative error of 0.105 and a squared relative error of 0.708. In the Make3D cross-domain test, its strong robustness was further demonstrated.
{"title":"Enhanced Multi-Scale PoseNet for Self-Supervised Monocular Depth Estimation","authors":"Chao Zhang;Tian Tian;Cheng Han;Tiancheng Shao;Mi Zhou;Shichao Zhao","doi":"10.1109/LSP.2025.3639361","DOIUrl":"https://doi.org/10.1109/LSP.2025.3639361","url":null,"abstract":"Monocular depth estimation is essential for 3D perception in applications such as autonomous driving and robotics. Self-supervised methods avoid depth labels but often rely on shallow pose networks with weak temporal modeling, leading to unstable predictions. We propose EMSP-Net, an Enhanced Multi-Scale PoseNet for self-supervised monocular depth estimation. It introduces a hierarchical feature fusion encoder, a temporal attention-context decoder, and a pose consistency loss to jointly improve feature extraction, temporal stability, and geometric constraints. On the KITTI dataset, EMSP-Net achieved an absolute relative error of 0.105 and a squared relative error of 0.708. In the Make3D cross-domain test, its strong robustness was further demonstrated.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"33 ","pages":"316-320"},"PeriodicalIF":3.9,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886629","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 : 2025-12-02DOI: 10.1109/LSP.2025.3639352
Zhengyi Liu;Jiali Wu;Xianyong Fang;Linbo Wang
Text-driven medical image segmentation aims to accurately segment pathological regions in medical images based on textual descriptions. Existing methods face two major challenges: (a) The significant modality heterogeneity between textual and visual features leads to inefficient cross-modal feature alignment; (b) The insufficient utilization of medical shared knowledge restricts semantic understanding. To address these challenges, two large language model (LLM) bridges are constructed. LLM semantic bridge leverages the sequential modeling capability of a frozen LLM to reorganize visual features into semantically coherent units that possess linguistic logic, thereby effectively bridging vision and language. The LLM prompt bridge appends learnable prompts, which encode medical shared knowledge from the LLM, to text embeddings, thereby effectively bridging case-specificity and medical consensus knowledge. Experimental results show the predominant performance due to LLM participation.
{"title":"Text-Driven Medical Image Segmentation With LLM Semantic Bridge and LLM Prompt Bridge","authors":"Zhengyi Liu;Jiali Wu;Xianyong Fang;Linbo Wang","doi":"10.1109/LSP.2025.3639352","DOIUrl":"https://doi.org/10.1109/LSP.2025.3639352","url":null,"abstract":"Text-driven medical image segmentation aims to accurately segment pathological regions in medical images based on textual descriptions. Existing methods face two major challenges: (a) The significant modality heterogeneity between textual and visual features leads to inefficient cross-modal feature alignment; (b) The insufficient utilization of medical shared knowledge restricts semantic understanding. To address these challenges, two large language model (LLM) bridges are constructed. LLM semantic bridge leverages the sequential modeling capability of a frozen LLM to reorganize visual features into semantically coherent units that possess linguistic logic, thereby effectively bridging vision and language. The LLM prompt bridge appends learnable prompts, which encode medical shared knowledge from the LLM, to text embeddings, thereby effectively bridging case-specificity and medical consensus knowledge. Experimental results show the predominant performance due to LLM participation.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"33 ","pages":"146-150"},"PeriodicalIF":3.9,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145772018","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}
We consider the joint problem of online experiment design and parameter estimation for identifying nonlinear system models, while adhering to system constraints. We utilize a receding horizon approach and propose a new adaptive input design criterion, which is tailored to continuously updated parameter estimates, along with a new sequential estimator. We demonstrate the ability of the method to design informative experiments online, while steering the system within operational constraints.
{"title":"Adaptive Experiment Design for Nonlinear System Identification With Operational Constraints","authors":"Jingwei Hu;Dave Zachariah;Torbjörn Wigren;Petre Stoica","doi":"10.1109/LSP.2025.3639512","DOIUrl":"https://doi.org/10.1109/LSP.2025.3639512","url":null,"abstract":"We consider the joint problem of online experiment design and parameter estimation for identifying nonlinear system models, while adhering to system constraints. We utilize a receding horizon approach and propose a new adaptive input design criterion, which is tailored to continuously updated parameter estimates, along with a new sequential estimator. We demonstrate the ability of the method to design informative experiments online, while steering the system within operational constraints.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"33 ","pages":"151-155"},"PeriodicalIF":3.9,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145772065","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}
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