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signal processing, sensor/Information fusion, and target recognition XXXIII 2024

ISBN: (纸本)9781510674325

The proceedings contain 36 papers. The topics discussed include: statistically efficient estimation of noise variances for a wiener process observed with measurement noise;interfacing topological data analysis (TDA) with AI/ML for multimodal data fusion and automatic target recognition (ATR);sensor fusion with multi-modal ground sensor network for endangered animal protection in large areas;enhancing drone abnormal behavior detection using data fusion techniques and dynamic Bayesian network methods;analytics and models in the era of strategic competition;digital twin meets information fusion: panel summary;value-based sensor and information fusion;and objects recognition in high-scattering conditions at radio/microwave frequency.

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signal processing, sensor/Information fusion, and target recognition XXXII

Signal Processing, Sensor/Information Fusion, and Target Rec...

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signal processing, sensor/Information fusion, and target recognition XXXII 2023

ISBN: (纸本)9781510662100

The proceedings contain 33 papers. The topics discussed include: centralized multi-sensor multi-target data fusion with tracks as measurements;generalizing the unscented Kalman filter for state estimation;estimation-based missile threat detection and evasion maneuver for a low-altitude aircraft;validation volume reduction with tracking in sensor coordinates;a machine learning-based state estimation approach for varying noise distributions;a robust fault detection and identification strategy for aerospace systems;derivation of the sliding innovation information filter for target tracking;topological multimodal sensor data analytics for target recognition and information exploitation in contested environments;machine learning model cards toward model-based system engineering analysis of resource-limited systems;multimodal data fusion using signal/image processing methods for multi-class machine learning;and microtexture region segmentation using matching component analysis applied to eddy current testing data.

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signal processing, sensor/Information fusion, and target recognition XXXI

Signal Processing, Sensor/Information Fusion, and Target Rec...

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signal processing, sensor/Information fusion, and target recognition XXXI 2022

ISBN: (纸本)9781510651203

The proceedings contain 45 papers. The topics discussed include: a merge/split algorithm for multitarget tracking using generalized labeled multi-Bernoulli filters;a square-root formulation of the sliding innovation filter for target tracking;combined particle and smooth innovation filtering for nonlinear estimation;stone soup open source framework for tracking and state estimation: enhancements and applications;missile motion parameter estimation with a passive sensor from a high speed aircraft;ballistic missile tracking in the presence of decoys using space base IR sensors;application of the sliding innovation filter to complex road;and sensor fusion for media manipulation with ambiguous hypotheses, evidence alignment, and a novel belief combination rule.

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signal processing, sensor/Information fusion, and target recognition XXX

Signal Processing, Sensor/Information Fusion, and Target Rec...

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signal processing, sensor/Information fusion, and target recognition XXX 2021

ISBN: (纸本)9781510643499

The proceedings contain 35 papers. The topics discussed include: application of machine learning for drone classification using radars;2d point set registration via stochastic particle flow filter;the application of machine learning to signal processing for the detection and identification of signals of interest and anomalies;adversarial machine learning and adversarial risk analysis in multi-source command and control;risk-based security: from theory to practice;anomaly detection of unstructured big data via semantic analysis and dynamic knowledge graph construction;and learning intent and behavior models from motion trajectories for unsupervised semantic labeling.

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Multimodal Data fusion using signal/Image processing Methods for Multi-Class Machine Learning 32

Multimodal Data Fusion using Signal/Image Processing Methods...

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Conference on signal processing, sensor/Information fusion, and target recognition XXXII

作者： Richards, Casey J. Valliani, Nawal Johnson, Benjamin A. Wong, Nelson Ka Ki Pennati, Angelo Saeed, Amir K. Rodriguez, Benjamin M. Johns Hopkins Univ Whiting Sch Engn 3400 N Charles St Baltimore MD 21218 USA

ISBN: (数字)9781510662117

ISBN: (纸本)9781510662100;9781510662117

As the world progresses further into the digital era, we see a growing utility for combining datasets gathered on different devices and receivers as well as on varying time ranges, for use in machine learning. However, machine learning classification introduces a requirement for standardized data, which in turn hampers the ability to utilize diverse sets of data at a given timestamp. In this paper, we investigate the application of various signal pre-processing techniques (Daubecheis wavelet, discrete cosine and discrete fourier transform among others) for multi-modal, multi-class machine learning. Following the pre-processing, the multi-faceted signals are represented solely by features generated from first order statistics, eigen decomposition, and linear discriminant. Utilizing these generated features, as opposed to the signals themselves, these diverse datasets may now be combined as input to machine learning methods. Furthermore, we apply Fisher's linear discriminant ratio and Random Forest feature importance metrics for feature ranking and feature space reduction followed by a comparison of the approaches. Our work demonstrates that dissimilar datasets with common classes may be combined using the proposed methods with a classification accuracy >= 95%. This paper demonstrates that the feature space may be reduced by approximately 60% with <= 5% loss in classification accuracy, and in some cases, a slight increase in classification accuracy.

关键词： statistical signal processing linear discriminant analysis feature generation feature ranking transforms classification

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Radar-Infrared sensor fusion Based on Hierarchical Features Mining

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IEEE signal processing LETTERS 2024年 31卷 66-70页

作者： Yang, Lihe Feng, Wei Wu, Yaojun Huang, Liang Quan, Yinghui Xidian Univ Sch Elect Engn Dept Remote Sensing Sci & Technol Xian 710077 Peoples R China Xidian Univ Minist Educ Key Lab Collaborat Intelligence Syst Xian 710071 Peoples R China Xian Key Lab Adv Remote Sensing Xian 710071 Peoples R China

High resolution range profile (HRRP) provides abundant target information but is susceptible to external electromagnetic interference. While infrared sensor possesses strong anti-jamming capability, it has limited detection range and is vulnerable to weather conditions, leading to reduced imaging resolution. The integration of radar and infrared sensors can synergize their respective strengths to not only improve the reliability and robustness of the system but also enhance the credibility and accuracy of the data. However, there exist many challenges in the research on the fusion of heterogeneous data like HRRP 1D data and infrared 2D data. In this letter, a radar infrared sensor fusion method based on hierarchical features mining (HFM) is proposed to solve the problems above. The method is applied to multi-target recognition tasks to verify the effectiveness. The results demonstrate that the proposed method can enhance the information completeness of the target and improve the accuracy of target recognition.

关键词： Feature extraction Scattering Radar target recognition Infrared sensors Radar imaging Data mining Feature fusion high resolution range profile (HRRP) infrared images support vector machine (SVM) target recognition

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signal processing, sensor/Information fusion, and target recognition XXIX

Signal Processing, Sensor/Information Fusion, and Target Rec...

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signal processing, sensor/Information fusion, and target recognition XXIX 2020

ISBN: (纸本)9781510636231

The proceedings contain 29 papers. The topics discussed include: the application of machine learning and artificial neural networks to RF signal processing for the detection and identification of signals of interest and environmental anomalies;neuro-fuzzy logic for parts-based reasoning about complex scenes in remotely sensed data;decentralized formation shape control of UAV swarm using dynamic programming;improvement of moving object detection accuracy on aerial imagery using sensor geometry;data fusion methods for materials awareness;identification of local features on a group of images obtained in different electromagnetic ranges;contraction monitor for high risk pregnancies;and demosaicing images in low lighting environments.

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Arithmetic Average Density fusion-Part iv: Distributed Heterogeneous fusion of RFS and LRFS Filters via Variational Approximation

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IEEE TRANSACTIONS ON signal processing 2025年 73卷 1454-1469页

作者： Li, Tiancheng Liang, Haozhe Li, Guchong Herrero, Jesus Garcia Pan, Quan Northwestern Polytech Univ Sch Automat Key Lab Informat Fus Technol Minist Educ Xian 710129 Peoples R China Univ Carlos III Madrid Grp GIAA Madrid 28270 Spain

This paper is the fourth part of a series of papers on the arithmetic average (AA) density fusion approach and its application for target tracking. In this paper, we address the intricate challenge of distributed heterogeneous multisensor multitarget tracking, where each inter-connected sensor operates a probability hypothesis density (PHD) filter, a multiple Bernoulli (MB) filter or a labeled MB (LMB) filter and they cooperate with each other via information fusion. Our recent work has proven that the existing linear fusion of these filters is all exactly built on averaging their respective unlabeled/labeled PHDs. Based on this finding, two PHD-AA fusion approaches are proposed via variational minimization of the upper bound of the Kullback-Leibler divergence between the local and multi-filter averaged PHDs subject to cardinality consensus based on the Gaussian mixture implementation, enabling heterogeneous filter cooperation. One focuses solely on fitting the weights of the local Gaussian components (L-GCs), while the other simultaneously fits all the parameters of the L-GCs at each sensor, both seeking average consensus on the unlabeled PHD, irrespective of the specific posterior form of the local filters. For the distributed peer-to-peer communication, both the classic consensus and flooding paradigms have been investigated. Simulations have demonstrated the effectiveness and flexibility of the proposed approaches in both homogeneous and heterogeneous scenarios.

关键词： Radio frequency Filters target tracking Time measurement Arithmetic Robustness Peer-to-peer computing Fuses Clutter Training Random finite set arithmetic average fusion distributed tracking heterogeneous fusion, multitarget tracking variational approximation

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Personalized models for human activity recognition with wearable sensors: deep neural networks and signal processing

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APPLIED INTELLIGENCE 2023年第5期53卷 6041-6061页

作者： Gholamiangonabadi, Davoud Grolinger, Katarina Western Univ Dept Elect & Comp Engn London ON N6A 5B9 Canada

Human Activity recognition (HAR) has been attracting research attention because of its importance in applications such as health monitoring, assisted living, and active living. In recent years, deep learning, specifically Convolutional Neural Networks (CNNs), have been achieving great results due to their ability to extract features and model complex actions. These generic models work great for the subjects on which they were trained, but their performance degrades substantially for new subjects. Consequently, this paper proposes a personalized HAR model based on CNN and signal decomposition. First, features are extracted from multi-modal sensor data with signal processing techniques, including Stationary Wavelet Transform, Empirical Mode Decomposition (EMD), and Ensemble EMD. Next, CNN carries out the information fusion and the final classification. Personalization is achieved by using a few seconds of the target subject data to select the version of the trained CNN best suited for the target subject. Results show that EMD with cubic spline achieves better accuracy than other signal processing techniques. Moreover, the proposed approach, irrelevant of the type of signal processing, outperforms the state-of-the-art CNN approaches with time-domain features.

关键词： Human activity recognition Personalized models Deep convolutional neural networks Wearable sensors signal processing sensor fusion

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Interfacing topological data analysis (TDA) with AI/ML for multimodal data fusion and automatic target recognition (ATR) 33

Interfacing topological data analysis (TDA) with AI/ML for m...

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Conference on signal processing, sensor/Information fusion, and target recognition XXXIII

作者： Schrader, Paul T. Informat Directorate AFRL Air Force Res Lab RIGC 525 Brooks Rd Rome NY 13441 USA

ISBN: (纸本)9781510674332;9781510674325

DoD agencies produces a deluge of heterogeneous data from arrays of multimodal sensor sources. Ideally, these collects contribute to global and local situational awareness supporting decision speed. The effective management, orchestration, and interpretation of this data, within ever-increasing adversarial deception capabilities, for (near) real-time actionable processes has obscured resulting in imminent costs and loss of life. A key factor contributing to these mission needs include the lack of exploitation over degree of freedom spaces that upstream multimodal sensor data and their fused manifolds possess. Within these structures are rich, alternative sources of mathematically rigorous organization and data fusion techniques where a paradigm shift in local or global SA could be instantiated. This research expands upon and validates a TDA AI/ML network design (U.S. Patent Pending No. 63/499,338) presented in the 2023 SPIE DCS conference. Modified custom approaches, involving the data fusion of its three modalities (specifically acoustic, electro-optical, and infrared) and testing results for predictive automatic target recognition are presented along with several mathematical generalizations and clustering capacities.

关键词： topological data analysis (TDA) artificial intelligence/machine learning (AI/ML) multimodal sensor data fusion autonomous target recognition (ATR) sheaf theory

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