A Wind Estimation Based on Unscented Kalman Filter for Standoff Target Tracking Using a Fixed-Wing UAV

Sun, Shun; Dong, Kai; Guo, Chen; Tan, Daning

doi:10.1007/s42405-020-00290-7

Cited by 13 publications

(6 citation statements)

References 30 publications

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“…Linearization error that arises from the marginalization of past variables requires new links among the remaining variables. Note also that the elimination of state variables leads to reduced interdependence between landmark variables [51,142].…”

Section: Search Space Reduction In Nonlinear Systems Using Extended K...mentioning

confidence: 99%

“…Images captured from terrestrial and aerial photogrammetry for feature extraction enable data association for the joint estimation of the map and the UAV state [155]. Figure 5 illustrates the computation of the UAV position at time n. The distribution shows the joint posterior density of the UAV position q at any time n, and the landmark positions l, based on the observations and motion from time 0 to n with respect to the initial position q 0 [142]. To fix misalignment issues that may come up with the imagery data, the point clouds may be colorized to enhance the level of detail and accuracy of image sensors [156].…”

Section: Visual Odometry and Photogrammetry In Uavmentioning

confidence: 99%

“…. , l M , they form a local map together with the T F timeframes [64,142]. The conditional distribution of the UAV positions is [q 1 , q 2 , .…”

Section: Impact Of Sensor Parameters On Accuracy Of Visual 3d Reconst...mentioning

confidence: 99%

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Simultaneous Localization and Mapping (SLAM) and Data Fusion in Unmanned Aerial Vehicles: Recent Advances and Challenges

Gupta¹,

Fernando²

2022

Preprint

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<p>This article presents a survey of simultaneous localization and mapping (SLAM) and data fusion techniques for object detection and environmental scene perception in unmanned aerial vehicles (UAVs). We critically evaluate some current SLAM implementations in robotics and autonomous vehicles and their applicability and scalability to UAVs. SLAM is envisioned as a potential technique for object detection and scene perception to enable UAV navigation through continuous state estimation. In this article, we bridge the gap between SLAM and data fusion in UAVs while also comprehensively surveying related object detection techniques such as visual odometry and aerial photogrammetry. We begin with an introduction to applications where UAV localization is necessary, followed by an analysis of multimodal sensor data fusion to fuse the information gathered from different sensors mounted on UAVs. We then discuss SLAM techniques such as Kalman filters and extended Kalman filters to address scene perception, mapping, and localization in UAVs. The findings are summarized to correlate prevalent and futuristic SLAM and data fusion for UAV navigation, and some avenues for further research are discussed.</p>

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Section: Search Space Reduction In Nonlinear Systems Using Extended K...mentioning

confidence: 99%

Section: Visual Odometry and Photogrammetry In Uavmentioning

confidence: 99%

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Simultaneous Localization and Mapping (SLAM) and Data Fusion in Unmanned Aerial Vehicles: Recent Advances and Challenges

Gupta¹,

Fernando²

2022

Preprint

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“…However, the response time of the robust controller is too long and unsuitable for target tracking requiring high maneuverability. To quantitatively describe wind dynamics, a simple conservative model (e.g., sine function as in [26] or linear model as in [27]) or a more sophisticated one (e.g., stochastic as in [28]) can be used. In [28], the Dryden model [29] and Davenport model [30] are used to describe the dynamics of wind at high altitudes or near the ground, respectively.…”

Section: Introductionmentioning

confidence: 99%

Cooperative Standoff Target Tracking using Multiple Fixed-Wing UAVs with Input Constraints in Unknown Wind

Liu,

Xiang,

Zhu

2023

Drones

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This paper investigates the problem of cooperative standoff tracking using multiple fixed-wing unmanned aerial vehicles (UAVs) with control input constraints. In order to achieve accurate moving target tracking in the presence of unknown background wind, a coordinated standoff target tracking algorithm is proposed. The objective of the research is to steer multiple UAVs to fly a circular orbit around a moving target with prescribed intervehicle angular spacing. To achieve this goal, two control laws are proposed, including relative range regulation and space phase separation. On one hand, a heading rate control law based on a Lyapunov guidance vector field is proposed. The convergence analysis shows that the UAVs can asymptotically converge to a desired circular orbit around the target, regardless of their initial position and heading. Through a rigorous theoretical proof, it is concluded that the command signal of the proposed heading rate controller will not violate the boundary constraint on the heading rate. On the other hand, a temporal phase is introduced to represent the phase separation and avoid discontinuity of the wrapped space phase angle. On this basis, a speed controller is developed to achieve equal phase separation. The proposed airspeed controller meets the requirements of the airspeed constraint. Furthermore, to improve the robustness of the aircraft during target tracking, an estimator is developed to estimate the composition velocity of the unknown wind and target motion. The proposed estimator uses the offset vector between the UAV’s actual flight path and the desired orbit, which is defined by the Lyapunov guidance vector field, to estimate the composition velocity. The stability of the estimator is proved. Simulations are conducted under different scenarios to demonstrate the effectiveness of the proposed cooperative standoff target tracking algorithm. The simulation results indicate that the temporal-phase-based speed controller can achieve a fast convergence speed and small phase separation error. Additionally, the composition velocity estimator exhibits a fast response speed and high estimation accuracy.

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“…The LGVF methods are improved for multiple UAVs to track the target in [12]. Besides, to overcome complex environmental constraints, Sun et al [13] proposes a wind state estimation method based on the Unscented Kalaman Filter, where LGVF is introduced to achieve standoff target tracking. Some other algorithms include the partially observable Markov decision process, backstepping-like technique, hierarchical collaboration and deep learning [14~17], where the hierarchical collaboration method is one of the most effective collaborative ways.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Collaboration Approach for Tracking of Flying Target by UAVs

Zhang

Yue

et al. 2020

IEEE Access

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This paper investigates a hierarchical target tracking method based on the collaboration of unmanned aerial vehicles (UAVs) at different altitudes. To enhance the target tracking range, the highaltitude UAVs monitor the wide area, and transmit their surveillance information to low-altitude UAVs which directly detect and collect information about the target's movements. The contributions of this paper are threefold: First, to track the flying target in a dynamic environment, a modified Lyapunov guidance vector field (LGVF) method is used to plan velocities for UAVs, where a time-varying vertical component is incorporated into the traditional LGVF function to satisfy the constraints of cluster communication. Secondly, a three-dimensional local collision-free guidance vector field (TLCGVF) method is proposed for UAVs to plan collision-free paths on line. To simultaneously track the target and avoid obstacles, the vector field by LGVF is used as the original vector field of TLCGVF. Thirdly, the rolling optimization strategy is used to adjust the reactive parameters of TLCGVF to enhance the path quality. The simulation results confirm the feasibility of the above approach.

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A Wind Estimation Based on Unscented Kalman Filter for Standoff Target Tracking Using a Fixed-Wing UAV

Cited by 13 publications

References 30 publications

Simultaneous Localization and Mapping (SLAM) and Data Fusion in Unmanned Aerial Vehicles: Recent Advances and Challenges

Simultaneous Localization and Mapping (SLAM) and Data Fusion in Unmanned Aerial Vehicles: Recent Advances and Challenges

Cooperative Standoff Target Tracking using Multiple Fixed-Wing UAVs with Input Constraints in Unknown Wind

Hierarchical Collaboration Approach for Tracking of Flying Target by UAVs

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