A Novel Ranging and IMU-Based Method for Relative Positioning of Two-MAV Formation in GNSS-Denied Environments

Cheng, Jia; Ren, Peng; Deng, Tingxiang

doi:10.3390/s23094366

Cited by 6 publications

(5 citation statements)

References 39 publications

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“…In the implementation of relative positioning technology in denied environments, some researchers have attempted to equip a single platform with multiple heterogeneous sensors, such as cameras [21], barometers [22], inertial navigation systems [23,24], and range and angle measurement devices [25,26]. These sensors, combined with multi-sensor data fusion algorithms [27,28], aim to achieve positioning.…”

Section: Related Workmentioning

confidence: 99%

Topology Perception and Relative Positioning of UAV Swarm Formation Based on Low-Rank Optimization

Di,

Guo

2024

Aerospace

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In a satellite-denied environment, a swarm of drones is capable of achieving relative positioning and navigation by leveraging the high-precision ranging capabilities of the inter-drone data link. However, because of factors such as high drone mobility, complex and time-varying channel environments, electromagnetic interference, and poor communication link quality, distance errors and even missing distance values between some nodes are inevitable. To address these issues, this paper proposes a low-rank optimization algorithm based on the eigenvalue scaling of the distance matrix. By gradually limiting the eigenvalues of the observed distance matrix, the algorithm reduces the rank of the matrix, bringing the observed distance matrix closer to the true value without errors or missing data. This process filters out distance errors, estimates and completes missing distance elements, and ensures high-precision calculations for subsequent topology perception and relative positioning. Simulation experiments demonstrate that the algorithm exhibits significant error filtering and missing element completion capabilities. Using the F-norm metric to measure the relative deviation from the true value, the algorithm can optimize the relative deviation of the observed distance matrix from 11.18% to 0.25%. Simultaneously, it reduces the relative positioning error from 518.05 m to 35.24 m, achieving robust topology perception and relative positioning for the drone swarm formation.

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Section: Related Workmentioning

confidence: 99%

Topology Perception and Relative Positioning of UAV Swarm Formation Based on Low-Rank Optimization

Di,

Guo

2024

Aerospace

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“…Cheng, J et al considered the characteristics of close-range accompanying flight of UAV swarms. By omitting the elevation dimension of the navigation coordinate system and assuming that all UAVs are in the same local navigation coordinate system, the H coordinate system is proposed [ 3 ]. Based on this system, UAV relative motion equations are derived, simplifying the traditional relative barycenter equation’s parameter calculations and improving algorithm efficiency.…”

Section: Introductionmentioning

confidence: 99%

Adaptive UAV Navigation Method Based on AHRS

Lu,

Li,

Xiong

et al. 2024

Sensors

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To address the inaccuracy of the Constant Acceleration/Constant Velocity (CA/CV) model as the state equation in describing the relative motion state in UAV relative navigation, an adaptive UAV relative navigation method is proposed, which is based on the UAV attitude information provided by Attitude and Heading Reference System (AHRS). The proposed method utilizes the AHRS output attitude parameters as the benchmark for dead reckoning and derives a relative navigation state equation with attitude error as process noise. By integrating the extended Kalman filter output for relative state estimation and employing an adaptive decision rule designed using the innovation of the filter update phase, the proposed method recalculates motion states deviating from the actual motion using the Tasmanian Devil Optimization (TDO) algorithm. The simulation results show that, compared with the CA/CV model, the proposed method reduces the relative position errors by 12%, 23%, and 32% in the X, Y, and Z directions, respectively, and that it reduces the relative velocity errors by 350%, 330%, and 300%, respectively. There is a significant improvement in the relative navigation accuracy.

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“…The antenna array anti-jamming technology uses multiple antenna elements. According to the different incident directions of the signal and jamming, interference cancellation is performed using vector weighting, mainly using spatial domain anti-jamming algorithms [24][25][26][27][28][29][30][31][32][33]. Compared with antenna arrays, single antennas are widely used in the drone field due to their small size, low cost, and low power consumption.…”

Section: Introductionmentioning

confidence: 99%

A Study on Anti-Jamming Algorithms in Low-Earth-Orbit Satellite Signal-of-Opportunity Positioning Systems for Unmanned Aerial Vehicles

Yao,

Qin,

et al. 2024

Drones

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Low-Earth-Orbit (LEO) satellite Signal-of-Opportunity (SOP) positioning technology has gradually matured to meet the accuracy requirements for unmanned aerial vehicle (UAV) positioning in daily scenarios. Advancements in miniaturization technology for positioning terminals have also made this technology’s application to UAV positioning crucial for UAV development. However, in the increasingly complex electromagnetic environment, there remains a significant risk of degradation in positioning performance for UAVs in LEO satellite SOP positioning due to unintentional or malicious jamming. Furthermore, there is a lack of in-depth research from scholars both domestically and internationally on the anti-jamming capabilities of LEO satellite SOP positioning technology. Due to significant differences in the downlink signal characteristics between LEO satellites and Global Navigation Satellite System (GNSS) signals based on Medium Earth Orbit (MEO) or Geostationary Earth Orbit (GEO) satellites, the anti-jamming research results of traditional satellite navigation systems cannot be directly applied. This study addresses the narrow bandwidth and high signal-to-noise ratio (SNR) characteristics of signals from LEO satellite constellations. We propose a Consecutive Iteration based on Signal Cancellation (SCCI) algorithm, which significantly reduces errors during the model fitting process. Additionally, an adaptive variable convergence factor was designed to simultaneously balance convergence speed and steady-state error during the iteration process. Compared to traditional algorithms, simulation and experimental results demonstrated that the proposed algorithm enhances the effectiveness of jamming threshold settings under narrow bandwidth and high-power conditions. In the context of LEO satellite jamming scenarios, it improves the frequency-domain anti-jamming performance significantly and holds high application value for drone positioning.

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A Novel Ranging and IMU-Based Method for Relative Positioning of Two-MAV Formation in GNSS-Denied Environments

Cited by 6 publications

References 39 publications

Topology Perception and Relative Positioning of UAV Swarm Formation Based on Low-Rank Optimization

Topology Perception and Relative Positioning of UAV Swarm Formation Based on Low-Rank Optimization

Adaptive UAV Navigation Method Based on AHRS

A Study on Anti-Jamming Algorithms in Low-Earth-Orbit Satellite Signal-of-Opportunity Positioning Systems for Unmanned Aerial Vehicles

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