On the Localization of Wireless Targets: A Drone Surveillance Perspective

Bisio, Igor; Garibotto, Chiara; Haleem, Halar; Lavagetto, Fabio; Sciarrone, Andrea

doi:10.1109/mnet.011.2000648

Cited by 30 publications

(9 citation statements)

References 14 publications

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“…Lastly, the outputted map of the static environment can also be used for path planning and obstacle avoidance. Improved strategies of autonomous control can benefit industrial pick and place robots or collaborative surveillance agents [ 51 – 53 ].…”

Section: Conclusion and Discussionmentioning

confidence: 99%

A featureless approach for object detection and tracking in dynamic environments

et al. 2023

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One of the challenging problems in mobile robotics is mapping a dynamic environment for navigating robots. In order to disambiguate multiple moving obstacles, state-of-art techniques often solve some form of dynamic SLAM (Simultaneous Localization and Mapping) problem. Unfortunately, their higher computational complexity press the need for simpler and more efficient approaches suitable for real-time embedded systems. In this paper, we present a ROS-based efficient algorithm for constructing dynamic maps, which exploits the spatial-temporal locality for detecting and tracking moving objects without relying on prior knowledge of their geometrical features. A two-prong contribution of this work is as follows: first, an efficient scheme for decoding sensory data into an estimated time-varying object boundary that ultimately decides its orientation and trajectory based on the iteratively updated robot Field of View (FoV); second, lower time-complexity of updating the dynamic environment through manipulating spatial-temporal locality available in the object motion profile. Unlike existing approaches, the snapshots of the environment remain constant in the number of moving objects. We validate the efficacy of our algorithm on both V-Rep simulations and real-life experiments with a wide array of dynamic environments. We show that the algorithm accurately detects and tracks objects with a high probability as long as sensor noise is low and the speed of moving objects remains within acceptable limits.

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Section: Conclusion and Discussionmentioning

confidence: 99%

A featureless approach for object detection and tracking in dynamic environments

et al. 2023

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“…Classical networking (star, tree, P2P, mesh, bus topologies) [5], [7], [28], [44], opportunistic networking [3], [5], [45], and cognitive radios [46] are different classes of networking. Drones can also be used for networking purposes, such as measuring, intruder detection, and localization [43], [66], [67].…”

Section: Networkingmentioning

confidence: 99%

Wireless Sensor Networks-based Smart Agriculture: Sensing Technologies, Application and Future Directions

Rahu

Karim

Shams

et al. 2023

SJET

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With the advent of the latest sensing technologies, agricultural tasks can be performed so quickly andadequately and termed Smart agriculture. In this paper, a system based on sensor networks has been designed tomonitor agricultural parameters wirelessly. The proposed system has been deployed in a Wheat field. The aim of this workis to increase the quality and productivity of the Wheat crops and minimize the extensive field visits of the farmers. Thissystem enables precision agriculture by periodically measuring the three most key parameters (temperature, light, andwater level) for achieving a remarkable increase in quality, productivity and growth of the Wheat crops. Thus, this systemhelps the agriculturists, landowners and research experts to monitor these parameters at the base station without goingto the field site. A GUI tool is also designed to display the measured data and stored it in the database accordingly. Whiledesigning this system; IRIS mote, MDA100 data acquisition board, and MIB520 USB interface board are employed. Weuse TinyOS operating system for the development of codes for wireless nodes and the GUI tool is designed in MicrosoftVisual Studio. ZigBee IEEE 802.15.4 protocol and direct topology are used for the communication of nodes with the basestation.

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“…However, instead of the emerging applications of UAVs, in recent years, challenges regarding privacy and safety have been raised by the use of UAV systems [6]. The introduction of recreational UAVs into national airspace has sparked worries about unqualified and unlicensed pilots entering forbidden areas and interfering with aircraft operations.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Survey of Unmanned Aerial Vehicles Detection and Classification Using Machine Learning Approach: Challenges, Solutions, and Future Directions

Rahman,

Sejan,

Aziz

et al. 2024

Remote Sensing

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Autonomous unmanned aerial vehicles (UAVs) have several advantages in various fields, including disaster relief, aerial photography and videography, mapping and surveying, farming, as well as defense and public usage. However, there is a growing probability that UAVs could be misused to breach vital locations such as airports and power plants without authorization, endangering public safety. Because of this, it is critical to accurately and swiftly identify different types of UAVs to prevent their misuse and prevent security issues arising from unauthorized access. In recent years, machine learning (ML) algorithms have shown promise in automatically addressing the aforementioned concerns and providing accurate detection and classification of UAVs across a broad range. This technology is considered highly promising for UAV systems. In this survey, we describe the recent use of various UAV detection and classification technologies based on ML and deep learning (DL) algorithms. Four types of UAV detection and classification technologies based on ML are considered in this survey: radio frequency-based UAV detection, visual data (images/video)-based UAV detection, acoustic/sound-based UAV detection, and radar-based UAV detection. Additionally, this survey report explores hybrid sensor- and reinforcement learning-based UAV detection and classification using ML. Furthermore, we consider method challenges, solutions, and possible future research directions for ML-based UAV detection. Moreover, the dataset information of UAV detection and classification technologies is extensively explored. This investigation holds potential as a study for current UAV detection and classification research, particularly for ML- and DL-based UAV detection approaches.

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On the Localization of Wireless Targets: A Drone Surveillance Perspective

Cited by 30 publications

References 14 publications

A featureless approach for object detection and tracking in dynamic environments

A featureless approach for object detection and tracking in dynamic environments

Wireless Sensor Networks-based Smart Agriculture: Sensing Technologies, Application and Future Directions

A Comprehensive Survey of Unmanned Aerial Vehicles Detection and Classification Using Machine Learning Approach: Challenges, Solutions, and Future Directions

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