Multi‐UAVs Cooperative Localization Algorithms with Communication Constraints

Fu, Xiaowei; Bi, Haiyang; Gao, Xiaoguang

doi:10.1155/2017/1943539

Cited by 14 publications

(10 citation statements)

References 11 publications

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“…In this paper, the receding horizon method [2,[24][25][26] was used to make maneuver decision for multi-UAVs target tracking problem. We set the length of horizon L = 3, that means, "look forward 3 steps, take one step".…”

Section: Distributed Motion Control Algorithmmentioning

confidence: 99%

Multi-UAVs Communication-Aware Cooperative Target Tracking

Liu

Gao

2018

Applied Sciences

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A kind of communication-aware cooperative target tracking algorithm is proposed, which is based on information consensus under multi-Unmanned Aerial Vehicles (UAVs) communication noise. Each UAV uses the extended Kalman filter to predict target movement and get an estimation of target state. The communication between UAVs is modeled as a signal to noise ratio model. During the information fusion process, communication noise is treated as a kind of observation noise, which makes UAVs reach a compromise between observation and communication. The classical consensus algorithm is used to deal with observed information, and consistency prediction of each UAV's target state is obtained. Each UAV calculates its control inputs using receding horizon optimization method based on consistency results. The simulation results show that introducing communication noise can make UAVs more focused on maintaining good communication with other UAVs in the process of target tracking, and improve the accuracy of cooperative target tracking.Keywords: communication noise model; information fusion; receding horizon optimization IntroductionIn recent years, UAVs (Unmanned Aerial Vehicles) are playing an increasingly important role in collaborative investigation, battlefield combat, target status monitoring and other fields [1-3]. One of the most popular area is the multi-UAVs cooperative target tracking problem which can be abstracted as a mobile sensor network configuration problem [4][5][6][7][8]. The optimal sensor configuration can greatly reduce uncertainty of target state estimation. In the traditional target tracking problem, communication between sensors (or UAVs) usually uses the disc model [3], which means that the UAV has a clear communication radius. But the real communication between UAVs is very complicated. The disc model is only an ideal model, which cannot be a good simulation model of real communication. There have been some researches on target tracking problem with communication awareness [9-12], but the communication models ignore the impact of interference and attenuation. In reality, quality of communication is affected by many factors. There are many characteristics of the wireless channel of networked UAVs, such as signal-to-noise ratio (SNR) [9,11,12], signal-to-interference-plus-noise ratio (SINR) [13,14], and throughput gains [15][16][17][18][19]. SINR is widely used to describe the quality of a transmission in presence of interference. In order to improve the network performance using UAVs as flying base stations in a device-to-device communication network, SINR model was used in Reference [13] to quantify communication quality. In Reference [14], the authors studied a cellular-enabled UAV communication system consisting of one UAV and multiple GBSs (ground base stations). The quality of GBS-UAV link was determined by the received SNR. A similar problem was also studied in Reference [15], where an UAV was dispatched as a mobile AP (access point) in an UAV-enabled wireless powered communication network. The wo...

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Section: Distributed Motion Control Algorithmmentioning

confidence: 99%

Multi-UAVs Communication-Aware Cooperative Target Tracking

Liu

Gao

2018

Applied Sciences

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“…However, restricted by the development of current intelligence technology, the UCAV still cannot complete the tasks independently. Therefore, a remotely piloted UCAV will be a prospective mode of air fight in the future, and remotely piloted technology has become an important way to win unmanned aerial combat [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Universal Adaptive Neural Network Predictive Algorithm for Remotely Piloted Unmanned Combat Aerial Vehicle in Wireless Sensor Network

Fang

Fan

et al. 2020

Sensors

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Remotely piloted unmanned combat aerial vehicle (UCAV) will be a prospective mode of air fight in the future, which can remove the physical restraint of the pilot, maximize the performance of the fighter and effectively reduce casualties. However, it has two difficulties in this mode: (1) There is greater time delay in the network of pilot-wireless sensor-UCAV, which can degrade the piloting performance. (2) Designing of a universal predictive method is very important to pilot different UCAVs remotely, even if the model of the control augmentation system of the UCAV is totally unknown. Considering these two issues, this paper proposes a novel universal modeling method, and establishes a universal nonlinear uncertain model which uses the pilot’s remotely piloted command as input and the states of the UCAV with a control augmentation system as output. To deal with the nonlinear uncertainty of the model, a neural network observer is proposed to identify the nonlinear dynamics model online. Meanwhile, to guarantee the stability of the overall observer system, an adaptive law is designed to adjust the neural network weights. To solve the greater transmission time delay existing in the pilot-wireless sensor-UCAV closed-loop system, a time-varying delay state predictor is designed based on the identified nonlinear dynamics model to predict the time delay states. Moreover, the overall observer-predictor system is proved to be uniformly ultimately bounded (UUB). Finally, two simulations verify the effectiveness and universality of the proposed method. The results indicate that the proposed method has desirable performance of accurately compensating the time delay and has universality of remotely piloting two different UCAVs.

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“…Recently, multiple UAVs have received more and more attention for their accomplishments in both military and civil applications. Cooperative search and coverage is one major application of multiple UAVs equipped with sensors [ 1 ], such as camera, radar, and sonar. The goal of cooperative search and coverage is to control multiple UAVs to find several unknown ground targets scattered in a given surveillance region, while maximally reducing the uncertainty of the environment and minimizing the search time [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

A Cooperative Search and Coverage Algorithm with Controllable Revisit and Connectivity Maintenance for Multiple Unmanned Aerial Vehicles

Liu

Gao

2018

Sensors

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In this paper, we mainly study a cooperative search and coverage algorithm for a given bounded rectangle region, which contains several unknown stationary targets, by a team of unmanned aerial vehicles (UAVs) with non-ideal sensors and limited communication ranges. Our goal is to minimize the search time, while gathering more information about the environment and finding more targets. For this purpose, a novel cooperative search and coverage algorithm with controllable revisit mechanism is presented. Firstly, as the representation of the environment, the cognitive maps that included the target probability map (TPM), the uncertain map (UM), and the digital pheromone map (DPM) are constituted. We also design a distributed update and fusion scheme for the cognitive map. This update and fusion scheme can guarantee that each one of the cognitive maps converges to the same one, which reflects the targets’ true existence or absence in each cell of the search region. Secondly, we develop a controllable revisit mechanism based on the DPM. This mechanism can concentrate the UAVs to revisit sub-areas that have a large target probability or high uncertainty. Thirdly, in the frame of distributed receding horizon optimizing, a path planning algorithm for the multi-UAVs cooperative search and coverage is designed. In the path planning algorithm, the movement of the UAVs is restricted by the potential fields to meet the requirements of avoiding collision and maintaining connectivity constraints. Moreover, using the minimum spanning tree (MST) topology optimization strategy, we can obtain a tradeoff between the search coverage enhancement and the connectivity maintenance. The feasibility of the proposed algorithm is demonstrated by comparison simulations by way of analyzing the effects of the controllable revisit mechanism and the connectivity maintenance scheme. The Monte Carlo method is employed to validate the influence of the number of UAVs, the sensing radius, the detection and false alarm probabilities, and the communication range on the proposed algorithm.

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Multi‐UAVs Cooperative Localization Algorithms with Communication Constraints

Cited by 14 publications

References 11 publications

Multi-UAVs Communication-Aware Cooperative Target Tracking

Multi-UAVs Communication-Aware Cooperative Target Tracking

Universal Adaptive Neural Network Predictive Algorithm for Remotely Piloted Unmanned Combat Aerial Vehicle in Wireless Sensor Network

A Cooperative Search and Coverage Algorithm with Controllable Revisit and Connectivity Maintenance for Multiple Unmanned Aerial Vehicles

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