NNAKF: A Neural Network Adapted Kalman Filter for Target Tracking

Jouaber, Sami; Bonnabel, Silvère; Velasco-Forero, Santiago; Pilté, Marion

doi:10.1109/icassp39728.2021.9414681

Cited by 26 publications

(1 citation statement)

References 13 publications

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“…The small memory footprint and high speed of the Kalman filter make it well-suited for real-time applications and embedded systems, as it only needs to retain the previous state and can efficiently update the estimated state in real-time. Different types of Kalman filters are among the most important technologies for moving target tracking [103][104][105]. In these studies, the Kalman filter is typically used for tracking after UAV detection and recognition.…”

Section: Filters-based Trackermentioning

confidence: 99%

UAV Detection and Tracking in Urban Environments Using Passive Sensors: A Survey

Yan,

Fu,

Lin

et al. 2023

Applied Sciences

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Unmanned aerial vehicles (UAVs) have gained significant popularity across various domains, but their proliferation also raises concerns about security, public safety, and privacy. Consequently, the detection and tracking of UAVs have become crucial. Among the UAV-monitoring technologies, those suitable for urban Internet-of-Things (IoT) environments primarily include radio frequency (RF), acoustic, and visual technologies. In this article, we provide a comprehensive review of passive UAV surveillance technologies, encompassing RF-based, acoustic-based, and vision-based methods for UAV detection, localization, and tracking. Our research reveals that certain lightweight UAV depth detection models have been effectively downsized for deployment on edge devices, facilitating the integration of edge computing and deep learning. In the city-wide anti-UAV, the integration of numerous urban infrastructure monitoring facilities presents a challenge in achieving a centralized computing center due to the large volume of data. To address this, calculations can be performed on edge devices, enabling faster UAV detection. Currently, there is a wide range of anti-UAV systems that have been deployed in both commercial and military sectors to address the challenges posed by UAVs. In this article, we provide an overview of the existing military and commercial anti-UAV systems. Furthermore, we propose several suggestions for developing general-purpose UAV-monitoring systems tailored for urban environments. These suggestions encompass considering the specific requirements of the application scenario, integrating detection and tracking mechanisms with appropriate countermeasures, designing for scalability and modularity, and leveraging advanced data analytics and machine learning techniques. To promote further research in the field of UAV-monitoring systems, we have compiled publicly available datasets comprising visual, acoustic, and radio frequency data. These datasets can be employed to evaluate the effectiveness of various UAV-monitoring techniques and algorithms. All of the datasets mentioned are linked in the text or in the references. Most of these datasets have been validated in multiple studies, and researchers can find more specific information in the corresponding papers or documents. By presenting this comprehensive overview and providing valuable insights, we aim to advance the development of UAV surveillance technologies, address the challenges posed by UAV proliferation, and foster innovation in the field of UAV monitoring and security.

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Section: Filters-based Trackermentioning

confidence: 99%