Pub Date : 2025-12-11DOI: 10.1109/LSP.2025.3643352
Zuomin Qu;Yimao Guo;Qianyue Hu;Wei Lu
Deepfakes pose significant societal risks, motivating the development of proactive defenses that embed adversarial perturbations in facial images to prevent manipulation. However, in this paper, we show that these preemptive defenses often lack robustness and reliability. We propose a novel approach, Low-Rank Adaptation (LoRA) patching, which injects a plug-and-play LoRA patch into Deepfake generators to bypass state-of-the-art defenses. A learnable gating mechanism adaptively controls the effect of the LoRA patch and prevents gradient explosions during fine-tuning. We also introduce a Multi-Modal Feature Alignment (MMFA) loss, encouraging the features of adversarial outputs to align with those of the desired outputs at the semantic level. Beyond bypassing, we present defensive LoRA patching, embedding visible warnings in the outputs as a complementary solution to mitigate this newly identified security vulnerability. With only 1,000 facial examples and a single epoch of fine-tuning, LoRA patching successfully defeats multiple proactive defenses. These results reveal a critical weakness in current paradigms and underscore the need for more robust Deepfake defense strategies.
{"title":"LoRA Patching: Exposing the Fragility of Proactive Defenses Against Deepfakes","authors":"Zuomin Qu;Yimao Guo;Qianyue Hu;Wei Lu","doi":"10.1109/LSP.2025.3643352","DOIUrl":"https://doi.org/10.1109/LSP.2025.3643352","url":null,"abstract":"Deepfakes pose significant societal risks, motivating the development of proactive defenses that embed adversarial perturbations in facial images to prevent manipulation. However, in this paper, we show that these preemptive defenses often lack robustness and reliability. We propose a novel approach, Low-Rank Adaptation (LoRA) patching, which injects a plug-and-play LoRA patch into Deepfake generators to bypass state-of-the-art defenses. A learnable gating mechanism adaptively controls the effect of the LoRA patch and prevents gradient explosions during fine-tuning. We also introduce a Multi-Modal Feature Alignment (MMFA) loss, encouraging the features of adversarial outputs to align with those of the desired outputs at the semantic level. Beyond bypassing, we present defensive LoRA patching, embedding visible warnings in the outputs as a complementary solution to mitigate this newly identified security vulnerability. With only 1,000 facial examples and a single epoch of fine-tuning, LoRA patching successfully defeats multiple proactive defenses. These results reveal a critical weakness in current paradigms and underscore the need for more robust Deepfake defense strategies.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"33 ","pages":"286-290"},"PeriodicalIF":3.9,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145830948","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 : 2025-12-11DOI: 10.1109/LSP.2025.3643359
Pingping Pan;Yunjian Zhang;You Li;Renzhong Guo
Time-frequency analysis (TFA) and ridge separation of non-stationary signals have long been research topics in signal processing. They are mutually dependent: informative time-frequency representations (TFRs) enable reliable ridge estimation, while accurate ridges refine TFRs by outlining component-wise time-frequency (TF) trajectories. However, the uncertainty principle limits TF resolution and ridge discriminability, and existing ridge tracking or optimization-based methods rely on empirical tuning and degrade with weak or closely spaced components, highlighting the need for a more robust and unified solution. This letter proposes a unified network that jointly performs TFA and ridge separation. It features a knowledge-guided short-time transform module for extracting discriminative TF features, coupled with an instance segmentation module with learnable queries that interacts with the extracted TF features to achieve ridge separation. This knowledge- and data-integrated framework enables fine-grained TFR construction and high-accuracy ridge separation, while eliminating manual parameter tuning and enhancing adaptability. Finally, experiments on simulated and real-world data validate its effectiveness.
{"title":"Learnable Time-Frequency Transform and Ridge Separation","authors":"Pingping Pan;Yunjian Zhang;You Li;Renzhong Guo","doi":"10.1109/LSP.2025.3643359","DOIUrl":"https://doi.org/10.1109/LSP.2025.3643359","url":null,"abstract":"Time-frequency analysis (TFA) and ridge separation of non-stationary signals have long been research topics in signal processing. They are mutually dependent: informative time-frequency representations (TFRs) enable reliable ridge estimation, while accurate ridges refine TFRs by outlining component-wise time-frequency (TF) trajectories. However, the uncertainty principle limits TF resolution and ridge discriminability, and existing ridge tracking or optimization-based methods rely on empirical tuning and degrade with weak or closely spaced components, highlighting the need for a more robust and unified solution. This letter proposes a unified network that jointly performs TFA and ridge separation. It features a knowledge-guided short-time transform module for extracting discriminative TF features, coupled with an instance segmentation module with learnable queries that interacts with the extracted TF features to achieve ridge separation. This knowledge- and data-integrated framework enables fine-grained TFR construction and high-accuracy ridge separation, while eliminating manual parameter tuning and enhancing adaptability. Finally, experiments on simulated and real-world data validate its effectiveness.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"33 ","pages":"296-300"},"PeriodicalIF":3.9,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145830817","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 : 2025-12-11DOI: 10.1109/LSP.2025.3643357
Daisy Das;Nabamita Deb;Saswati Sanyal Choudhury
Pregnancy is often a time of increased stress and anxiety, both psychologically and physically. More and more research is being done on the calming effects of relaxation techniques on the mother's brain. Passively listening to meditative mantras is one such method. This study investigates neuronal phase synchronization in three distinct cognitive states in pregnant women with their eyes closed: resting state (RS), mantra listening (M), and after mantra listening (AM). 32 scalp electrodes and a 128 Hz sampling rate were used to collect EEG data from 43 pregnant subjects. There were two 2-minute trials in each state. To assess the temporal synchrony of brain oscillations, the Inter-Trial Coherence (ITC), a phase-locking metric that quantifies the stability of neural phase over multiple trials, was computed. Bandpass filtering followed by Hilbert transform was used to assess ITC across the Theta (4–8 Hz), Alpha (8–13 Hz), and Beta (13–30 Hz) frequency bands. The Mantra condition had the greatest mean ITC, according to the results: 0.9117 (Theta), 0.8891 (Alpha), and 0.8083 (Beta). Conversely, the After-Mantra condition displayed moderate ITC levels of 0.6582 (Theta), 0.6510 (Alpha), and 0.6437 (Beta), whereas the Resting State produced 0.6392 (Theta), 0.6381 (Alpha), and 0.6368 (Beta). According to these results, passive mantra listening improves brain phase synchrony, especially in the lower frequency bands, and could be a useful non-invasive method of meditative pregnant relaxation.
{"title":"Inter-Trial Coherence Reveals Enhanced Synchrony During Mantra Listening","authors":"Daisy Das;Nabamita Deb;Saswati Sanyal Choudhury","doi":"10.1109/LSP.2025.3643357","DOIUrl":"https://doi.org/10.1109/LSP.2025.3643357","url":null,"abstract":"Pregnancy is often a time of increased stress and anxiety, both psychologically and physically. More and more research is being done on the calming effects of relaxation techniques on the mother's brain. Passively listening to meditative mantras is one such method. This study investigates neuronal phase synchronization in three distinct cognitive states in pregnant women with their eyes closed: resting state (RS), mantra listening (M), and after mantra listening (AM). 32 scalp electrodes and a 128 Hz sampling rate were used to collect EEG data from 43 pregnant subjects. There were two 2-minute trials in each state. To assess the temporal synchrony of brain oscillations, the Inter-Trial Coherence (ITC), a phase-locking metric that quantifies the stability of neural phase over multiple trials, was computed. Bandpass filtering followed by Hilbert transform was used to assess ITC across the Theta (4–8 Hz), Alpha (8–13 Hz), and Beta (13–30 Hz) frequency bands. The Mantra condition had the greatest mean ITC, according to the results: 0.9117 (Theta), 0.8891 (Alpha), and 0.8083 (Beta). Conversely, the After-Mantra condition displayed moderate ITC levels of 0.6582 (Theta), 0.6510 (Alpha), and 0.6437 (Beta), whereas the Resting State produced 0.6392 (Theta), 0.6381 (Alpha), and 0.6368 (Beta). According to these results, passive mantra listening improves brain phase synchrony, especially in the lower frequency bands, and could be a useful non-invasive method of meditative pregnant relaxation.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"33 ","pages":"291-295"},"PeriodicalIF":3.9,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145830760","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 : 2025-12-10DOI: 10.1109/LSP.2025.3642765
Jianhong Ye;Haiquan Zhao;Yi Peng
Building upon the mean $p$-power error (MPE) criterion, the normalized subband $p$-norm (NSPN) algorithm demonstrates superior robustness in $alpha$-stable noise environments ($1< alpha leq 2$) through effective utilization of low-order moment hidden in robust loss functions. Nevertheless, its performance degrades significantly when processing noise input or additive noise characterized by $alpha$-stable processes ($0< alpha leq 1$). To overcome these limitations, we propose a novel fractional-order NSPN (FoNSPN) algorithm that incorporates the fractional-order stochastic gradient descent (FoSGD) method into the MPE framework. Additionally, this paper also analyzes the convergence range of its step-size, the theoretical domain of values for the fractional-order $beta$, and establishes the theoretical steady-state mean square deviation (MSD) model. Simulations conducted in diverse impulsive noise environments confirm the superiority of the proposed FoNSPN algorithm against existing state-of-the-art algorithms.
{"title":"P-Norm Based Fractional-Order Robust Subband Adaptive Filtering Algorithm for Impulsive Noise and Noisy Input","authors":"Jianhong Ye;Haiquan Zhao;Yi Peng","doi":"10.1109/LSP.2025.3642765","DOIUrl":"https://doi.org/10.1109/LSP.2025.3642765","url":null,"abstract":"Building upon the mean <inline-formula><tex-math>$p$</tex-math></inline-formula>-power error (MPE) criterion, the normalized subband <inline-formula><tex-math>$p$</tex-math></inline-formula>-norm (NSPN) algorithm demonstrates superior robustness in <inline-formula><tex-math>$alpha$</tex-math></inline-formula>-stable noise environments (<inline-formula><tex-math>$1< alpha leq 2$</tex-math></inline-formula>) through effective utilization of low-order moment hidden in robust loss functions. Nevertheless, its performance degrades significantly when processing noise input or additive noise characterized by <inline-formula><tex-math>$alpha$</tex-math></inline-formula>-stable processes (<inline-formula><tex-math>$0< alpha leq 1$</tex-math></inline-formula>). To overcome these limitations, we propose a novel fractional-order NSPN (FoNSPN) algorithm that incorporates the fractional-order stochastic gradient descent (FoSGD) method into the MPE framework. Additionally, this paper also analyzes the convergence range of its step-size, the theoretical domain of values for the fractional-order <inline-formula><tex-math>$beta$</tex-math></inline-formula>, and establishes the theoretical steady-state mean square deviation (MSD) model. Simulations conducted in diverse impulsive noise environments confirm the superiority of the proposed FoNSPN algorithm against existing state-of-the-art algorithms.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"33 ","pages":"281-285"},"PeriodicalIF":3.9,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145830893","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 : 2025-12-09DOI: 10.1109/LSP.2025.3642058
Petr Fiedler;Kamil Dedecius
The letter investigates the problem of distributed multitarget tracking with a network of sensors with limited and partially overlapping or non-overlapping fields of view. The information processing is based on information diffusion, where each sensor can communicate only with its adjacent neighbors. The communication comprises an adaptation phase suited for the exchange of measurements, followed by a combination phase where the estimates are shared and fused via arithmetic average rule. Each phase is performed only once at each discrete time step, thus effectively reducing computational, memory, and communication overheads. An important part of the solution is the self-referencing mechanism, allowing the incorporation of only those neighbors' information that aligns with local estimates or enhances them. The simulation example demonstrates improved localization performance and resilience to misdetections.
{"title":"Self-Referencing Adapt-Then-Combine Information Diffusion Scheme for Distributed PHD Filtering","authors":"Petr Fiedler;Kamil Dedecius","doi":"10.1109/LSP.2025.3642058","DOIUrl":"https://doi.org/10.1109/LSP.2025.3642058","url":null,"abstract":"The letter investigates the problem of distributed multitarget tracking with a network of sensors with limited and partially overlapping or non-overlapping fields of view. The information processing is based on information diffusion, where each sensor can communicate only with its adjacent neighbors. The communication comprises an adaptation phase suited for the exchange of measurements, followed by a combination phase where the estimates are shared and fused via arithmetic average rule. Each phase is performed only once at each discrete time step, thus effectively reducing computational, memory, and communication overheads. An important part of the solution is the self-referencing mechanism, allowing the incorporation of only those neighbors' information that aligns with local estimates or enhances them. The simulation example demonstrates improved localization performance and resilience to misdetections.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"33 ","pages":"251-255"},"PeriodicalIF":3.9,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145830833","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 : 2025-12-08DOI: 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}
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