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检索条件"主题词=Filtering algorithms"

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A Two-Stage Multispectral Demosaicking Method Using Neville Filters and Directional Interpolation

A Two-Stage Multispectral Demosaicking Method Using Neville ...

引用

Signal, Information and Data Processing (ICSIDP), IEEE International Conference on

作者： Wenzhuo Yang Yakun Liu Yan Shi Wei Li Ran Tao School of Information and Electronics Beijing Institute of Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Snapshot multispectral imaging systems typically capture multispectral images in a single shot by covering the sensor with a multispectral filter array (MSFA). A demosaicking algorithm is generally required for such systems to reconstruct the full-resolution multispectral images. A two-stage demosaicking method is proposed in this paper. In the first stage, the image is progressively interpolated by Neville filters. In the second stage, the directional interpolation with a novel weighting function is performed to enhance the quality of the pre-interpolated image. Experimental results demonstrate that the proposed approach achieves superior performance in both subjective and objective assessments.

关键词： Interpolation Multispectral imaging Array signal processing filtering algorithms Information filters Data processing Image reconstruction Sensor arrays

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PMB filter based distributed tracking of multiple extended targets under different resolutions

PMB filter based distributed tracking of multiple extended t...

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IEEE Conference on Vehicular Technology (VTC)

作者： Yue Li Lin Gao Ping Wei Wanchun Li Huaguo Zhang Hao Mu School of Information and Communication Engineering University of Electronic Science and Technology of China National Key Laboratory of Electromagnetic Space Security

ISBN: (数字)9798331517786

ISBN: (纸本)9798331517793

A key feature of extended target (ET) is that it can produce multiple measurements, thus providing detailed information such as size, orientation. However, due to the measurement number decrease, the ET tends to be a point target (PT) when it becomes far from the sensor, leading to disability of estimating the extensions. In this paper we consider the problem of distributed ETs tracking based on multiple sensors. In such a case the measurements decrease of an ET can be compensated by other sensors, so that the tracking performance can be maintained. In the proposed algorithm the targets are modeled by Poisson multi-Bernoulli (PMB) random finite set (RFS) and the state of each target consists of two parts representing the possibility of being ET and PT, respectively. In the local filtering state, interaction between ET and PT states is considered in the prediction step for target identity change between ET and PT, based on which the local PMB filter is achieved for seamlessly tracking ETs and PTs. In the fusion part a generalized covariance intersection (GCI) based criterion is proposed to fuse the posteriors of each sensor. The performance of proposed algorithm is verified via simulations.

关键词： Radio frequency Vehicular and wireless technologies Target tracking Fuses Switches filtering algorithms Prediction algorithms Information filters Size measurement Covariance matrices

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Detecting Ghost Aircraft Flooding in the Surveillance of Low-Flying Civil and Military Aircraft

Detecting Ghost Aircraft Flooding in the Surveillance of Low...

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Computer, Control and Robotics (ICCCR), International Conference on

作者： Pirolley Melvyn Raphaël Couturier Fabrice Ambert Michel Salomon Univerśite de Franche-Comte FEMTO-ST Institute CNRS Belfort France Université de Franche-Comté FEMTO-ST Institute CNRS Belfort France

ISBN: (数字)9798350373141

ISBN: (纸本)9798350373158

In the past few years, the increasing number of aircraft in low-altitude traffic has brought new challenges for air traffic controllers. As the ADS-B protocol takes an important part in the air surveillance task, it is important to be prepared to face attacks related to it. Moreover, as the ADS-B is open and uses broadcasts, it is easy for an attacker to emit fake ADS-B data to flood the air traffic controller's screens. In this work, we will present two algorithms, the first one is based on hashing techniques, invariant to geometric transformations to detect trajectory replays. The second one uses a deep-learning model to detect irrelevant ghost trajectories. Our proposed approach can automatically filter a majority of ghost aircraft without risking to filter real aircraft.

关键词： Hash functions Atmospheric modeling Surveillance filtering algorithms Military aircraft Trajectory Floods

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Probabilistic 3D Multi-Object Cooperative Tracking for Autonomous Driving via Differentiable Multi-Sensor Kalman Filter

Probabilistic 3D Multi-Object Cooperative Tracking for Auton...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Hsu-Kuang Chiu Chien-Yi Wang Min-Hung Chen Stephen F. Smith Robotics Institute of Carnegie Mellon University NVIDIA Research

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Current state-of-the-art autonomous driving vehicles mainly rely on each individual sensor system to perform perception tasks. Such a framework’s reliability could be limited by occlusion or sensor failure. To address this issue, more recent research proposes using vehicle-to-vehicle (V2V) communication to share perception information with others. However, most relevant works focus only on cooperative detection and leave cooperative tracking an underexplored research field. A few recent datasets, such as V2V4Real, provide 3D multi-object cooperative tracking benchmarks. However, their proposed methods mainly use cooperative detection results as input to a standard single-sensor Kalman Filter-based tracking algorithm. In their approach, the measurement uncertainty of different sensors from different connected autonomous vehicles (CAVs) may not be properly estimated to utilize the theoretical optimality property of Kalman Filter-based tracking algorithms. In this paper, we propose a novel 3D multi-object cooperative tracking algorithm for autonomous driving via a differentiable multi-sensor Kalman Filter. Our algorithm learns to estimate measurement uncertainty for each detection that can better utilize the theoretical property of Kalman Filter-based tracking methods. The experiment results show that our algorithm improves the tracking accuracy by 17% with only 0.037x communication costs compared with the state-of-the-art method in V2V4Real. Our code and videos are available at the URL and the URL.

关键词： Uniform resource locators Training Three-dimensional displays Measurement uncertainty filtering algorithms Probabilistic logic Feature extraction

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Decentralized Multi-Agent Active Search and Tracking when Targets Outnumber Agents

Decentralized Multi-Agent Active Search and Tracking when Ta...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Arundhati Banerjee Jeff Schneider School of Computer Science Carnegie Mellon University Pittsburgh USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Multi-agent multi-target tracking has a wide range of applications, including wildlife patrolling, security surveillance or environment monitoring. Such algorithms often make restrictive assumptions: the number of targets and/or their initial locations may be assumed known, or agents may be pre-assigned to monitor disjoint partitions of the environment, reducing the burden of exploration. This also limits applicability when there are fewer agents than targets, since agents are unable to continuously follow the targets in their fields of view. Multi-agent tracking algorithms additionally assume inter-agent synchronization of observations, or the presence of a central controller to coordinate joint actions. Instead, we focus on the setting of decentralized multi-agent, multi-target, simultaneous active search-and-tracking with asynchronous interagent communication. Our proposed algorithm DecSTER uses a sequential monte carlo implementation of the Probability Hypothesis Density filter for posterior inference combined with Thompson sampling for decentralized multi-agent decision making. We compare different action selection policies, focusing on scenarios where targets outnumber agents. In simulation, we demonstrate that DecSTER is robust to unreliable inter-agent communication and outperforms information-greedy baselines in terms of the Optimal Sub-Pattern Assignment (OSPA) metric for different numbers of targets and varying teamsizes.

关键词： Target tracking Uncertainty Surveillance Robot kinematics Wildlife Object detection filtering algorithms

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Facial Detection Algorithm based on Improved YOLOv5s

Facial Detection Algorithm based on Improved YOLOv5s

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IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC)

作者： Lin Meng Wenbo Yuan Meng Li Bingjie Zhang Lingmin Zhang School of Electrical Engineering University of Jinan Shandong China Shandong Jinruan Science and Technology Co .Ltd. Shandong China

ISBN: (数字)9798350316537

ISBN: (纸本)9798350316544

To address the challenges of low detection accuracy and the ease of misdetection and omissions due to small face targets or masked faces in complex environments, this paper proposes a face detection algorithm based on an improved YOLOv5s. Initially, the EIoU bounding box loss function is introduced to enhance the regression speed and localization accuracy of the target bounding box. Next, the CBAM attention mechanism module is incorporated to augment the YOLOv5s network’s capability in extracting image features without substantially increasing the model’s parameters. Finally, the post-processing NMS algorithm is enhanced to optimize the redundant bounding box rejection method and filter out high-quality detection results. Experiments on face detection are carried out using the YOLO_Mask dataset, resulting in an enhanced Precision by 2.2%, mAP@0.5 by 2.6%, and mAP@0.5:0.95 by 4.2%. The experiments demonstrate that the improved face detection algorithm notably enhances the detection of small and occluded targets, validating the effectiveness of this improved method in addressing the issues of low detection accuracy for small and occluded targets, as well as misdetection and missed detections.

关键词： YOLO Location awareness Accuracy Semantics Production filtering algorithms Feature extraction

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A Comparative Study of Bernoulli Gaussian-max Filter and Bernoulli Gaussian-sum Filter

A Comparative Study of Bernoulli Gaussian-max Filter and Ber...

引用

International Conference on Control, Automation and Information Sciences (ICCAIS)

作者： Zhijin Chen Branko Ristic Du Yong Kim School of Engineering RMIT University Melbourne Australia

ISBN: (数字)9798331542047

ISBN: (纸本)9798331542054

As a class of exact Bayesian filtering algorithms for non-linear/non-Gaussian recursive estimation of dynamic stochastic systems which can randomly switch on and off, the Bernoulli filter has been extensively studied and applied in target tracking and other dynamic phenomena. In general, there is no analytic solution for the Bernoulli filter, and it is implemented in two ways: Monte Carlo approximation and Gaussian-sum filter (GSF) based on Gaussian-sum models. GSF can be given in the analytic form and has the computational advantage. The possibility Bernoulli filter, which is based on Uncertain Finite Set (UFS) instead of Random Finite Sets (RFS) and implemented as an analogue of standard Bernoulli filter, has been introduced to address the epistemic uncertainties arising from imprecise or partial knowledge of models and/or filtering parameters. Similarly, the Bernoulli Gaussian-max filter (GMF) is formulated as an analogue of the Bernoulli GSF in the framework of possibility theory, rather than the framework of probability theory, with an objective to achieve enhanced ro-bustness. The performance of the Bernoulli GMF and Bernoulli GSF is evaluated and compared through simulation tests in a single target tracking application.

关键词： Target tracking Possibility theory Upper bound Heuristic algorithms Computational modeling Noise filtering algorithms Probabilistic logic filtering theory Noise measurement

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Auto Detection Hard Exudates by Using VistaView Devise Based on Hybrid Algorithm

Auto Detection Hard Exudates by Using VistaView Devise Based...

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Advances in Engineering Science and Technology (AEST), International Conference on

作者： Suhair Hussein Talib Osama Qasim Jumah Al-Thahab Department of Electrical Engineering College of Engineering University of Babylon Baghdad Iraq

ISBN: (数字)9798331502690

ISBN: (纸本)9798331502706

Untreated diabetic retinopathy (DR) can lead to various levels of visual impairment and potentially complete loss of eyesight. Annual eye examinations are crucial in identifying potential visual impairments, especially for individuals with diabetes, to prevent early-stage vision deterioration. Technological advancements have made retinal imaging technologies for cell phones commercially available, enabling cost-effective and efficient screening for diabetic retinopathy in various settings. This study examines multiple approaches to handling retinal images in ophthalmology to identify and assess abnormalities. Fundus cameras are expensive and heavy, making compact, portable, and reasonable retinal imaging technologies for quick DR screening necessary. The Vistaview retinal imaging system, with its superior image quality and extensive field of view, is examined for DR screening during a general health exam. The effectiveness of MRLS, a multilevel rhombus segmentation method, is investigated for identifying lesions in retinal images caused by conditions like diabetic retinopathy. This work is a significant step towards developing reliable and effective strategies for diagnosing and treating retinal disorders promptly.

关键词： Image quality Diabetic retinopathy Image segmentation Cellular phones Visual impairment filtering algorithms Retina Ophthalmology Reliability Lesions

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Dynamic Segmentation Enhanced Asynchronous Non-linear Federated Learning for Fault Diagnosis Using Incremental Aggregation Mechanism

Dynamic Segmentation Enhanced Asynchronous Non-linear Federa...

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Smart World Congress (SWC), IEEE

作者： Luo Fang Jiafu Wan Hao Tang Uzair Aslam Bhatti Jinbiao Tan School of Mechanical & Automotive Engineering South China University of Technology Guangzhou China School of Information and Communication Engineering Hainan University Haikou China

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

To address the challenges of nonlinearity in edge raw data and the absence of real-time feedback in distributed heterogeneous fault diagnosis, we propose a dynamic segmentation enhanced asynchronous federated learning method (FedDSA) utilizing an incremental aggregation mechanism. This methodology incorporates a meta-learning-driven strategy during the segmentation of edge groups (EGs), facilitating dynamic self-organized segmentation across multiple EGs. During the prototype aggregation among EGs, a particle filtering algorithm is integrated to accommodate nonlinear data, thereby enabling asynchronous global prototype aggregation and constructing an adaptive asynchronous incremental aggregation mechanism. Ultimately, a hybrid asynchronous federated learning framework based on segmentation across multiple EGs is established. Within the framework, a global prototype generator (GPG) is trained in the cloud, leveraging embedded fault feature prototype representations. Finally, a federated fault diagnosis platform is designed and implemented, with its effectiveness validated through extensive experimental evaluations.

关键词： Fault diagnosis Metalearning Federated learning Heuristic algorithms Scalability Prototypes filtering algorithms Real-time systems Particle filters Generators

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Marine Platform Motion Measurement Technology Based on Micro-Inertial Sensors

Marine Platform Motion Measurement Technology Based on Micro...

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Artificial Intelligence, Computer Technology and Control Engineering (ACTCE), International Seminar on

作者： WangYang Zhao AnLuo Guo YuHao Liu XueZhi Wang YuanMing Wang The Navy Force Representative Office in Huludao Area Huludao China CSSC South China Marine Machinery Co. LTD Wuzhou China

ISBN: (数字)9798331521714

ISBN: (纸本)9798331521721

With the deepening of ocean development, precise measurement of marine platform motion is crucial for safe and efficient operations. To improve the measurement accuracy and stability of marine operation platforms, a technology based on micro-inertial sensors for marine platform motion measurement has been developed. This includes the creation of MEMS (Micro-Elector-Mechanical Systems) measurement benchmark equipment for marine platforms, which enables real-time acquisition of marine platform motion data. Characterized by miniaturization, low cost, low power consumption, and high accuracy, this technology provides a reliable solution for safe operations on marine platforms. In the measurement of marine platform motion, a data online analysis method for wave frequency domain self-learning and real-time sea condition monitoring is designed to address the changing frequency conditions of sea waves. By real-time adjustment of filter parameters to adapt to changes in sea conditions and the design of precise compensation algorithms for the phase and amplitude of frequency signals, adaptive compensation for phase and amplitude errors is achieved, effectively solving the displacement estimation accuracy issues under long-period heave motions.

关键词： Condition monitoring Seminars Accuracy Frequency-domain analysis Sea measurements filtering algorithms Sensor phenomena and characterization Real-time systems Stability analysis Motion measurement

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