Improving Navigation in GNSS-Challenging Environments: Multi-UAS Cooperation and Generalized Dilution of Precision

Causa, Flavia; Fasano, Giancarmine

doi:10.1109/taes.2020.3043543

Cited by 36 publications

(19 citation statements)

References 39 publications

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“…For the sake of brevity, details about magnetometer and GNSS residual and covariance matrices are omitted from this manuscript. For further details, the interested reader is referred to [29].…”

Section: Cooperative Navigation Filtermentioning

confidence: 99%

“…The necessity of a numerical approach derives from the problem dependency on the system dynamics, which makes bias estimation performance dependent not only on cooperative navigation measurements but also on the time evolution of position, velocity, and attitude, in a fully coupled fashion. Thus, a purely analytical approach such as the one proposed in [29] for positioning accuracy prediction cannot be applied. A custom camera/IMU/GNSS/magnetometer simulator has been developed for this purpose in MATLAB ® .…”

Section: Numerical Analysismentioning

confidence: 99%

“…Cooperative or networked navigation is intended as the operation of using networked relative measurements (e.g., range-based, angle-based) to the aim of the navigation performance increase [28]. This approach has been widely used in the open literature with promising outcomes especially when navigating under non-nominal GNSS coverage [29,30]. As an example, a networked system of the GNSS ground receiver has been exploited in [31,32].…”

Section: Introductionmentioning

confidence: 99%

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Improved In-Flight Estimation of Inertial Biases through CDGNSS/Vision Based Cooperative Navigation

Causa

Fasano

2021

Sensors

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This paper discusses the exploitation of a cooperative navigation strategy for improved in-flight estimation of inertial sensors biases on board unmanned aerial vehicles. The proposed multi-vehicle technique is conceived for a “chief” Unmanned Aerial Vehicle (UAV) and relies on one or more deputy aircrafts equipped with Global Navigation Satellite System (GNSS) antennas for differential positioning which also act as features for visual tracking. Combining carrier-phase differential GNSS and visual estimates, it is possible to retrieve accurate inertial-independent attitude information, thus potentially enabling improved bias estimation. Camera and carrier-phase differential GNSS measurements are integrated within a 15 states extended Kalman filter. Exploiting an ad hoc developed numerical environment, the paper analyzes the performance of the cooperative approach for inertial biases estimation as a function of number of deputies, formation geometry and distances, and absolute and relative dynamics. It is shown that exploiting two deputies it is possible to improve biases estimation, while a single deputy can be effective if changes of relative geometry and dynamics are also considered. Experimental proofs of concept based on two multi-rotors flying in formation are presented and discussed. The proposed framework is applicable beyond the domain of small UAVs.

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Section: Cooperative Navigation Filtermentioning

confidence: 99%

Section: Numerical Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improved In-Flight Estimation of Inertial Biases through CDGNSS/Vision Based Cooperative Navigation

Causa

Fasano

2021

Sensors

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“…4 Therefore, in some scenarios with high positioning accuracy requirements, ordinary GNSS navigation systems are difficult to meet the needs. By enhancing the positioning performance of UAV swarm through a certain positioning base station and external measurements such as Lidar, cameras and opportunity signals, the real-time kinematic (RTK) technique provides effective ways to solve the navigation problem with UAVs, 5–7 but such applications are limited by strict working scenarios and costs.…”

Section: Introductionmentioning

confidence: 99%

A hybrid cooperative navigation method for UAV swarm based on factor graph and Kalman filter

Chen

Xiong

2022

International Journal of Distributed Sensor Networks

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Navigation plays an important role in the task execution of the micro-unmanned aerial vehicle (UAV) swarm. The Cooperative Navigation (CN) method that fuses the observation of onboard sensors and relative information between UAVs is a research hotspot. Aiming at the efficiency and accuracy problems of previous studies, this article proposes a hybrid-CN method for UAV swarm based on Factor Graph and Kalman filter. A global Factor Graph is used to combine Global Navigation Satellite System (GNSS) and ranging information to provide position estimations for modifying the distributed Kalman filter; distributed Kalman filter is established on each UAV to fuse inertial information and optimized position estimation to modify the navigation states. In order to provide time-consistent GNSS position information for the Factor Graph, a time synchronization filter is designed. The proposed method is tested and verified using standard Monte Carlo simulations, simulation results show that it can provide a more precise and efficient CN solution than traditional CN methods.

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“…In order to obtain positioning, navigation, and timing (PNT) information [1][2][3][4][5][6][7][8][9][10], the demand for global navigation satellite systems (GNSSs), in particular the global positioning system (GPS) [11][12][13][14][15][16], is increasing in various applications such as ground vehicles, air/marine transport, unmanned aerial vehicles (UAVs), and robots [17][18][19][20][21][22][23][24][25][26][27]. Thanks to multi-constellation and multi-frequency GNSS technology, the positioning accuracy and availability significantly improved [28,29].…”

Section: Introductionmentioning

confidence: 99%

GPS Multipath Detection Based on Carrier-to-Noise-Density Ratio Measurements from a Dual-Polarized Antenna

Kim¹,

Lee²,

Park³

2021

Preprint

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In this study, the global positioning system (GPS) multipath detection was performed based on the carrier-tonoise-density ratio, C/N 0 , measured through a dual-polarized antenna. As the right hand circular polarization (RHCP) antenna is sensitive to the signals directly received from the GPS, and the left hand circular polarization (LHCP) antenna is sensitive to the singly reflected signals, the C/N 0 difference between the RHCP and LHCP measurements is used for multipath detection. Once we collected the GPS signals in a low multipath location, we calculated the C/N 0 difference to obtain a threshold value that can be used to detect the multipath GPS signal received from another location. The results were validated through a ray-tracing simulation.

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Improving Navigation in GNSS-Challenging Environments: Multi-UAS Cooperation and Generalized Dilution of Precision

Cited by 36 publications

References 39 publications

Improved In-Flight Estimation of Inertial Biases through CDGNSS/Vision Based Cooperative Navigation

Improved In-Flight Estimation of Inertial Biases through CDGNSS/Vision Based Cooperative Navigation

A hybrid cooperative navigation method for UAV swarm based on factor graph and Kalman filter

GPS Multipath Detection Based on Carrier-to-Noise-Density Ratio Measurements from a Dual-Polarized Antenna

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