An investigation of geolocation-aware beamforming algorithms for swarming UAVs

Yeh, Shihyuan; Chamberland, Jean-François; Huff, Gregory H.

doi:10.1109/apusncursinrsm.2017.8072363

Cited by 15 publications

(6 citation statements)

References 2 publications

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“…To solve (7), we decompose the problem into a two (2) sub-problems describing the beamforming vectors and the UAV swarm trajectory. The original problem (7) can be solved iteratively between the sub-problems to obtain the suboptimal/near optimal results that satisfies the constraints as used in [3], [10], [21].…”

Section: Proposed Solutionmentioning

confidence: 99%

“…In [5], the UAV swarm tracked the movement of the eavesdropper to jam its received signal while maximizing the secrecy rate of the main receivers. The location of the eavesdropper is assumed to be known in this work and the geometry of the UAV swarm was not considered contrary to the specifications suggested in [7], [18]. However, without considering the geometry of the UAV swarm, the optimized beamforming weights are not guaranteed to produce a beam pattern [18].…”

Section: Introductionmentioning

confidence: 99%

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Gridded UAV Swarm for Secrecy Rate Maximization with Unknown Eavesdropper

Nnamani,

Khandaker,

Sellathurai

2021

Preprint

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This paper considers grid formation of an unmanned aerial vehicle (UAV) swarm for maximizing the secrecy rate in the presence of an unknown eavesdropper. In particular, the UAV swarm performs coordinated beamforming onto the null space of the legitimate channel to jam the eavesdropper located at an unknown location. By nulling the channel between the legitimate receiver and the UAV swarm, we obtain an optimal trajectory and jamming power allocation for each UAV enabling wideband single ray beamforming to improve the secrecy rate. Results obtained demonstrate the effectiveness of the proposed UAV-aided jamming scheme as well as the optimal number of UAVs in the swarm necessary to observe a saturation effect in the secrecy rate. We also show the optimal radius of the unknown but constrained location of the eavesdropper.

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Section: Proposed Solutionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Gridded UAV Swarm for Secrecy Rate Maximization with Unknown Eavesdropper

Nnamani,

Khandaker,

Sellathurai

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Leveraging mobile platforms to extend array capabilities is an emerging topic. To realize mobile phased arrays applications, three challenges need to be overcome: wireless phase synchronizations [1], positions and orientations awareness [2], and mobile beamforming and beamshaping techniques [3,4]. The contribution of this work is limited and primarily focuses on developments of mobile beamforming techniques.…”

Section: Introductionmentioning

confidence: 99%

Beamforming a circular-polarized radiation pattern on volumetric random arrays with deliberately aligned array antennas

Yeh

Chen

2021

IEICE ComEX

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This letter proposes a circularly polarized beamforming technique on volumetric random arrays with each antenna aligned deliberately. This technique seeks to advance the capabilities of spatially distributed antenna systems on mobile platforms. Sixteen microstrip patch antennas grouped as the tested array are adopted to investigate the fidelity of the developed beamforming technique. Each array element is distributed randomly and rotated freely to emulate the real mobile environment. Detailed and systematic beamforming procedures are provided and illustrated. Measured radiation patterns of the volumetric random array are compared to the simulated ones to benchmark their performance.

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“…In recent years, with the development of control, communication, electronics, and other technologies, the cooperative work of multiple UAVs has been realized. [20][21][22][23][24][25] For this reason, a multi-UAV cooperative target localization scheme is proposed in this paper. The main contributions of this paper are summarized as follows:…”

Section: Introductionmentioning

confidence: 99%

3D target localization based on multi–unmanned aerial vehicle cooperation

Yin

Huang

et al. 2020

Measurement and Control

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Ground target three-dimensional positions measured from optical remote-sensing images taken by an unmanned aerial vehicle play an important role in related military and civil applications. The weakness of this system lies in its localization accuracy being unstable and its efficiency being low when using a single unmanned aerial vehicle. In this paper, a novel multi–unmanned aerial vehicle cooperative target localization measurement method is proposed to overcome these issues. In the target localization measurement stage, three or more unmanned aerial vehicles simultaneously observe the same ground target and acquire multiple remote-sensing images. According to the principle of perspective projection, the target point, its image point, and the camera’s optic center are collinear, and nonlinear observation equations are established. These equations are then converted to linear equations using a Taylor expansion. Robust weighted least-squares estimation is used to solve the equations with the objective function of minimizing the weighted square sum of re-projection errors from target points to multiple pairs of images, which can make the best use of the effective information and avoid interference from the observation data. An automatic calculation strategy using a weight matrix is designed, and the weight matrix and target-position coordinate value are updated in each iteration until the iteration stopping condition is satisfied. Compared with the stereo-image-pair cross-target localization method, the multi–unmanned aerial vehicle cooperative target localization method can use more observation information, which results in higher rendezvous accuracy and improved performance. Finally, the effectiveness and robustness of this method is verified by numerical simulation and flight testing. The results show that the proposed method can effectively improve the precision of the target’s localization and demonstrates great potential for providing more accurate target localization in engineering applications.

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An investigation of geolocation-aware beamforming algorithms for swarming UAVs

Cited by 15 publications

References 2 publications

Gridded UAV Swarm for Secrecy Rate Maximization with Unknown Eavesdropper

Gridded UAV Swarm for Secrecy Rate Maximization with Unknown Eavesdropper

Beamforming a circular-polarized radiation pattern on volumetric random arrays with deliberately aligned array antennas

3D target localization based on multi–unmanned aerial vehicle cooperation

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