UGV‐UAV robust cooperative positioning algorithm with object detection

2021 ◽  
Author(s):  
Dongjia Wang ◽  
Baowang Lian ◽  
Chengkai Tang
Keyword(s):  
Object Detection ◽  
Cooperative Positioning ◽  
Positioning Algorithm

scholarly journals Factor-graph-aided three-dimensional faster cooperative positioning algorithm

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Chengkai Tang ◽  
Jiaqi Liu ◽  
Yi Zhang ◽  
Xingxing Zhu ◽  
Lingling Zhang
Keyword(s):  
Three Dimensional ◽  
Factor Graph ◽  
Cooperative Positioning ◽  
Positioning Algorithm

scholarly journals Factor Graph-Assisted Distributed Cooperative Positioning Algorithm in the GNSS System

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3748 ◽  
Author(s):  
Chengkai Tang ◽  
Lingling Zhang ◽  
Yi Zhang ◽  
Houbing Song
Keyword(s):  
Smart Cities ◽  
Factor Graph ◽  
Positioning Error ◽  
Positioning Accuracy ◽  
Cooperative Positioning ◽  
Measured Range ◽  
Ranging Error ◽  
Positioning Algorithm ◽  
Range Error ◽  
Cooperative Nodes

The development of smart cities calls for improved accuracy in navigation and positioning services; due to the effects of satellite orbit error, ionospheric error, poor quality of navigation signals and so on, it is difficult for existing navigation technology to achieve further improvements in positioning accuracy. Distributed cooperative positioning technology can further improve the accuracy of navigation and positioning with existing GNSS (Global Navigation Satellite System) systems. However, the measured range error and the positioning error of the cooperative nodes exhibit larger reductions in positioning accuracy. In response to this question, this paper proposed a factor graph-aided distributed cooperative positioning algorithm. It establishes the confidence function of factor graphs theory with the ranging error and the positioning error of the coordinated nodes and then fuses the positioning information of the coordinated nodes by the confidence function. It can avoid the influence of positioning error and ranging error and improve the positioning accuracy of cooperative nodes. In the simulation part, the proposed algorithm is compared with a mainly coordinated positioning algorithm from four aspects: the measured range error, positioning error, convergence speed, and mutation error. The simulation results show that the proposed algorithm leads to a 30–60% improvement in positioning accuracy compared with other algorithms under the same measured range error and positioning error. The convergence rate and mutation error elimination times are only 1 / 5 to 1 / 3 of the other algorithms.

scholarly journals Factor Graph Weight Particles aided Distributed Underwater Cooperative Positioning Algorithm

2021 ◽  
Author(s):  
lingling zhang ◽  
baoguo yu ◽  
Chengkai Tang ◽  
yi zhang ◽  
Houbing Song
Keyword(s):  
Power Efficiency ◽  
High Efficiency ◽  
Low Complexity ◽  
Factor Graph ◽  
Network Nodes ◽  
Cooperative Positioning ◽  
Underwater Localization ◽  
Positioning Algorithm ◽  
Marine Exploration ◽  
Real Test

Abstract The growing scale of marine exploration requires high-resolution underwater localization, which necessitates cooperation among underwater network nodes, considering the channel complexity and power efficiency. In this paper, we proposed factor graph weight particles aided distributed underwater nodes cooperative positioning algorithm (WP-DUCP). It capitalized on the factor graph and sum-product algorithm to decompose the global optimization to the product of several local optimization functions. Combined with the Gaussian parameters to construct the weighted particles and to realize the belief transfer, it shows low complexity and high efficiency, suitable to the energy-restricted and communication distance-limited underwater networks. In terms of convergence, localization resolution, and computation complexity, we conducted the simulation and real-test with comparison to the existing co-localization methods. The results verified a higher resolution of the proposed method with no extra computation burden.

scholarly journals An Advanced Cooperative Positioning Algorithm Based on Improved Factor Graph and Sum-Product Theory for Multiple AUVs

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 67006-67017 ◽  
Author(s):  
Shiwei Fan ◽  
Ya Zhang ◽  
Chunyang Yu ◽  
Minghong Zhu ◽  
Fei Yu
Keyword(s):  
Factor Graph ◽  
Cooperative Positioning ◽  
Positioning Algorithm

Analysis of Flying Vehicle Cooperative Positioning by Sea-Surface Wireless Sensor Networks

2012 ◽  
Vol 505 ◽  
pp. 338-344
Author(s):  
Wei Ming Xu ◽  
Xiao Dong Yin ◽  
Geng Feng Wang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Time Synchronization ◽  
Sea Surface ◽  
Wireless Sensor ◽  
Relative Positioning ◽  
Cooperative Positioning ◽  
Positioning Method ◽  
Absolute Bias ◽  
Positioning Algorithm

Sea-surface wireless sensor networks (S2WSN) is a combination of many nodes forming a certain geometric shape, such as ships and sea-surface radio buoys. To satisfy the requirement of precise tracking for flying vehicle (FV) in times of exterior location datum outages, a cooperative positioning algorithm (CPA) for the FV is proposed. Time synchronization among the nodes is crucial to guarantee CPA. Taking a single-hop S2WSN as an example, the problem of low synchronization precision is resolved by two-way timing with unequal reply time (TWT-UTD). Monte Carlo simulation results show that, through optimizing the position dilution of precision among the sea-surface nodes and the FV, the absolute bias of the FV tracking by the proposed CPA is superior to that of the conventional single ship-based relative positioning method. Meanwhile, the synchronization precision is increased by more than 20% via TWT-UTD method.

A Cooperative Positioning Algorithm in Cellular Networks with Hearability Problem

2013 ◽  
Vol 2 (1) ◽  
pp. 66-69 ◽  
Author(s):  
Ziming He ◽  
Abdullah Alonazi ◽  
Yi Ma ◽  
Rahim Tafazolli
Keyword(s):  
Cellular Networks ◽  
Cooperative Positioning ◽  
Positioning Algorithm

A Fast Multidimensional Scaling Filter for Vehicular Cooperative Positioning

2012 ◽  
Vol 65 (2) ◽  
pp. 223-243 ◽  
Author(s):  
Mahmoud Efatmaneshnik ◽  
Nima Alam ◽  
Allison Kealy ◽  
Andrew G Dempster
Keyword(s):  
Vehicular Networks ◽  
Computational Cost ◽  
Communication Technologies ◽  
Convergence Time ◽  
Vehicular Communication ◽  
Major Drawback ◽  
Cooperative Positioning ◽  
Dimensional Scaling ◽  
Positioning Algorithm ◽  
Multi Dimensional Scaling

Vehicular communication technologies are becoming staples of modern societies. This paper proposes a new positioning algorithm for vehicular networks. The algorithm is a non-classic Multi-Dimensional Scaling Filter (MDSF) that builds on a novel and computationally effective Multi-Dimensional Scaling (MDS) solution covariance estimation technique and also a Maximum Likelihood (ML) filter. In general a major drawback of the non-classic MDS is the high computational cost because of its iterative nature. It is shown that a special blend between vehicular Map-Matching (MM) and MDSF considerably reduces the number of iterations and the convergence time, making the MDSF a suitable algorithm for vehicular network positioning. The performance of MDSF is compared with that of an Extended Kalman Filter (EKF) together with the Cramar Rao Lower Bound (CRLB). It is shown through simulation that for all types of traffic conditions MDSF performs better than EKF and closer to CRLB than EKF. It is also shown that both MDSF and EKF algorithms are robust to typical Global Positioning System (GPS) outages in deep urban canyons. CRLB also proves that Cooperative Positioning (CP) in general has the ability to bridge short GPS outages.

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