Integrating Communication and Sensor Arrays to Model and Navigate Autonomous Unmanned Aerial Systems

Abstract

The emerging concept of drone swarms creates new opportunities with major societal implications. However, future drone swarm applications and services pose new communications and sensing challenges, particularly for collaborative tasks. To address these challenges, in this paper, we integrate sensor arrays and communication to propose a mathematical model to route a collection of autonomous unmanned aerial systems (AUAS), a so-called drone swarm or AUAS swarm, without having a base station of communication but communicating with each other using multiple spatio-temporal data. The theories of structured matrices, concepts in multi-beam beamforming, and sensor arrays are utilized to propose a swarm routing algorithm. We address the routing algorithm’s computational and arithmetic complexities, precision, and reliability. We measure bit-error-rate (BER) based on the number of elements in sensor arrays and beamformed output of the members of the swarm to authenticate and secure the routing for the decentralized AUAS networking. The proposed model has the potential to enable future drone swarm applications and services. Finally, we discuss future work on obtaining a machine-learning-based low-cost drone swarm routing algorithm.

Multi-Beam Beamforming-Based ML Algorithm to Optimize the Routing of Drone Swarms

“…The 5G wireless networking facilitates the improvement of the capacity of beamforming on the collection of unmanned autonomous systems (UAS) or drone swarms [1][2][3][4][5][6][7][8][9][10]. Beamforming allows mm-wave multi-input multi-output communication systems to increase the link capacity by exploiting the spatial multiplexing and/or diversity provided by multiple propagation paths [11][12][13][14].…”

“…Beamforming allows mm-wave multi-input multi-output communication systems to increase the link capacity by exploiting the spatial multiplexing and/or diversity provided by multiple propagation paths [11][12][13][14]. Although beamforming has been implemented to enhance UAS networking, the existing UAS routing algorithms rely on the ad hoc ondemand distance vector (AODV) and optimized link state routing (OLSR) [1]. AODV is a decentralized algorithm that exhibits robustness in dynamic topologies while maintaining low management overhead [15].…”

“…Contribution in Our Previous Work [1] and Building Work from [1] In contrast to most literature on unmanned autonomous systems, which focuses on a single flying antenna system (as a hub) [25][26][27][28][29][30], we proposed a unique mathematical model to route a collection of AUAS in [1] based on decentralization, followed by spatiotemporal data and wideband multibeam beamforming to route drone swarms. The algorithm in [1] is based on the brute-force calculation of the frequency Vandermonde matrices by vectors. We showed numerical results based on time-stamped beamformed signals of the routing algorithm and compared those with ground truth signals.…”

“…Building upon our previous work [1], and to improve the accuracy and efficiency of time-stamped beamformed signals of drone swarms, we present novel and efficient AUAS routing algorithms, i.e., classical followed by ML algorithms. The algorithms are based on the sparse factorization of the frequency Vandermonde matrices corresponding to each drone, whose entries are determined via spatiotemporal data.…”

“…This characteristic makes ANN structures desirable in applications where the direct implementation of a function would be computationally expensive, but an ANN approximation of that function would be suitable. An example is the MIMO-based swarm routing algorithm [1], which is intended to run on resource-constrained onboard UAS controllers. In this application, the enhanced speed and efficiency of a carefully validated ANN approximation are acceptable, even with some degree of error and undefined behavior in poorly trained edge cases, as it serves as a component of a larger control system [40,41].…”