Collaborative Multiple UAVs Navigation With GPS/INS/UWB Jammers Using Sigma Point Belief Propagation

Chen, Hongmei; Wang, Xianbo; Liu, Jianjuan; Wang, Jun; Ye, Wen

doi:10.1109/access.2020.3031605

Cited by 15 publications

(5 citation statements)

References 27 publications

(32 reference statements)

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“…UWB localization systems have been widely used in various applications [21]- [24]. However, the UWB range that can be covered is limited by the locations of the UWB anchors.…”

Section: Proposed Movable Uwb Anchor System Based On a Swarm Of Uavsmentioning

confidence: 99%

A Novel Movable UWB Localization System Using UAVs

Moon

Youn

2022

IEEE Access

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Recently, interest in ultrawideband (UWB)-based localization systems has increased in various application fields. However, since UWB anchors are usually fixed, they work only within the limited range that UWB measurement signals can reach. To address this issue, a swarm flight system with movable UWB anchors is developed. One ground station controls multiple unmanned aerial vehicles (UAVs), each of which is equipped with a UWB anchor and real-time kinematic global positioning system (RTK-GPS) capabilities, and collects the precise (centimeter-level) positions of the UWB anchor UAVs through RTK-GPS. In this way, the constraints of existing UWB systems can be alleviated by changing the UWB anchor positions in real time as desired by the user. In addition, this paper proposes a novel localization algorithm using only time-of-flight (TOF) measurements from UWB signals, heading information from an attitude and heading reference system (AHRS), and altitude information from a barometer. The results of UAV flight tests show that the proposed algorithm provides better localization performance than existing algorithms.INDEX TERMS Interacting multiple model, Kalman filter, localization, swarm flight system, ultrawideband (UWB), unmanned aerial vehicle (UAV).

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“…UWB localization systems have been widely used in various applications [21]- [24]. However, the UWB range that can be covered is limited by the locations of the UWB anchors.…”

Section: Proposed Movable Uwb Anchor System Based On a Swarm Of Uavsmentioning

confidence: 99%

A Novel Movable UWB Localization System Using UAVs

Moon

Youn

2022

IEEE Access

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“…Nevertheless, this approach is heavily influenced by complex environmental factors and is also constrained by sensor limitations, which significantly restricts its effectiveness. The detection method based on the time-frequency domain characteristics of fault arc current and voltage is currently a more mainstream approach in direct current arc fault detection methods [21][22][23][24]. A large number of domestic and international scholars have conducted extensive research in this field.…”

Section: Introductionmentioning

confidence: 99%

Arc Detection of Photovoltaic DC Faults Based on Mathematical Morphology

Song,

Lu,

et al. 2024

Machines

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With the rapid growth of the photovoltaic industry, fire incidents in photovoltaic systems are becoming increasingly concerning as they pose a serious threat to their normal operation. Research findings indicate that direct current (DC) fault arcs are the primary cause of these fires. DC arcs are characterized by high temperature, intense heat, and short duration, and they lack zero crossing or periodicity features. Detecting DC fault arcs in intricate photovoltaic systems is challenging. Hence, researching DC fault arcs in photovoltaic systems is of crucial significance. This paper discusses the application of mathematical morphology for detecting DC fault arcs. The system utilizes a multi-stage mathematical morphology filter, and experimental results have shown its effective extraction of fault arc features. Subsequently, we propose a method for detecting DC fault arcs in photovoltaic systems using a cyclic neural network, which is well-suited for time series processing tasks. By combining multiple features extracted from experiments, we trained the neural network and achieved high accuracy. This experiment demonstrates that our recurrent neural network (RNN) based scheme for DC fault arc recognition has significant reference value and implications for future research. The ROC curve on the test set approaches 1 from the initial state, and the accuracy on the test set remains at 98.24%, indicating the strong robustness of the proposed model.

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“…The ISAC protocol, system architecture design [8], signal-sharing [8][9][10][11][12][13], time-sharing [14], array-sharing [15], spectrum-sharing [16] and power-sharing [17] have been studied to realize the ISAC system. In signal-sharing, due to no significant difference between radar and communication in the time and spatial domains, both the radar detection and the wireless communication use the same waveform [8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

“…Array-sharing is used in array system, where the different functions are realized in different sub-arrays [15]. Recently, a distributed dual-function radar-communication (DFRC), multiple input and multiple output (MIMO) system was proposed using both signal and power sharing [17]. Integrating cognitive radio (CR-o) and cognitive radar (CR-r) paradigms, as spectrum-sharing paradigms, achieve the intelligent utilization of spectrum resources [18].…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning-Based UAVs Resource Allocation for Integrated Sensing and Communication (ISAC) System

et al. 2022

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Due to the limited ability of a single unmanned aerial vehicle (UAV), group unmanned aerial vehicles (UAVs) have attracted more attention in communication and radar fields. The use of an integrated sensing and communication (ISAC) system can make communication and radar modules share a radar module’s resources, coupled with efficient resource allocation methods. It can effectively solve the problem of inadequate UAV resources and the low utilization rate of resources. In this paper, the resource allocation problem is addressed for group UAVs to achieve a trade-off between the detection and communication performance, where the ISAC system is equipped in group UAVs. The resource allocation problem is described by an optimization problem, but with group UAVs, the problem is complex and cannot be solved efficiently. Compared with the traditional resource allocation scheme, which needs a lot of calculation or sample set problems, a novel reinforcement-learning-based method is proposed. We formulate a new reward function by combining mutual information (MI) and the communication rate (CR). The MI describes the radar detection performance, and the CR is for wireless communication. Simulation results show that compared with the traditional Kuhn Munkres (KM) or the deep neural network (DNN) methods, this method has better performance with the increase in problem complexity. Additionally, the execution time of this scheme is close to that of the DNN scheme, and it is better than the KM algorithm.

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Collaborative Multiple UAVs Navigation With GPS/INS/UWB Jammers Using Sigma Point Belief Propagation

Cited by 15 publications

References 27 publications

A Novel Movable UWB Localization System Using UAVs

A Novel Movable UWB Localization System Using UAVs

Arc Detection of Photovoltaic DC Faults Based on Mathematical Morphology

Reinforcement Learning-Based UAVs Resource Allocation for Integrated Sensing and Communication (ISAC) System

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