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One New Designed Wiener Filter Method for SQUID Magnetogastrogram Detection

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Li, Hua Zhang, Mingyue Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol Natl Key Lab Mat Integrated Circuits Shanghai 200050 Peoples R China

Superconducting quantum interference device (SQUID) magnetogastrogram (MGG) is a medical functional imaging method with great clinical potential for noninvasive diagnosis of gastric diseases. MGG signal frequency is about 0.05 Hz, and the low-frequency environmental noise interference is serious, can be several times stronger in magnitude than the signals of interest, and may severely impede the extraction of relevant information. Wiener filter is one classic denoising solution for biomagnetic applications. In this article, a new high-pass Wiener filter and signal processing framework for MGG measurement is proposed, which can filter low-frequency noises not only specific artifacts. The filter was successfully applied to MGG signal denoising. Using this general Wiener filter frame, the filter signal-to-noise ratio (SNR) is 11.3 dB better than the classical Wiener filter and it also has 16.7 dB of SNR better than without signal noise separation step. Based on our methods, 36-point array MGG signals were detected successfully and the results were consistent with the main gastric slow wave activity.

关键词： Wiener filters Signal to noise ratio Magnetic separation Superconducting filters Adaptive filters Fitting filtering theory filtering algorithms SQUIDs Nonlinear filters Denoising solutions magnetogastrogram (MGG) superconducting quantum interference device (SQUID) applications SQUID biomagnetism Wiener filter

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Optimal Discrete-Time Distributed Kalman Filter With Reduced Communication

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2025年第4期70卷 2754-2761页

作者： Battilotti, Stefano Borri, Alessandro Cacace, Filippo d'Angelo, Massimiliano Sapienza Univ Roma Dipartimento Ingn Informat Automat & Gestionale I-00185 Rome Italy Natl Res Council Italy CNR Ist Anal Sistemi & Informat Antonio Ruberti IASI I-00185 Rome Italy Via Giosue Carducci I-67100 Laquila Italy Univ Campus Biomed Roma I-00128 Rome Italy

This article proposes and analyzes a distributed filter where the consensus term is a virtual output rather than the local state estimate. This feature allows for reducing the data transmitted among nodes at each intermediate step, namely, instead of exchanging a vector of the dimension of the state, nodes exchange a vector of the dimension of the rank of the total output matrix. The main finding is that the convergence to the performance of the centralized Kalman filter and mean-square boundedness of the estimation error are not lost despite an increase in the number of consensus steps. Simulations show that the total communication overhead is reduced without performance degradation with respect to the original distributed filter, where nodes exchange local state estimates.

关键词： Kalman filters Vectors filtering algorithms Covariance matrices Symmetric matrices Information filters Estimation error Sensors Noise Mathematical models Distributed filtering network analysis stochastic systems

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Optimization of Delta-Sigma Modulator Based on Reinforcement Learning for Mobile Fronthaul

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IEEE PHOTONICS TECHNOLOGY LETTERS 2025年第7期37卷 397-400页

作者： Yan, Zijun Zhu, Yixiao Yang, Guangying Hu, Weisheng Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Dept Elect Engn State Key Lab Adv Opt Commun Syst & Networks Shanghai 200240 Peoples R China

Delta-sigma modulator (DSM) offers a high-fidelity solution for mobile fronthaul. It achieves a high quantization signal-to-noise ratio through oversampling and noise shaping. However, the DSM is a nonlinear system that lacks of comprehensive design method for the loop filter. The traditional loop filter design methods established on linear filter systems have suboptimal performance. In this work, we propose a reinforcement learning (RL)-based method to optimize the design of the loop filter, which utilizes the historical information. The RL method achieves a 14-dB and a >2.4-dB SNR gain compared to traditional methods and polynomial fitting-based approach, respectively.

关键词： Signal to noise ratio Training Optimization Nonlinear filters Maximum likelihood detection Trajectory Poles and zeros filtering algorithms Reinforcement learning Optical filters Delta-sigma modulator mobile fronthaul reinforcement learning optimization

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Adaptive Heading Estimation Method for Pedestrian Dead Reckoning With Magnetic Interference

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Shi, Fangyan Xu, Xiangbo Zhu, Yahui Zhang, Yanan Beijing Forestry Univ Sch Technol Beijing 100083 Peoples R China

Heading estimation is crucial for pedestrian dead reckoning. Magnetometers correct gyroscopic drift by measuring Earth's magnetic field, but their susceptibility to localized magnetic interference compromises attitude estimation accuracy. To address this issue, an improved Mahony heading estimation method based on proportional-integral (PI) control and magnetic disturbance detection is proposed. The method constructs the system model based on a characteristic polynomial and estimates PI filter parameters using damping ratio and natural frequency. The generalized likelihood ratio test (GLRT) is incorporated into the method to detect magnetic interference and evaluates magnetometer reliability. Additionally, an adaptive adjustment mechanism is designed to dynamically optimize the filter's bandwidth and damping ratio based on disturbance conditions and sensor variance, enhancing heading estimation accuracy. Experiments were conducted on regular terrain and random terrain to validate the algorithm's effectiveness. The proposed method reduces the root mean square error by 9.43% and 35.47% compared to the Mahony algorithm, and by 70.73% and 76.49% compared to the extended Kalman filter (EKF)-based algorithm. The experimental results show that the improved Mahony filter effectively minimizes magnetic interference on heading estimation, while maintaining good real-time performance and accuracy.

关键词： Magnetometers Estimation Magnetic separation Interference Magnetic field measurement Magnetomechanical effects Accuracy Pedestrians filtering algorithms Real-time systems Heading estimation magnetic interference Mahony filter pedestrian positioning sensor fusion

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Poisson Multi-Bernoulli Mixture Filter for Multiple Extended Object Tracking Using KolmogorovSmirnov Test

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IEEE SENSORS JOURNAL 2025年第4期25卷 6541-6555页

作者： Li, Peng Chen, Cheng Sun, Youpeng Wang, Wenhui Jiangsu Univ Technol Sch Comp Changzhou 213001 Peoples R China Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214122 Peoples R China Jiangsu Univ Technol Sch Mech Engn Changzhou 213001 Peoples R China

The Poisson multi-Bernoulli mixture (PMBM) filter has been proved to be effective in tracking missed objects and an unknown number of objects with clutter in complex scenarios. However, when the objects are closely spaced, spawning or maneuvering will affect the performance of traditional object prior-based partitioning methods. To solve this problem, this article proposes an expectation-maximization (EM) partitioning method for PMBM filtering of extended target tracking based on the improved Kolmogorov-Smirnov (KS) test. The initial partitioning and correlation of measurements are performed from the perspective of measurement distribution. Data association is further optimized by combining with the prior to obtain more reasonable associations and guide the generation of the spawning object Poisson point process (PPP). In addition, a method is proposed for dynamically generating the spawning object PPP to achieve accurate tracking of spawning objects. Simulation results show that the proposed method has good robustness in scenarios involving intersection, closely spaced objects, and derivation compared with the Gamma-Gaussian inverse Wishart (GGIW)-PMBM filter.

关键词： Sensors Covariance matrices Shape Laser radar Estimation Object tracking Gamma distribution filtering algorithms Clutter Accuracy Extended object tracking Kolmogorov-Smirnov (KS) test Poisson multi-Bernoulli mixture (PMBM) tracking

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A Pre-Processing Signal Modeling Approach for Multichannel ATI Digitizers

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Mei, Sitao Zhao, Yijiu Shao, E. Wang, Yusen Zhang, Yi Univ Elect Sci & Technol China Sch Automat Engn Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Shenzhen Inst Adv Study Shenzhen 518110 Peoples R China

Asynchronous time-interleaved (ATI) analog-to-digital converter (ADC) architecture utilizes pre-processing, low-pass filtering, and ADCs to simultaneously enhance the sampling rate and bandwidth of the acquisition system. We propose a novel approach to generate pre-processing signal in multichannel ATI architecture. The generated pre-processing signal is no longer limited to pulse wave or square wave with a fixed duty. We derive the condition for reconstructing input signal as the invertibility of the pre-processing matrix. Additionally, we analyzed the error sources in the multichannel ATI. Based on the spectral shift characteristics of the ATI, we propose an error coefficient estimation method. Using the estimated error coefficient, a digital filter can be designed to calibrate the system. A four-channel hardware prototype was developed to evaluate the performance of the proposed method, satisfactory results were obtained and the feasibility of the proposed approach was demonstrated.

关键词： Low-pass filters Calibration Mixers Estimation Frequency response filtering algorithms Time-frequency analysis Signal processing algorithms Hardware Fourier series Analog-to-digital converters (ADCs) asynchronous time-interleaved (ATI) frequency-interleaved (FI) wideband

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ReAdapt-II: Energy-Quality Optimizations for VLSI Adaptive Filters Through Automatic Reconfiguration and Built-In Iterative Dividers

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IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS 2025年第2期33卷 558-562页

作者： Pereira, Pedro T. L. Costa, Patricia Ucker L. da Costa, Eduardo Flores, Paulo Bampi, Sergio Fed Univ Rio Grande UFRGS Informat Inst Grad Program Microelect PGMICRO BR-91501970 Porto Alegre Brazil Univ Catolica Pelotas UCPel Grad Program Elect Engn & Comp BR-96015560 Pelotas Brazil Univ Lisboa ULisboa INESC ID P-1000029 Lisbon Portugal Univ Lisboa ULisboa Inst Super Tecn IST P-1000029 Lisbon Portugal

Adaptive filters using least mean square (LMS) algorithms offer high precision, low complexity, and fast convergence, but choosing the correct algorithm can be difficult and time-consuming. In this brief, we present ReAdapt-II, a VLSI circuit that enhances energy efficiency in adaptive filters through automatic reconfiguration and built-in iterative dividers, optimizing the energy-quality (EQ) tradeoff. This design features a self-selecting, reconfigurable hardware system with four adaptive algorithms, integrating iterative-based dividers and reusing arithmetic operators. Our results show a minimum energy consumption reduction of 39.75%, a 66.61% reduction in the circuit area, and a maximum accuracy increase of 17.07% compared with the previous ReAdapt architecture.

关键词： Adaptive filters filtering algorithms Very large scale integration Computer architecture Accuracy Power demand Logic gates energy-quality (EQ)-scalable filtering feedback-control circuit iterative-based divider reconfigurable architecture

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Semiglobal Safety-Filtered Extremum Seeking With Unknown CBFs

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2025年第3期70卷 1698-1713页

作者： Williams, Alan Krstic, Miroslav Scheinker, Alexander Univ Calif San Diego Dept Mech & Aerosp Engn San Diego CA 92093 USA Los Alamos Natl Lab Accelerator Operat & Technol Appl Electrodynam AOT Los Alamos NM 87545 USA

We introduce a safe extremum-seeking algorithm that achieves the minimization of an unknown objective function while ensuring that an unknown, yet measured, control barrier function (CBF) remains above an arbitrarily small negative value for all time. In other words, "practical safety" is maintained during the entire period of convergence to the constrained extremum. Our design is based on quadratic program (QP) CBF style filters for safety, which is applied in an average and estimated sense. Using nonsmooth analysis tools, we guarantee semiglobal practical asymptotic (SPA) stability of the global constrained optimum, practical convergence to the safe set if starting in a condition violating the CBF, and practical safety for all time-semiglobally-if starting in safe set. The safety result of this article is analogous with modern notions of SPA stability, guaranteeing that, for any small violation of safety, there exist design coefficients that guarantee that such a small violation is not exceeded. This article outlines a set of sufficient conditions on the barrier and objective functions, and by way of a Lyapunov argument, we demonstrate that nonconvex constrained optimization problems can be solved. We present these results in the setting of a static map and a dynamical system. A simulation example illustrates the results.

关键词： Safety Heuristic algorithms Asymptotic stability Optimization filtering algorithms Thermal stability Switches Convergence Approximation algorithms Stability criteria Constrained optimization extremum seeking (ES) safe control

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State Estimation With Nonlinear Inequality Constraints for Small Celestial Body Flexible Landing

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2025年第2期61卷 4155-4167页

作者： Cui, Pingyuan Chen, Zelong Ge, Dantong Zhu, Shengying Cui, Shisheng Beijing Inst Technol Sch Aerosp Engn Beijing 100081 Peoples R China Beijing Inst Technol Minist Ind & Informat Technol Key Lab Autonomous Nav Control Deep Space Explorat Beijing 100081 Peoples R China Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China Beijing Inst Technol State Key Lab Autonomous Intelligent Unmanned Syst Beijing 100081 Peoples R China

Compared to rigid landers, a flexible lander can reduce the landing risks in small celestial body missions by taking advantage of its soft structure. In this article, we focus on the autonomous navigation problem of the flexible lander. Particularly, we consider the utilization of the additional information provided by the inherent state constraints of the landing system, i.e., the flexible deformation constraints, into the design of the navigation filter. To involve such nonlinear inequality constraints, we develop a constrained filtering algorithm with theoretically guaranteed performance. By exploiting the geometrical relationship between the state estimates and the constraint region, we establish the estimation refinement theorem. This theorem presents the sufficient condition for improving estimation accuracy through the inclusion of constraints. Guided by this theorem, we devise a constrained filter where a maximum margin separating hyperplane is optimized to refine the state estimate. Further, we demonstrate that the constrained estimation error is exponentially bounded. At last, we validate the proposed filter through a 433 Eros-based flexible landing simulation.

关键词： Deformation Navigation Information filters Optical filters Estimation error Autonomous robots Surface topography State estimation filtering theory filtering algorithms Autonomous navigation estimation refinement theorem flexible landing inequality constraints stability of nonlinear systems

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Deep Learning-Based Performance Testing for Analog Integrated Circuits

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IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS 2025年第4期33卷 1187-1191页

作者： Cao, Jiawei Guo, Chongtao Wang, Houjun Wang, Zhigang Li, Hao Li, Geoffrey Ye Univ Elect Sci & Technol China Sch Automat Engn Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Shenzhen Inst Adv Study Shenzhen 518110 Guangdong Peoples R China Shenzhen Univ Coll Elect & Informat Engn Guangdong Key Lab Intelligent Informat Proc Shenzhen 518060 Peoples R China Imperial Coll London Dept Elect & Elect Engn London SW7 2AZ England

In this brief, we propose a deep learning-based performance testing framework to minimize the number of required test modules while guaranteeing the accuracy requirement, where a test module corresponds to a combination of one circuit and one stimulus. First, we apply a deep neural network (DNN) to establish the mapping from the response of the circuit under test (CUT) in each module to all specifications to be tested. Then, the required test modules are selected by solving a 0-1 integer programming problem. Finally, the predictions from the selected test modules are combined by a DNN to form the specification estimations. The simulation results validate the proposed approach in terms of testing accuracy and cost.

关键词： Testing Accuracy Costs Training Very large scale integration Integer programming Hardware filtering algorithms Circuit faults Artificial neural networks Analog integrated circuits (ICs) deep learning intelligent method low cost performance testing

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