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Radar-Infrared sensor fusion Based on Hierarchical Features Mining

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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Value-Based sensor and information fusion 33

Value-Based Sensor and Information Fusion

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conference on signal processing, sensor/information fusion, and target recognition XXXIII

作者： Blasch, Erik Air Force Res Lab Arlington VA 22203 USA

ISBN: (纸本)9781510674332;9781510674325

There are many examples, methods, and processes showing the importance of sensor, data, and information fusion. However, there is a need to determine the value added of information fusion in the context of data (e.g., multimodal, amount, and resolution), software (e.g., artificial intelligence/machine learning), hardware (e.g., size, weight, and performance), as well as architectures (e.g., cloud, fog, and edge computing). This paper utilizes the analytical hierarchy process (AHP) to determine the pairwise performance needs among different human-machine information fusion system tradeoffs to show the value added from sensor fusion. The paper examines the potential value added by coordinating deep, active, and reinforcement learning for information fusion systems. Among the information metrics, the combined methods of artificial intelligence learning methods highlight user requirements for accuracy, confidence, and timeliness.

关键词： Data fusion information Group Model information fusion Active Learning Deep Learning Reinforcement Learning

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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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Asynchronous sensor fusion for multiple object tag-less activity tracking in manufacturing 33

Asynchronous sensor fusion for multiple object tag-less acti...

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conference on signal processing, sensor/information fusion, and target recognition XXXIII

作者： Yang, Kaipei Pezeshk, Aria Lehman, Charles Arbabian, Amin Plato Syst 360 Pine St Floor 6 San Francisco CA 94104 USA

ISBN: (纸本)9781510674332;9781510674325

Centralized fusion ensures minimal information loss and maximizes the effectiveness of state estimation. Statistically, it is the optimal solution for all sensor fusion configurations. In this paper, we introduce a local-sensor-driven asynchronous low-level centralized fusion methodology that seamlessly integrates radar and camera data at the level of detections from each sensor. For a local-sensor-driven asynchronous system, detections from the two sensing modalities with different sampling rates are transmitted to a centralized filter, which is updated whenever it receives a measurement. We implemented the proposed algorithm and validated the results using real data from manufacturing and industrial work sites. The data was obtained by Plato System's Argus perception system, which combines high-resolution imaging mm-wave radar with camera sensors to provide indoor and outdoor activity tracking. We further compare the fusion results with vision-only MOT, as well as track-level fusion (track-to-track fusion).

关键词： Centralized fusion Multiple Object Tracking sensor fusion Manufacturing Activity Tracking

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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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EO and radar fusion for fine-grained target classification with a strong few-shot learning baseline 33

EO and radar fusion for fine-grained target classification w...

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conference on signal processing, sensor/information fusion, and target recognition XXXIII

作者： Ballan, Luca Melo, Jorge G. O. van den Broek, Sebastiaan P. Baan, Jan Heslinga, Friso G. Huizinga, Wyke Dijk, Judith Dilo, Arta TNO Intelligent Imaging Oude Waalsdorperweg 63 NL-2597AK The Hague Netherlands

ISBN: (纸本)9781510674332;9781510674325

Combining data from multiple sensors to improve the overall robustness and reliability of a classification system has become crucial in many applications, from military surveillance and decision support, to autonomous driving, robotics, and medical imaging. This so-called sensor fusion is especially interesting for fine-grained target classification, in which very specific sub-categories (e.g. ship types) need to be distinguished, a task that can be challenging with data from a single modality. Typical modalities are electro-optical (EO) image sensors, that can provide rich visual details of an object of interest, and radar, that can yield additional spatial information. Defined by the approach used to combine data from these sensors, several fusion techniques exist. For example, late fusion can merge class probabilities outputted by separate processing pipelines dedicated to each of the individual sensor data. In particular, deep learning (DL) has been widely leveraged for EO image analysis, but typically requires a lot of data to adapt to the nuances of a fine-grained classification task. Recent advances in DL on foundation models have shown a high potential when dealing with in-domain data scarcity, especially in combination with few-shot learning. This paper presents a framework to effectively combine EO and radar sensor data, and shows how this method outperforms stand-alone single sensor methods for fine- grained target classification. We adopt a strong few-shot image classification baseline based on foundation models, which robustly handles the lack of in-domain data and exploits rich visual features. In addition, we investigate a weighted and a Bayesian fusion approach to combine target class probabilities outputted by the image classification model and radar kinematic features. Experiments with data acquired in a measurement campaign at the port of Rotterdam show that our fusion method improves on the classification performance of individual modalities.

关键词： deep learning sensor fusion image classification few-shot learning foundation models EO and radar decision support

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Diver detection and tracking with different beamforming algorithms 32

Diver detection and tracking with different beamforming algo...

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conference on signal processing, sensor/information fusion, and target recognition XXXII

作者： Sevimli, Rasim A. Baskent Univ Dept Elect Elect Engn Ankara Turkiye

ISBN: (数字)9781510662117

ISBN: (纸本)9781510662100;9781510662117

Diver Detection Sonars (DDS) aim to detect the diver and tracking at the specific distance. At the side of signal processing case, there are bunch of beamforming algorithms to localize the target or diver in our case in the literature. In this paper, some beamforming algorithms are combined and compared via PSNR, time each other. Some algorithms show that the effect of sidelobes and reverberation are clearly decreased. Moreover, detection and tracking algorithms are applied to artificial sonar data created with a specific scenario for this purpose.

关键词： Diver Detection Sonars beamforming algorithms Sonar signal processing

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Sliding mode and variable structure filters for signal processing: a literature review 33

Sliding mode and variable structure filters for signal proce...

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conference on signal processing, sensor/information fusion, and target recognition XXXIII

作者： Obaideen, Khaled Hilal, Waleed Gadsden, S. Andrew AlShabi, Mohammad Univ Sharjah Sharjah U Arab Emirates McMaster Univ Hamilton ON Canada

ISBN: (纸本)9781510674332;9781510674325

This paper will conduct an exhaustive analysis of sliding mode and variable structure filters, essential methodologies employed in signal processing that solve problems noisy and volatile environments induces. This study brings into focus the very foundations and practicality of interference filters by professional exploration of the theoretical basis, the extent of practical implementation, and the present research frontiers. It also emphasizes the critical role these filters play in making signal processing system more precise, adaptable, and resilient. The Sliding mode and the variable structure filters are optimal in the field of nonlinear problems that are augmented by interferences. These are what their purpose in noise reduction, error minimization and the system stability is. Review encompasses a wide range of applications, from robotics, aerospace to telecommunication with the aim to emphasis the variety and successfully of the filtering techniques when the signal processing is carried out in complex situations. The synthesis of the most recent achievements of the technology and discovery of the potential future directions passed down the concept of this work. The aim is to provide a push to developers to promote the implementation of the advanced application-specific solutions. And this discussion also demonstrates the flexible character that helps meet the demands of dynamic signal processing environments, forming the foundation for the next advanced systems that are adaptive and robust.

关键词： Estimation theory robust estimation optimal estimation signal processing variable structure filter sliding innovation filter

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Digital Twin Meets information fusion: Panel Summary 33

Digital Twin Meets Information Fusion: Panel Summary

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conference on signal processing, sensor/information fusion, and target recognition XXXIII

作者： Blasch, Erik Chen, Genshe Chen, Yu Zheng, Yufeng Grewe, Lynne Kadar, Ivan Savakis, Andreas Air Force Res Lab Arlington VA 22203 USA Intelligent Fus Technol Inc 20271 Goldenrod Ln Germantown MD 20271 USA Binghamton Univ Binghamton NY 13902 USA Univ Mississippi Med Ctr Jackson MS 39216 USA Calif State Univ East Bay 25800 Carlos Bee Blvd Hayward CA 94542 USA Interlink Syst Sci Inc Lake Success NY 11042 USA Rochester Inst Technol Rochester NY 14623 USA

ISBN: (纸本)9781510674332;9781510674325

Within the recent years, the concept of Digital Twins (DT) emerged to support digital engineering physical-systems design in the processing and analysis of device components, data flows, and networked systems. As an element of systems engineering, the DT serves as a method for life cycle management for operations and maintenance through monitoring, diagnostics, and prognostics. Key to DT methods is the use of distributed sensors to monitor the system and determine the functioning with the use of physical design information, such as a series of distributed edge sensors and the layout of a physical electrical grid. While industries like industrial manufacturing, electrical power, space systems, and healthcare maintenance have embraced DT;other groups are utilizing the concept for analysis. Given that a large number of sensors are used to gather data on the health of system, it is natural that data fusion, estimation theory, and signals processing support digital twin fusion (DTwF);but there are a variety of challenges such as big data, distribution fusion, and edge analytics. The panel will discuss areas in which data and information fusion techniques enhance DT applications.

关键词： Data fusion information Group Model information fusion User Refinement Deep Learning Cyber awareness Digital Twins digital twin fusion (DTwF)

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Centralized multi-sensor multi-target data fusion with tracks as measurements 32

Centralized multi-sensor multi-target data fusion with track...

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conference on signal processing, sensor/information fusion, and target recognition XXXII

作者： Hunter, Emily Clark, Daniel E. Balaji, Bhashyam O'Rourke, Sean Queens Univ Sch Comp Kingston ON Canada Univ Southampton Elect & Comp Sci Southampton Hants England Carleton Univ Dept Syst & Comp Engn Ottawa ON Canada Air Force Res Lab Sensors Directorate Dayton OH USA

ISBN: (数字)9781510662117

ISBN: (纸本)9781510662100;9781510662117

Tracking systems often provide sets of tracks rather than raw detections obtained from sensors. Integrating these track sets into other tracking systems is challenging because the usual sensor models do not apply. In this work we present a method for fusing track data from multiple sensors in a central fusion node. The algorithm exploits the covariance intersection algorithm as a pseudo-Kalman filter which is integrated into a multi-sensor multi-target tracker within a Bayesian paradigm. This makes it possible to (i) integrate the proposed fusion method seamlessly into any existing tracker;(ii) modify multi-target trackers to take a set of tracks as a set of measurements;and (iii) perform gating to enable data association between tracks. The described method is demonstrated in simulations using several target trackers within the Stone Soup tracking framework.

关键词： Covariance intersection multi-target tracking multi-sensor fusion distributed data fusion

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