Target Tracking with a Flexible UAV Cluster Array

Sridhar, V.; Manikas, A.

doi:10.1109/glocomw.2016.7849056

Cited by 11 publications

(6 citation statements)

References 14 publications

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“…These mobile platforms perform online coded capture and offline storage and processing such as image correction and matching [262] and other methods in the edge server. As a different example, in the applications of surveillance, one needs to conduct real-time object detection and tracking [263], which can be executed on the edge server and send specific action instructions back to the mobile platform, such as adjusting the speed according to the deviation from the reference [264], or the position of other platforms and objects [265]. Such tasks demand low latency, but are not bandwidth starving.…”

Section: ) Single Mobile Platformmentioning

confidence: 99%

Computational Imaging and Artificial Intelligence: The Next Revolution of Mobile Vision

Suo¹,

Zhang²,

Gong³

et al. 2021

Preprint

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Signal capture stands in the forefront to perceive and understand the environment and thus imaging plays the pivotal role in mobile vision. Recent explosive progresses in Artificial Intelligence (AI) have shown great potential to develop advanced mobile platforms with new imaging devices. Traditional imaging systems based on the "capturing images first and processing afterwards" mechanism cannot meet this unprecedented demand. Differently, Computational Imaging (CI) systems are designed to capture high-dimensional data in an encoded manner to provide more information for mobile vision systems. Thanks to AI, CI can now be used in real systems by integrating deep learning algorithms into the mobile vision platform to achieve the closed loop of intelligent acquisition, processing and decision making, thus leading to the next revolution of mobile vision. Starting from the history of mobile vision using digital cameras, this work first introduces the advances of CI in diverse applications and then conducts a comprehensive review of current research topics combining CI and AI. Motivated by the fact that most existing studies only loosely connect CI and AI (usually using AI to improve the performance of CI and only limited works have deeply connected them), in this work, we propose a framework to deeply integrate CI and AI by using the example of self-driving vehicles with high-speed communication, edge computing and traffic planning. Finally, we outlook the future of CI plus AI by investigating new materials, brain science and new computing techniques to shed light on new directions of mobile vision systems.

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Section: ) Single Mobile Platformmentioning

confidence: 99%

Computational Imaging and Artificial Intelligence: The Next Revolution of Mobile Vision

Suo¹,

Zhang²,

Gong³

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…Among these, UAVs are rapidly gaining popularity for research and applications in cooperative robotics, due to the availability of reliable, stable, and cost-effective devices which are also able to carry a variety of sensors. Recent research concerning multirobots target tracking based on UAV clusters can be found in [3], in which cooperative control of fixed-wings UAVs is addressed for the tracking of ground mobile targets emitting radio-frequency signals; in [4], the path planning strategy for video-based tracking of ground targets is especially aimed at maximizing the visibility of the target in an urban area setting; thus, when ground obstacles of different shapes and sizes may cause occlusion; in [5], a study on the localization and tracking of a generic target through a cluster of UAVs with acoustic sensors is illustrated.…”

Section: Introductionmentioning

confidence: 99%

Acoustic Target Tracking Through a Cluster of Mobile Agents

Drioli

Giordano

Salvati

et al. 2021

IEEE Trans. Cybern.

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This paper discusses the problem of tracking a moving target by means of a cluster of mobile agents that is able to sense the acoustic emissions of the target, with the aim of improving the target localization and tracking performance with respect to conventional fixed-array acoustic localization. We handle the acoustic part of the problem by modeling the cluster as a sensor network, and we propose a centralized control strategy for the agents that exploits the spatial sensitivity pattern of the sensor network to estimate the best possible cluster configuration with respect to the expected target position. In order to take into account the position estimation delay due to the frame-based nature of the processing, the possible positions of the acoustic target in a given future time interval are represented in terms of a compatible set, that is, the set of all possible future positions of the target, given its dynamics and its present state. A frame-by-frame cluster reconfiguration algorithm is presented, which adapts the position of each sensing agent with the goal of pursuing the maximum overlap between the region of high acoustic sensitivity of the entire cluster and the compatible set of the sound-emitting target. The tracking scheme iterates, at each observation frame, the computation of the target compatible set, the reconfiguration of the cluster, and the target acoustic localization. The reconfiguration step makes use of an opportune cost function proportional to the difference of the compatibility set and the acoustic sensitivity spatial pattern determined by the mobile agent positions. Simulations under different geometric configurations and positioning constraints demonstrate the ability of the proposed approach to effectively localize and track a moving target based on its acoustic emission. The Doppler effect related to moving sources and sensors is taken into account, and its impact on performance is analyzed. We compare the localization results with conventional static-array localization and positioning of acoustic sensors through genetic algorithm optimization, and results demonstrate the sensible improvements in terms of localization and tracking performance. Although the method is discussed here with respect to acoustic target tracking, it can be effectively adapted to video-based localization and

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“…The "flexible array" is defined as an antenna array with time varying geometry, i.e. each of the array elements move independently and this paper is an extension of [30] where a flexible array formed by a swarm of unmanned aerial vehicles (UAVs) is presented. If the array geometry is flexible, i.e.…”

mentioning

confidence: 99%

“…This paper is concerned with tracking the DOA of multiple sources in non-stationary wireless communications using both rigid arrays and flexible arrays. In addition to [30] in the current literature, to the best of our knowledge, there are only two papers [37], [38] that deal with the tracking using time-varying arrays. [37] is concerned with tracking stationary sources using an array of hydrophones.…”

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confidence: 99%

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Multi Direction-of-Arrival Tracking Using Rigid and Flexible Antenna Arrays

Tang

Manikas

2021

IEEE Trans. Wireless Commun.

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This paper is concerned with the problem of simultaneously tracking the direction-of-arrival (DOA) of far-field multiple moving sources/users in wireless communications using the vector-signal received by an antenna array of N elements. The antenna array can be rigid (fixed array locations) or flexible (time-varying array locations), and it is used in conjunction with a "manifold extender", a spatiotemporal state-space model and a Kalman-type tracking approach for non-stationary wireless channels. In particular, two tracking approaches are proposed. The first is based on an arrayed Extended Kalman Filter (arrayed-EKF) algorithm and the second on an arrayed Unscented Kalman Filter (arrayed-UKF) algorithm. Furthermore, if the array is rigid the spatiotemporal state-space model incorporates the DOAs and the angular velocities of the sources, while if it is flexible it also includes the array locations in the set of statevariables. The performance of the two approaches using both rigid and flexible arrays is evaluated using computer simulation studies and compared with a subspace tracking algorithm and a particle filter method under the same conditions. The results show that the arrayed-UKF and the arrayed-EKF show superior tracking performance, especially for low SNRs. Index Terms-Array processing, flexible array, DOA tracking, extended Kalman filter, unscented Kalman filter, spatiotemporal arrays, array manifolds, extended manifolds Zhuqing Tang (S'17) received the B. Eng. Degree (with Hons.) in Electronic and Communication Engineering from University of Liverpool, U.K., in 2016, and the M.Sc. degree in Communications and Signal Processing from Imperial College London, U.K., in 2017. She is currently pursuing the Ph.D. degree with the Communications and Signal Processing group, Department of Electrical and Electronic Engineering, Imperial College London, U.K., under the supervision of Prof. A. Manikas. Her research interests include array communications, array signal processing, flexible array signal processing, beamforming, and source tracking.

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Target Tracking with a Flexible UAV Cluster Array

Cited by 11 publications

References 14 publications

Computational Imaging and Artificial Intelligence: The Next Revolution of Mobile Vision

Computational Imaging and Artificial Intelligence: The Next Revolution of Mobile Vision

Acoustic Target Tracking Through a Cluster of Mobile Agents

Multi Direction-of-Arrival Tracking Using Rigid and Flexible Antenna Arrays

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