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Robust quaternion Kalman filter for state saturation systems with stochastic nonlinear disturbances

IF 3.4 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Signal Processing

Pub Date : 2025-02-08 DOI: 10.1016/j.sigpro.2025.109931

Dongyuan Lin , Xiaofeng Chen , Peng Cai , Yunfei Zheng , Qiangqiang Zhang , Junhui Qian , Shiyuan Wang

To tackle the quaternion robust state estimation problem, the robust quaternion Kalman filter (RQKF) has been developed for quaternion signals by the quaternion maximum correntropy criterion (QMCC) under non-Gaussian noises. However, in the presence of saturation phenomena and nonlinear disturbances impacting quaternion systems, the performance of RQKF may deteriorate. Hence, this paper focuses on the quaternion Kalman filtering issue for state saturation systems with stochastic nonlinear disturbances under non-Gaussian noises. First, a feasible upper bound on the filtering error covariance is first obtained by some quaternion matrix techniques, and then a QMCC-based RQKF for state saturation systems (MCQKF-SS) is developed. The posterior estimate of the MCQKF-SS algorithm, developed as an iterative online method with a recursive structure, is updated by a quaternion iterative equation (QIE). Subsequently, a sufficient condition is proposed to ensure the uniqueness of the QIE’s fixed point, thereby guaranteeing the convergence of MCQKF-SS. Moreover, an adaptive kernel width strategy addresses the kernel width selection problem, leading to the development of a variable kernel width version of MCQKF-SS (VKMCQKF-SS). Finally, simulation results of two numerical examples verify the effectiveness and robustness of proposed quaternion algorithms in the considered environment.

{"title":"Robust quaternion Kalman filter for state saturation systems with stochastic nonlinear disturbances","authors":"Dongyuan Lin , Xiaofeng Chen , Peng Cai , Yunfei Zheng , Qiangqiang Zhang , Junhui Qian , Shiyuan Wang","doi":"10.1016/j.sigpro.2025.109931","DOIUrl":"10.1016/j.sigpro.2025.109931","url":null,"abstract":"<div><div>To tackle the quaternion robust state estimation problem, the robust quaternion Kalman filter (RQKF) has been developed for quaternion signals by the quaternion maximum correntropy criterion (QMCC) under non-Gaussian noises. However, in the presence of saturation phenomena and nonlinear disturbances impacting quaternion systems, the performance of RQKF may deteriorate. Hence, this paper focuses on the quaternion Kalman filtering issue for state saturation systems with stochastic nonlinear disturbances under non-Gaussian noises. First, a feasible upper bound on the filtering error covariance is first obtained by some quaternion matrix techniques, and then a QMCC-based RQKF for state saturation systems (MCQKF-SS) is developed. The posterior estimate of the MCQKF-SS algorithm, developed as an iterative online method with a recursive structure, is updated by a quaternion iterative equation (QIE). Subsequently, a sufficient condition is proposed to ensure the uniqueness of the QIE’s fixed point, thereby guaranteeing the convergence of MCQKF-SS. Moreover, an adaptive kernel width strategy addresses the kernel width selection problem, leading to the development of a variable kernel width version of MCQKF-SS (VKMCQKF-SS). Finally, simulation results of two numerical examples verify the effectiveness and robustness of proposed quaternion algorithms in the considered environment.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"232 ","pages":"Article 109931"},"PeriodicalIF":3.4,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388284","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

Multi-operator retargeting for stereoscopic images towards salient feature classification

IF 3.4 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Signal Processing

Pub Date : 2025-02-08 DOI: 10.1016/j.sigpro.2024.109885

Yanli Wu, Zhenhua Tang, Xuejun Zhang

Most stereoscopic image retargeting (SIR) algorithms often use a single operator or a fixed strategy to resize various images. They ignore the adaptation between retargeting methods and specific image features, hence they fail to achieve a desirable retargeting quality. We propose a multi-operator retargeting method for stereoscopic images based on salient feature classification to address the issue. Specially, the original stereo image is first classified according to its salient features, including spatial and depth salient features. Then three operators, including stereo cropping, stereo seam carving, and stereo uniform scaling are combined to perform image retargeting in terms of image category, salient features, and target size. In particular, we design a retargeting strategy used to realize adaptive switching between operators. Besides, we construct two distance energy terms and integrate them into the total energy function of stereo cropping and stereo seam carving respectively to improve the quality of retargeted images. Extensive experiment results show that the performance of the proposed method is superior to that of other SIR algorithms. The proposed method can effectively preserve the integrity and geometry of the salient content while ensuring the stereoscopic sense of the images during retargeting.

{"title":"Multi-operator retargeting for stereoscopic images towards salient feature classification","authors":"Yanli Wu, Zhenhua Tang, Xuejun Zhang","doi":"10.1016/j.sigpro.2024.109885","DOIUrl":"10.1016/j.sigpro.2024.109885","url":null,"abstract":"<div><div>Most stereoscopic image retargeting (SIR) algorithms often use a single operator or a fixed strategy to resize various images. They ignore the adaptation between retargeting methods and specific image features, hence they fail to achieve a desirable retargeting quality. We propose a multi-operator retargeting method for stereoscopic images based on salient feature classification to address the issue. Specially, the original stereo image is first classified according to its salient features, including spatial and depth salient features. Then three operators, including stereo cropping, stereo seam carving, and stereo uniform scaling are combined to perform image retargeting in terms of image category, salient features, and target size. In particular, we design a retargeting strategy used to realize adaptive switching between operators. Besides, we construct two distance energy terms and integrate them into the total energy function of stereo cropping and stereo seam carving respectively to improve the quality of retargeted images. Extensive experiment results show that the performance of the proposed method is superior to that of other SIR algorithms. The proposed method can effectively preserve the integrity and geometry of the salient content while ensuring the stereoscopic sense of the images during retargeting.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"232 ","pages":"Article 109885"},"PeriodicalIF":3.4,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143378894","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

Analog–digital precoding based on mutual coupling considering the actual radiation performance of MIMO antenna arrays

IF 3.4 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Signal Processing

Pub Date : 2025-02-08 DOI: 10.1016/j.sigpro.2025.109932

Jianchuan Wei , Xiaoming Chen , Ruihai Chen , Chongwen Huang , Xiaoyu Huang , Wei E.I. Sha , Mérouane Debbah

Mutual coupling among antenna array elements can have impacts on not only the elements’ mutual impedance but also the radiation characteristics, which is usually not considered appropriately in previous precoding researches. In this paper, an analog–digital precoding scheme based on mutual coupling is proposed considering the actual radiation performance of multiple-input multiple-output antenna arrays. Instead of reducing or eliminating the mutual coupling effect as in the conventional approach, this paper focuses on exploiting it to improve the system performance. Open-circuit radiation pattern is firstly introduced in the system model to further evaluate the effect of mutual coupling on the antenna radiation performance. Conventional linear precoding is applied in the digital domain. As for the analog domain, to minimize the noise amplification factor and thus maximize the receive signal-to-noise ratio of the system through exploiting the mutual coupling effect, the proper load impedances and antenna weighting coefficients are selected by convex optimization and Newton method. Simulation results show that the proposed scheme performs significantly better than the conventional one without mutual coupling in zero forcing and minimum mean square error systems. Besides, greater performance gain in MIMO systems with smaller antenna spacing, higher dimensionality and larger apparent power are observed.

{"title":"Analog–digital precoding based on mutual coupling considering the actual radiation performance of MIMO antenna arrays","authors":"Jianchuan Wei , Xiaoming Chen , Ruihai Chen , Chongwen Huang , Xiaoyu Huang , Wei E.I. Sha , Mérouane Debbah","doi":"10.1016/j.sigpro.2025.109932","DOIUrl":"10.1016/j.sigpro.2025.109932","url":null,"abstract":"<div><div>Mutual coupling among antenna array elements can have impacts on not only the elements’ mutual impedance but also the radiation characteristics, which is usually not considered appropriately in previous precoding researches. In this paper, an analog–digital precoding scheme based on mutual coupling is proposed considering the actual radiation performance of multiple-input multiple-output antenna arrays. Instead of reducing or eliminating the mutual coupling effect as in the conventional approach, this paper focuses on exploiting it to improve the system performance. Open-circuit radiation pattern is firstly introduced in the system model to further evaluate the effect of mutual coupling on the antenna radiation performance. Conventional linear precoding is applied in the digital domain. As for the analog domain, to minimize the noise amplification factor and thus maximize the receive signal-to-noise ratio of the system through exploiting the mutual coupling effect, the proper load impedances and antenna weighting coefficients are selected by convex optimization and Newton method. Simulation results show that the proposed scheme performs significantly better than the conventional one without mutual coupling in zero forcing and minimum mean square error systems. Besides, greater performance gain in MIMO systems with smaller antenna spacing, higher dimensionality and larger apparent power are observed.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"232 ","pages":"Article 109932"},"PeriodicalIF":3.4,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143378397","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

An effective gridless sparse recovery space-time adaptive algorithm for airborne radar with non-uniform linear arrays

IF 3.4 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Signal Processing

Pub Date : 2025-02-06 DOI: 10.1016/j.sigpro.2025.109928

Ciyuan Liu, Tong Wang, Degen Wang, Xinying Zhang

In recent years, gridless sparse recovery based space–time adaptive processing (SR-STAP) algorithms have attracted extensive attention due to their excellent estimation performance even with grid mismatch. Among them, the SR-STAP algorithm based on atomic norm minimization (ANM) stands out as the most representative. However, most current gridless SR-STAP algorithms rely on the 2D Vandermonde structure of the space–time steering vector and are therefore restricted to uniform linear arrays (ULAs). In practice, it is essential to efficiently utilize gridless SR-STAP methods to non-uniform linear arrays (NLAs) with varying configurations. In this paper, we propose a fast gridless SR-STAP method based on ANM for NLAs with multiple measurement vectors (MMV), namely FNLAANM-STAP. Inspired by the array manifold separation technique, we reformulate the original spatial steering vector as the product of a Vandermonde vector and a sampling matrix, adapting it for NLAs without compromising efficiency. Then we develop an efficient iterative approach by utilizing the accelerated proximal gradient (APG) framework, which offers a low-complexity solution. Simulation results demonstrate that our proposed method outperforms in clutter suppression while requiring less computational complexity.

{"title":"An effective gridless sparse recovery space-time adaptive algorithm for airborne radar with non-uniform linear arrays","authors":"Ciyuan Liu, Tong Wang, Degen Wang, Xinying Zhang","doi":"10.1016/j.sigpro.2025.109928","DOIUrl":"10.1016/j.sigpro.2025.109928","url":null,"abstract":"<div><div>In recent years, gridless sparse recovery based space–time adaptive processing (SR-STAP) algorithms have attracted extensive attention due to their excellent estimation performance even with grid mismatch. Among them, the SR-STAP algorithm based on atomic norm minimization (ANM) stands out as the most representative. However, most current gridless SR-STAP algorithms rely on the 2D Vandermonde structure of the space–time steering vector and are therefore restricted to uniform linear arrays (ULAs). In practice, it is essential to efficiently utilize gridless SR-STAP methods to non-uniform linear arrays (NLAs) with varying configurations. In this paper, we propose a fast gridless SR-STAP method based on ANM for NLAs with multiple measurement vectors (MMV), namely FNLAANM-STAP. Inspired by the array manifold separation technique, we reformulate the original spatial steering vector as the product of a Vandermonde vector and a sampling matrix, adapting it for NLAs without compromising efficiency. Then we develop an efficient iterative approach by utilizing the accelerated proximal gradient (APG) framework, which offers a low-complexity solution. Simulation results demonstrate that our proposed method outperforms in clutter suppression while requiring less computational complexity.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"232 ","pages":"Article 109928"},"PeriodicalIF":3.4,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143372738","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 adaptive beamforming with interference-plus-noise covariance matrix reconstruction for FDA-MIMO radar

IF 3.4 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Signal Processing

Pub Date : 2025-02-06 DOI: 10.1016/j.sigpro.2025.109929

Yudian Hou, Wen-Qin Wang

Frequency-diverse array multiple-input-multiple-output (FDA-MIMO) antenna offers promising potential applications such as joint range-angle estimation, secure communication, and dual radar-communication systems. However, robust adaptive beamforming (RAB) for FDA-MIMO plays an important role, but it has not been well explored. In this paper, we identify that both steering vector and interference-plus-noise covariance (INC) matrix in FDA-MIMO antenna are time-variant, which may cause significant performance degradation. To address this issue for practical applications, we develop a RAB beamformer for FDA-MIMO by employing a two-dimensional decoupled atomic norm minimization (2D-DANM) approach for the INC matrix reconstruction. Unlike traditional methods that rely on multiple data snapshots, the proposed approach requires only a single snapshot, which can efficiently reconstruct the INC matrix to mitigate the time-variance. The steering vector is corrected through the reconstructed INC matrix by solving a quadratically constrained quadratic programming (QCQP) problem. The superiority is verified with simulation results, particularly in the term of output signal-to-interference-plus-noise ratio (SINR).

{"title":"Robust adaptive beamforming with interference-plus-noise covariance matrix reconstruction for FDA-MIMO radar","authors":"Yudian Hou, Wen-Qin Wang","doi":"10.1016/j.sigpro.2025.109929","DOIUrl":"10.1016/j.sigpro.2025.109929","url":null,"abstract":"<div><div>Frequency-diverse array multiple-input-multiple-output (FDA-MIMO) antenna offers promising potential applications such as joint range-angle estimation, secure communication, and dual radar-communication systems. However, robust adaptive beamforming (RAB) for FDA-MIMO plays an important role, but it has not been well explored. In this paper, we identify that both steering vector and interference-plus-noise covariance (INC) matrix in FDA-MIMO antenna are time-variant, which may cause significant performance degradation. To address this issue for practical applications, we develop a RAB beamformer for FDA-MIMO by employing a two-dimensional decoupled atomic norm minimization (2D-DANM) approach for the INC matrix reconstruction. Unlike traditional methods that rely on multiple data snapshots, the proposed approach requires only a single snapshot, which can efficiently reconstruct the INC matrix to mitigate the time-variance. The steering vector is corrected through the reconstructed INC matrix by solving a quadratically constrained quadratic programming (QCQP) problem. The superiority is verified with simulation results, particularly in the term of output signal-to-interference-plus-noise ratio (SINR).</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"232 ","pages":"Article 109929"},"PeriodicalIF":3.4,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143349583","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

A low computational complexity and high accuracy DOA estimation method in the hybrid analog-digital system with interleaved subarrays

IF 3.4 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Signal Processing

Pub Date : 2025-02-04 DOI: 10.1016/j.sigpro.2025.109925

Guodong Wang, Yan Zhou

The large-scale, partially connected phase-shifter Hybrid Analog-Digital System (HADS) has attracted significant attention due to its low hardware complexity, high reconfigurability, and robustness to failures. Direction-of-Arrival (DOA) estimation presents a critical challenge in HADS, as it directly impacts the Signal-to-Noise Ratio (SNR) and throughput. Existing DOA estimation methods in HADS, however, are hindered by high complexity, the need for sign corrections, and ambiguity. This paper proposes an accurate and unambiguous DOA estimation method for HADS with interleaved subarrays (HADSIS) under specific phase shift conditions. The method utilizes cross-correlation between signals received by adjacent subarrays to directly estimate the DOA through coherent accumulation. This approach simplifies DOA estimation and eliminates ambiguity, thereby significantly enhancing estimation efficiency. Furthermore, the sign of the cross-correlation is uniquely determined by the parity of the number of subarrays, eliminating the need for further sign corrections during signal accumulation. Finally, simulation experiments are conducted to validate the performance of the proposed method. Our approach enhances the SNR of the cross-correlation results, thereby ensuring more accurate DOA estimation. Its key features include low computational complexity and high accuracy.

{"title":"A low computational complexity and high accuracy DOA estimation method in the hybrid analog-digital system with interleaved subarrays","authors":"Guodong Wang, Yan Zhou","doi":"10.1016/j.sigpro.2025.109925","DOIUrl":"10.1016/j.sigpro.2025.109925","url":null,"abstract":"<div><div>The large-scale, partially connected phase-shifter Hybrid Analog-Digital System (HADS) has attracted significant attention due to its low hardware complexity, high reconfigurability, and robustness to failures. Direction-of-Arrival (DOA) estimation presents a critical challenge in HADS, as it directly impacts the Signal-to-Noise Ratio (SNR) and throughput. Existing DOA estimation methods in HADS, however, are hindered by high complexity, the need for sign corrections, and ambiguity. This paper proposes an accurate and unambiguous DOA estimation method for HADS with interleaved subarrays (HADSIS) under specific phase shift conditions. The method utilizes cross-correlation between signals received by adjacent subarrays to directly estimate the DOA through coherent accumulation. This approach simplifies DOA estimation and eliminates ambiguity, thereby significantly enhancing estimation efficiency. Furthermore, the sign of the cross-correlation is uniquely determined by the parity of the number of subarrays, eliminating the need for further sign corrections during signal accumulation. Finally, simulation experiments are conducted to validate the performance of the proposed method. Our approach enhances the SNR of the cross-correlation results, thereby ensuring more accurate DOA estimation. Its key features include low computational complexity and high accuracy.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"232 ","pages":"Article 109925"},"PeriodicalIF":3.4,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143349667","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

Alternating minimization algorithm for unlabeled sensing and linked linear regression

IF 3.4 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Signal Processing

Pub Date : 2025-02-04 DOI: 10.1016/j.sigpro.2025.109927

Ahmed Ali Abbasi , Shuchin Aeron , Abiy Tasissa

Unlabeled sensing is a linear inverse problem with permuted measurements. We propose an alternating minimization (AltMin) algorithm with a suitable initialization for two widely considered permutation models: partially shuffled/

k

-sparse permutations and

r

-local/block diagonal permutations. Key to the performance of the AltMin algorithm is the initialization. For the exact unlabeled sensing problem, assuming either a Gaussian measurement matrix or a sub-Gaussian signal, we bound the initialization error in terms of the number of blocks

s

and the number of shuffles

k

. Experimental results show that our algorithm is fast, applicable to both permutation models, and robust to choice of measurement matrix. We also test our algorithm on several real datasets for the ‘linked linear regression’ problem and show superior performance compared to baseline methods.

引用次数: 0

Single-tone frequency estimation using modified autocorrelation and polynomial root-finding

IF 3.4 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Signal Processing

Pub Date : 2025-02-03 DOI: 10.1016/j.sigpro.2025.109923

Hong-Cheng Liang, Hing Cheung So

Based on a novel extension scheme to autocorrelation with higher lags, this paper devises an unbiased and nearly-optimal estimator for a single real sinusoid in white noise. Specifically, the new autocorrelation functions are utilized to construct a univariate polynomial equation parameterized by the frequency. By comparing all roots of the polynomial equation with the cosine of a coarse estimate, the root corresponding to the sinusoidal frequency can be determined. The frequency variance is derived, which is then employed to find the optimal lag of autocorrelation for attaining the minimum mean square frequency error. Computer simulations are provided to corroborate the theoretical development and contrast with several existing estimators as well as the Cramér–Rao lower bound. The code link of our proposed estimator is available at https://github.com/Amao-Liang/MAPR-Algorithm-for-Single-Tone-Frequency-Estimation.

引用次数: 0

Accelerated and enhanced multiplicative deblurring schemes

IF 3.4 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Signal Processing

Pub Date : 2025-02-03 DOI: 10.1016/j.sigpro.2025.109910

Pierre-Alain Fayolle , Alexander G. Belyaev

We propose modifications of the Richardson–Lucy iterations (RL) and the Image Space Reconstruction Algorithm (ISRA) that demonstrate accelerated convergence and lead to improved image restoration results. We show that the iterations of RL, ISRA, and the proposed modifications can be interpreted as fixed-point iterations corresponding to the minimizers of certain variational problems. We demonstrate that combining each iteration of the proposed modifications with an adaptive image smoothing procedure leads to substantial improvements of the image restoration results. An implementation is available at https://github.com/fayolle/Mult_BBDeblur_demo.

引用次数: 0

Stochastic approximate inference of latent information in epidemic model: A data-driven approach

IF 3.4 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Signal Processing

Pub Date : 2025-02-01 DOI: 10.1016/j.sigpro.2025.109919

Jungmin Kwon , Sujin Ahn , Hyunggon Park , Minhae Kwon

Precise estimates of disease transmissibility, made using mathematical methods, are a critical part of epidemiology. This paper proposes a stochastic compartmental model based on a Discrete-Time Markov chain (DTMC) to estimate the transmission rate and important latent variables, such as the number of hidden patients. To find the transmission rate that best represents the reported data (e.g., the number of confirmed cases), we formulate a maximum log-likelihood estimation problem. However, this problem is challenging because it includes the posterior distribution of the reported data, which is mathematically intractable. Therefore, we relax the problem by proposing surrogate optimization with stochastic approximation, which allows us to successfully estimate the transmission rate and latent variables. To assess the proposed inference model, extensive simulations are performed using datasets from COVID-19, seasonal influenza, and mpox. The results confirm that the proposed algorithm finds a transmission rate explaining the macroscopic and microscopic variations in the waves of infectious diseases. Compared with existing solutions, the proposed algorithm better explains real-world disease evolution, such as reinfection and microscopic fluctuations in waves.

{"title":"Stochastic approximate inference of latent information in epidemic model: A data-driven approach","authors":"Jungmin Kwon , Sujin Ahn , Hyunggon Park , Minhae Kwon","doi":"10.1016/j.sigpro.2025.109919","DOIUrl":"10.1016/j.sigpro.2025.109919","url":null,"abstract":"<div><div>Precise estimates of disease transmissibility, made using mathematical methods, are a critical part of epidemiology. This paper proposes a stochastic compartmental model based on a Discrete-Time Markov chain (DTMC) to estimate the transmission rate and important latent variables, such as the number of hidden patients. To find the transmission rate that best represents the reported data (e.g., the number of confirmed cases), we formulate a maximum log-likelihood estimation problem. However, this problem is challenging because it includes the posterior distribution of the reported data, which is mathematically intractable. Therefore, we relax the problem by proposing surrogate optimization with stochastic approximation, which allows us to successfully estimate the transmission rate and latent variables. To assess the proposed inference model, extensive simulations are performed using datasets from COVID-19, seasonal influenza, and mpox. The results confirm that the proposed algorithm finds a transmission rate explaining the macroscopic and microscopic variations in the waves of infectious diseases. Compared with existing solutions, the proposed algorithm better explains real-world disease evolution, such as reinfection and microscopic fluctuations in waves.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"232 ","pages":"Article 109919"},"PeriodicalIF":3.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143378895","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

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