Highly reliable relative navigation for multi-UAV formation flight in urban environments

Wang, Shizhuang; Zhan, Xingqun; Zhai, Yawei; Chi, Cheng; Shen, Jiawen

doi:10.1016/j.cja.2020.05.022

Cited by 31 publications

(18 citation statements)

References 23 publications

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“…In this Section, the extension of the C-DGNSS concept beyond land-based vehicles is discussed, along with emerging challenges and future trends. Moreover, the "moving base station" technique can be fairly applied to drone positioning, drone-to-drone, droneto-car communications, internet of drones (IoD), multi-UAV systems, UAV swarms, coordinated drones, and to improve their relative position accuracy further [28][29][30].…”

Section: Challenges and Future Research Trendsmentioning

confidence: 99%

Cooperative D-GNSS Aided with Multi Attribute Decision Making Module: A Rigorous Comparative Analysis

et al. 2022

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Satellite positioning lies within the very core of numerous Intelligent Transportation Systems (ITS) and Future Internet applications. With the emergence of connected vehicles, the performance requirements of Global Navigation Satellite Systems (GNSS) are constantly pushed to their limits. To this end, cooperative positioning (CP) solutions have attracted attention in order to enhance the accuracy and reliability of low-cost GNSS receivers, especially in complex propagation environments. In this paper, the problem of efficient and robust CP employing low-cost GNSS receivers is investigated over critical ITS scenarios. By adopting a Cooperative-Differential GNSS (C-DGNSS) framework, the target’s vehicle receiver can obtain Position–Velocity–Time (PVT) corrections from a neighboring vehicle and update its own position in real-time. A ranking module based on multi-attribute decision-making (MADM) algorithms is proposed for the neighboring vehicle rating and optimal selection. The considered MADM techniques are simulated with various weightings, normalization techniques, and criteria associated with positioning accuracy and reliability. The obtained criteria values are experimental GNSS measurements from several low-cost receivers. A comparative and sensitivity analysis are provided by evaluating the MADM algorithms in terms of ranking performance and robustness. The positioning data time series and the numerical results are then presented, and comments are made. Scoring-based and distance-based MADM methods perform better, while L1 RMS, HDOP, and Hz std are the most critical criteria. The multi-purpose applicability of the proposed scheme, not only for land vehicles, is also discussed.

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Section: Challenges and Future Research Trendsmentioning

confidence: 99%

Cooperative D-GNSS Aided with Multi Attribute Decision Making Module: A Rigorous Comparative Analysis

et al. 2022

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show abstract

“…Theorem 1. The estimation error of target relative position by UAV i is defined as pi0 = pi0 − p i0 , thus the estimation method (11) designed in this paper can make the estimation error pi0 converge exponentially fast to a circular area of radius 𝛼−𝜁 r centered at origin as t → ∞.…”

Section: Target Relative Position Estimator Designmentioning

confidence: 99%

“…Consider the UAV model with dynamics described by (1) and the CMPF error kinematics model (20). Assume that the target position can be obtained by estimator (11). Design the control law…”

Section: Mpf Controller Designmentioning

confidence: 99%

“…Up to now, unmanned aerial vehicles (UAVs) have been widely used in military and civilian applications, such as area surveillance [1][2][3], target tracking [4][5][6], environmental protection and navigation [7][8][9][10][11]. Among the many proposed UAV application scenarios, the target enclosing problem has become a popular research topic in recent years due to its multiple application prospects, including target pursuit and surveillance tasks.…”

Section: Introductionmentioning

confidence: 99%

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Target localization and encirclement control for multi‐UAVs with limited information

Wang

Chen

Jia

et al. 2022

IET Control Theory & Appl

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The problem of localization and encirclement control of a moving target by multi unmanned aerial vehicles (UAVs) is considered in this paper. The main objective is to guide the UAVs to form evenly spaced formations along the circumference, with the centre of the circumference tracking the movement of the target. Firstly, each UAV is assumed only to obtain the bearing angle information of the target, for which an estimator is developed to localize the target by bearing measurements. Thereafter, a distributed encirclement control algorithm based on cooperative moving path following (CMPF) is designed to drive the multi‐UAVs to circumnavigate around a moving target at a desired distance, thus developing to pursue cooperatively. Finally, the simulation results demonstrate the effectiveness of the proposed method.

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“…However, still up to this day, there are UAVs (Unmanned Aerial Vehicles) available on the market that are equipped with single-frequency GPS (Global Positioning System) receivers, which allow the UAV's position to be determined with an accuracy of only just better than 10 m [3,4]. Moreover, in most of the currently available photogrammetric platforms, the UAVs use GPS pseudorange measurements in the Single Point Positioning (SPP) mode to determine the trajectory for photogrammetric studies [5]. The SPP positioning mode is the basic solution in air navigation for low-cost UAVs.…”

Section: Introductionmentioning

confidence: 99%

Application the SBAS/EGNOS Corrections in UAV Positioning

Krasuski¹,

Wierzbicki²

2021

Energies

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The paper presents a new concept of determining the resultant position of a UAV (Unmanned Aerial Vehicle) based on individual SBAS (Satellite-Based Augmentation System) determinations from all available EGNOS (European Geostationary Navigation Overlay Service) satellites for the SPP (Single Point Positioning) code method. To achieve this, the authors propose a weighted mean model to integrate EGNOS data. The weighted model was based on the inverse of the square of the mean position error along the component axes of the BLh ellipsoidal frame. The calculations included navigation data from the EGNOS S123, S126, S136 satellites. In turn, the resultant UAV position model was determined using the Scilab v.6.0.0 software. Based on the proposed computational strategy, the mean values of the UAV BLh coordinates’ standard deviation were better than 0.2 m (e.g., 0.0000018° = 0.01″ in angular measurement). Additionally, the numerical solution used made it possible to increase the UAV’s position accuracy by about 29% for Latitude, 46% for Longitude and 72% for ellipsoidal height compared to the standard SPP positioning in the GPS receiver. It is also worth noting that the standard deviation of the UAV position calculated from the weighted mean model improved by about 21 ÷ 50% compared to the arithmetic mean model’s solution. It can be concluded that the proposed research method allows for a significant improvement in the accuracy of UAV positioning with the use of EGNOS augmentation systems.

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Highly reliable relative navigation for multi-UAV formation flight in urban environments

Cited by 31 publications

References 23 publications

Cooperative D-GNSS Aided with Multi Attribute Decision Making Module: A Rigorous Comparative Analysis

Cooperative D-GNSS Aided with Multi Attribute Decision Making Module: A Rigorous Comparative Analysis

Target localization and encirclement control for multi‐UAVs with limited information

Application the SBAS/EGNOS Corrections in UAV Positioning

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