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A Safety-Oriented Motion Cueing Algorithm for a Serial Robotic Flight Simulator Using a Predictive Neural Network Reference Governor

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第11期25卷 15718-15731页

作者： Matheus, Aline da C. de Oliveira, Wesley R. Villani, Emilia Embraer BR-12227901 Sao Jose Dos Campos Brazil Aeronaut Inst Technol ITA Competence Ctr Mfg CCM Div Mech Engn IEM BR-12228900 Sao Jose Dos Campos Brazil

High fidelity flight simulators use motion platforms to reproduce the feeling of motion from a real flight. While most of the published works for both aircraft and vehicle simulators are related to parallel motion platforms, this work approaches the problem of designing the motion cueing algorithm of a flight simulator based on a serial manipulator. The simulator presents a large cockpit with an embedded visual system and dimensions that resemble those of an aircraft flight deck. Motion cueing in this context should be able to minimize false cues while ensuring safe operation, coping not only with the dynamic and kinematic constraints of the robot but also avoiding crash events that might happen between the cockpit and the serial arm. While there have been several contributions regarding classical filtering, tuning optimization, and model-based predictive control approaches to cope with constraints of parallel platforms, they result in the inefficient utilization of the robot workspace or even the inability to handle collisions of the cockpit with the robot. This work presents a novel motion cueing algorithm for a serial robotic flight simulator, which focuses on ensuring safety regarding the physical boundaries of the cockpit while enhancing motion fidelity. The approach is based on a hybrid model-based predictor that uses a neural network to infer workspace collisions in real-time (including crash events of the cockpit with the robotic arm), releasing a non-linear deterministic control action that acts as a feedforward reference governor. Simulation and experimental results evince improved workspace usage while ensuring safe operation.

关键词： Robots Collision avoidance Prediction algorithms Manipulators Robot sensing systems filtering algorithms Arms Motion cueing algorithm serial robotic flight simulator neural networks collision-avoidance

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GPU Accelerated Trilateral Filter for MR Image Restoration

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IEEE ACCESS 2025年 13卷 63013-63028页

作者： Sriram, Suthir Vijayaraj, Nivethitha Srilekha, T. Praveena, M. Murugan, Thangavel Amrita Vishwa Vidyapeetham Amrita Sch Comp Dept Comp Sci & Engn Chennai 601103 India Vaagdevi Engn Coll Warangal 506005 Telangana India United Arab Emirates Univ Coll Informat & Technol Al Ain Abu Dhabi U Arab Emirates

Medical image processing demands essential image restoration techniques to handle both blurred and noisy images. The image capture process frequently causes these degradations. The restoration of medical images such as MRI scans holds essential value for better diagnosis and improved treatment accuracy. MRI serves as a non-invasive imaging tool to diagnose brain diseases yet the noise produced during its acquisition phase degrades image quality so that diagnosis accuracy suffers. Reduction of noise becomes essential for both clinical diagnostic procedures and computer-assisted medical analysis practices which require tissue classification and registration and segmentation algorithms. Detecting and eliminating noise from magnetic resonance images represents a complex medical imaging challenge. Multiple noise filtering methods operate on MR images with distinct performance benefits. This work presents a trilateral filter specifically designed for image restoration which provides both precision and speed performance. The bilateral filter served as foundation to develop this filter while adding another intensity comparison function to deal with brain MR image noise. A weighting control function based on intensity entropy is established for intensity-based weight calculations. The implementation of GPU parallel computing techniques together with optimization of memory and threads leads to faster computation. A combination of texture-based image analysis with advanced computational algorithms powers the automated filtration process. The approach uses forward selection to identify 98 texture attributes while refining the selection process to find optimal regularity features. A two-phase classification system trains automation parameters using artificial neural networks together with support vector machines. Research findings show that trilateral filtering yields superior noise reduction alongside better definition of MR image features than alternative techniques.

关键词： Noise Magnetic resonance imaging Noise reduction Rician channels filtering algorithms Information filters Magnetic separation Image restoration Graphics processing units Three-dimensional displays Artificial neural networks automated systems image enhancement graphics processing unit magnetic resonance imaging support vector machine texture descriptor trilateral 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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Matching Vector filtering Methods For Sea Ice Motion Detection Using SAR Imagery Feature Tracking

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2022年 15卷 6197-6202页

作者： Li, Chaoyue Li, Gang Chen, Zhuoqi Wang, Xue Cheng, Xiao Sun Yat Sen Univ Sch Geospatial Engn & Sci Guangzhou 510275 Peoples R China Southern Marine Sci & Engn Lab Zhuhai 519082 Peoples R China

Applying feature tracking techniques to synthetic aperture radar (SAR) imagery generates high-resolution sea ice motion fields. However, the bad matching vectors still exist after the Nearest Neighbor Distance Ratio test and contaminate the derived motion fields, which need to be identified and filtered out. In this article, we propose two algorithms to eliminate such wrong matching vectors. The first employs the matching results derived by the maximum cross-correlation (MCC) method as the reference motion fields to evaluate such wrong matches. The second method employs the local spatial consistency presumption of sea ice motion fields. A Voronoi diagram is applied to slice the overlapping area of two SAR images into many fractions, and each fraction extends its size 50% outward to calculate the regional mean sea ice flow vector and standard deviation. Any vector within the fraction that exceeds 3 times the regional standard deviation will be recognized as an outlier and filtered out. Two methods are tested to two cases with strong rotation or irregular sea ice motion fields derived from Sentinel-1 imagery. The overall accuracy of our two methods is 93.9% and 98.7%, and they sacrifice 6.12% /1.22% of the correct vectors to filter out 100.0% / 94.12% of the wrong vectors for the MCC referenced filter and Voronoi fragmented filter, respectively.

关键词： Matched filters Sea ice filtering algorithms Tracking Information filters Radar polarimetry Feature extraction Feature tracking image matching sea ice motion synthetic aperture radar (SAR)

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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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RCS Information Aided Poisson Multi-Bernoulli Mixture Filter in Clutter Background

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IEEE SENSORS JOURNAL 2024年第4期24卷 5039-5052页

作者： Bai, Mengdi Zhang, Qilei Yu, Ruofeng Zhang, Yongsheng Sun, Bin Natl Univ Def Technol NUDT Coll Elect Sci & Technol Changsha 410073 Peoples R China Beijing Inst Tracking & Telecommun Technol Beijing 100094 Peoples R China

For modern radar systems, the measurement of the target's radar cross section (RCS) is a standard output besides kinematic measurements. It is straightforward to incorporate RCS information into tracking algorithms for performance improvements in more realistic and difficult scenarios. However, in practice, the proper integration of RCS recursion and Bayesian filter is promising but challenging. To address this issue, an RCS information aided Poisson multi-Bernoulli mixture (RCSI-PMBM) filter in clutter background is proposed in this article. First, the Bayesian RCS estimation strategy is presented, and then, the RCSI-PMBM filter is analytically developed. Moreover, based on the Gamma Gaussian mixture (GGM) form, an effective and efficient implementation of the proposed RCSI-PMBM filter is developed. Finally, the validity of the proposed algorithm is verified by simulation tests with challenging scenarios.

关键词： Information filters Target tracking filtering algorithms Signal to noise ratio Radio frequency Radar tracking Estimation Gamma Gaussian mixture (GGM) multitarget tracking (MTT) Poisson multi-Bernoulli mixture (PMBM) radar cross section (RCS) information

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On Semi-Blind Source Separation-Based Approaches to Nonlinear Echo Cancellation Based on Bilinear Alternating Optimization

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2024年 32卷 2973-2987页

作者： Wang, Xianrui Yang, Yichen Brendel, Andreas Ueda, Tetsuya Makino, Shoji Benesty, Jacob Kellermann, Walter Chen, Jingdong Northwestern Polytech Univ Ctr Intelligent Acoust & Immers Commun Xian 710072 Shaanxi Peoples R China Waseda Univ Grad Sch Informat Prod & Syst Kitakyushu Fukuoka 8080135 Japan Fraunhofer Inst Integrated Circuits IIS D-91058 Erlangen Germany Univ Quebec INRS EMT Montreal PQ H5A 1K6 Canada Univ Erlangen Nurnberg Chair Multimedia Commun & Signal Proc D-91058 Erlangen Germany

Acoustic echo cancellation (AEC) is a crucial task in full duplex communications. As conventional linear filtering approaches are ineffective to deal with double-talk, various semi-blind source separation (SBSS)-based AEC algorithms are deceived, most of which are formulated and implemented in the frequency domain based on the multiplicative transfer function (MTF) model for computational efficiency. To avoid large latency and in order to deal with loudspeaker nonlinearities, the convolutive transfer function (CTF) model and odd power series expansion are leveraged, which are employed by numerous SBSS-based nonlinear AEC (SBSS-NAEC) algorithms. Conventional SBSS-NAEC methods estimate the series expansion coefficients and the CTF filter simultaneously making the number of free parameters to estimate large. Hence, the corresponding algorithms are computationally expensive and are difficult to optimize. In this work, we propose to decouple the series expansion coefficients and the CTF filters into a bilinear form and present a bilinear alternating optimization framework for estimating the model parameters. An alternating iterative projection (AIP) algorithm and an alternating element-wise iterative source steering (AEISS) algorithm are proposed. As the bilinear representation consists of less parameters compared to the conventional methods, the proposed algorithms not only improve the AEC performance but also reduce the computational complexity, which is validated by comprehensive simulations and experiments.

关键词： filtering algorithms Computational modeling Loudspeakers Speech processing Transfer functions IP networks Convolution Semi-blind source separation nonlinear acoustic echo cancellation odd power series expansion convolutive transfer function model bilinear alternating optimization

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Blind Quality Evaluator for Enhanced Colonoscopy Images by Integrating Local and Global Statistical Features

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2024年 73卷 1页

作者： Wang, Yuhe Cui, Yueli Lin, Junhao Jiang, Gangyi Yu, Mei Fang, Chenli Zhang, Shiqing Taizhou Univ Sch Elect & Informat Engn Taizhou 318000 Zhejiang Peoples R China Ningbo Univ Fac Informat Sci & Engn Ningbo 315211 Peoples R China

In colonoscopy examinations, the dark imaging environment of the intestine may result in low-quality scene acquisition and pose challenges for subsequent disease diagnosis. Despite the development of numerous low-light image enhancement (LIE) methods to improve the quality of enhanced colonoscopy images (ECIs), various artifacts such as abnormal brightness and low contrast are still induced. To fairly compare various LIE algorithms and accurately measure the visual quality of ECIs, we develop a novel blind quality evaluator for ECIs by integrating local and global statistical features. For global features, we first extract multiscale gradient similarity features in the multiscale space domain and multi-resolution information entropy features in the discrete cosine transform (DCT) domain to capture their sharpness degradation. Second, we utilize asymmetric generalized Gaussian distribution (AGGD) models to extract fitting parameters in color-opponent components to measure color distortion in ECIs. In addition, a set of intermediate ECIs are generated by adjusting the brightness information, from which perceptual features are computed to capture global brightness variations. For local features, we utilize local binary pattern (LBP) descriptors in the gradient domain and combine them with contrast energy features and local color-aware features to capture local quality degradation. Finally, by integrating both local and global perceptual features, a regression function is employed to build a connection between quality-aware features and subjective ratings. Extensive experiments conducted on publicly available ECIQAD dataset demonstrate that our method outperforms current mainstream blind quality metrics.

关键词： Feature extraction Measurement Distortion Brightness Image color analysis Colonoscopy Low-pass filters Degradation Visualization filtering algorithms Enhanced colonoscopy image (ECI) global and local perspectives statistical features visual quality evaluator

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