UAV Swarm-Enabled Localization in Isolated Region: A Rigidity-Constrained Deployment Perspective

Abstract

In isolated regions, utilizing the unmanned aerial vehicle (UAV) as an aerial anchor node is a promising technique to enable location awareness of ground terminals (GTs). In this letter, considering a UAV swarm-enabled localization for a group of distributed GTs, we aim to minimize the maximum Cramer-Rao lower bound (CRLB) for position estimates with anchor uncertainty. Then, an efficient differential evolution (DE)based method is proposed to find a sub-optimal solution. In particular, the rigidity of the UAV swarm is recognized as a critical constraint in the problem formulation to provide a unique swarm coordinate configuration and to maintain a prescribed flight formation. A gradient-based local optimization for rigidity is then proposed and embedded in the DE algorithm. Numerical results demonstrate that our proposed designs can reach better performance in localization accuracy while ensuring the rigidity of the UAV swarm, as compared with a random approach.

Management of Positioning Functions in Cellular Networks for Time-Sensitive Transportation Applications

“…4(b) that UE cannot establish a basic threshold like 10m with some anchor BSs even with a long-time crosscorrelation process using t = 24 PRS symbols. This situation is undesirable for localization, since the WLS estimation needs at least 4 anchors to estimate the location and imbalanced ranging accuracy from BS 1 to 5 will inevitably lead to error propagation to the location update of UE [33]. Based on the above analysis and demonstration, the time delay estimation accuracy can be influenced by two main factors considered in this paper: 1) the received signal length used for cross-correlation and 2) the interference level of each received symbol.…”

A systematic literature review of flying ad hoc networks: State‐of‐the‐art, challenges, and perspectives

“…UAV placement problem which tries to maximize the coverage region is a nonconvex problem and proved to be NP‐hard (X. Liu et al, 2019; Lyu et al, 2017) in general. In UAV placement problem, UAVs horizontal and/or vertical positioning, inter‐UAV safety distance maintenance, cost, UAV numbers, coverage rate, and users–UAV connectivity are important factors needed to be considered in the deployment problem (Gao et al, 2021; Ghazal, 2021; Kim & Lee, 2018, 2020; Lahmeri et al, 2021; Q. Liu et al, 2018; Masroor et al, 2021a; Rahimi et al, 2021; J. Yang, Liang, et al, 2021; C. Zhang et al, 2021). Trajectory/path planning : One of the essential factors to optimize the UAV network's performance is UAV trajectory design or path planning (Mozaffari et al, 2019).…”

“…In this regard, low cost and high mobility, multiple UAV‐mounted BSs are sent to target regions to provide temporary wireless communication services. UAVs horizontal and/or vertical placement, distance, cost, UAV numbers, coverage rate, and users–UAV connectivity are important factors needed to be considered in the deployment problem (Gao et al, 2021; Ghazal, 2021; Lahmeri et al, 2021; Q. Liu et al, 2018; Masroor et al, 2021a; Rahimi et al, 2021; J. Yang, Liang, et al, 2021; C. Zhang et al, 2021). ML algorithms are used to predict the optimal position of the UAVs by identifying the overloaded traffic areas by predicting users' demands and positions (Nouri, Abouei, et al, 2021; Nouri, Fazel, et al, 2021; Oliveira et al, 2021).…”

QoS-Oriented Sensing–Communication–Control Co-Design for UAV-Enabled Positioning

“…In these studies, UAVs were leveraged as the anchor nodes with perfect knowledge of their own positions. In practice, UAVs' positions are obtained through state sensing, and the inevitable sensing errors will cause uncertainty in anchor position information [21], [22]. Thus, the authors in [21] studied the problem of UAV self-localization and evaluated the impact of UAV position uncertainty on positioning performance.…”

“…Thus, the authors in [21] studied the problem of UAV self-localization and evaluated the impact of UAV position uncertainty on positioning performance. Moreover, Liu et al [22] proposed a deployment optimization method to improve the accuracy of UAV-enabled positioning whole considering UAV position uncertainty. These two studies assume that UAVs can hover stably at fixed positions, which is difficult to achieve in practice due to the influence of environmental factors like the wind [23].…”