Robust all-source positioning of UAVs based on belief propagation

Chen, Xi; Gao, Wenyun; Wang, Jiabo

doi:10.1186/1687-6180-2013-150

Cited by 12 publications

(11 citation statements)

References 31 publications

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“…The fusion algorithm used for jammers measurement plays a critical role in the state-of-the-art swarm UAVs. A large number of scholars in UAVs have conducted relevant research in recent years, including GPS with distance measuring sensors [15] [16], machine learning [10], and filtering techniques [ 17 ]. The researchers have proposed an algorithm of re-localizing UAV with a malfunction in its GPS receiver, which equipped the most of any other healthy UAVs [10].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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Location awareness and navigation promote varieties of emerging applications of mobile collaborative multiple uncrewed aerial vehicles (UAVs). Cooperative UAVs fuse the global position system (GPS), inertial navigation systems (INS), peer to peer ranging radios derived from relative navigation of ultra-wideband (UWB) under complicated environments. Those information sources can be incorporated into spatiotemporal cooperation posited by the intra-user measurement of INS and GPS， and the inter-user measurement of the relative navigation of swarm UAVs. This paper considers the localization and navigation of multiple collaborative UAVs in networks with GPS/INS/UWB jammers in the case that the measurements are missed or randomly delayed by a sampling period. In a navigation situation with a partially denied navigation signals (e.g.GPS Jammers for some UAVs, UWB jammers for others, etc.), we propose an improved method of cooperation location for the swarm, allowing measurement jammers concerning the normal sigma point belief propagation (SPBP). This algorithm integrates message passing based on the Bayesian framework, a sigma point belief propagation of random packet loss (SPBP-RPL) to exploit spatiotemporal cooperation and measurement knowledge. Compared with existing general sigma point belief propagation, the advantages of the novel method are validated through a simulation of swarm UAVs with GPS/INS/UWB. Results show that the algorithm of combining spatiotemporal cooperation with measurement knowledge reduces the location uncertainty of swarm UAVs agents and improves location accuracy remarkably. INDEX TERMS Collaborative networks belief propagation; location awareness and navigation for swarm UAVs; randomly delayed measurements; message-passing; sigma point belief propagation

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Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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“…Therefore, the condition of effective estimation of the target position is limited by the selection of array spacing. On this basis, the maximum entropy algorithm is proposed in [5], the Capon minimum variance algorithm is proposed in [6] and the Pisarenko harmonic decomposition algorithm is proposed in [7], etc. Although these algorithms improve the azimuth resolution to a certain extent, they have a few shortcomings, such as low efficiency, poor timeliness, etc.…”

Section: Introductionmentioning

confidence: 99%

Gaussian Parameterized Information Aided Distributed Cooperative Underwater Positioning Algorithm

Zhang

Tang²,

Chen

et al. 2020

IEEE Access

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With the exploration of marine resources, the number of underwater nodes is increasing with each passing day, and the demand of mutual cooperative position between nodes is becoming extremely urgent. The existing underwater positioning technology is mainly realized by transplanting cooperative position technology from ground space, without taking into consideration the consequent problems of complex underwater acoustic channels, large signal propagation attenuation and the limited load of underwater nodes. According to the characteristics of the underwater cooperative position network, this paper proposes gaussian parameterized information aided distributed cooperative positioning algorithm (GPI-CP) with underwater environment. The algorithm utilized here is the form of Gauss parameter to calculate information and establish a confidence model. In order to reduce the computational complexity, the Taylor model was adopted to linear approximate European distance. Then distributed underwater node cooperative position is obtained by combing with the factor map theory. This algorithm combined with the sum-product theory and parameterized Gaussian information transmission, is promising to realize fast underwater cooperative position and reduce communication calculations. The proposed algorithm was simulated and analyzed in terms of location ambiguity, ranging error and the number of nodes, etc. When the proposed algorithm was compared with the existing underwater cooperative position method, it was noticed that the position performance had improved more than 10% and the communication calculation is less.

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“…have been used in the distributed estimation architecture. The work for distributed cooperative localization using ground based robots seems to be very extensive (Shi et al 2010, Carrillo-Arce et al 2013, Wanasinghe et al 2014, Savic and Zazo 2013, Nerukar et al 2009, Madhavan et al 2002, Pillonetto and Carpin 2007,Bailey et al 2011 but the work for airborne platforms seems to be somewhat limited (Indelman et al 2012, Qu et al 2010, Qu and Zhang 2011, Melnyk et al 2012, Chen et al 2013, Wan et al 2015. Some of the researchers (Indelman et al 2012, Melnyk et al 2012) have attempted to use vision based sensors for cooperative localization.…”

Section: Introductionmentioning

confidence: 99%

Cooperative Uas Localization Using Lowcost Sensors

Goel

Kealy²,

Lohani

2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Networks of small, low cost Unmanned Aerial Systems (UASs) have the potential to improve responsiveness and situational awareness across an increasing number of applications including defense, surveillance, mapping, search and rescue, disaster management, mineral exploration, assisted guidance and navigation etc. These ad hoc UAS networks typically have the capability to communicate with each other and can share data between the individual UAS nodes. Thus these networks can operate as robust and efficient information acquisition platforms. For any of the applications involving UASs, a primary requirement is the localization i.e. determining the position and orientation of the UAS. The performance requirements of localization can vary with individual applications, for example: mapping applications need much higher localization accuracy as compared to the applications involving only surveillance. The sharing of appropriate data between UASs can prove to be advantageous when compared to a single UAS, in terms of improving the positioning accuracy and reliability particularly in partially or completely GNSS denied environments. This research aims to integrate low cost positioning sensors and cooperative localization technique for a network of UASs. Our hypothesis is that it is possible to achieve high accurate, real-time localization of each of the nodes in the network even with cheaper sensors if the nodes of the network share information among themselves. This hypothesis is validated using simulations and the results are analyzed both for centralized and distributed estimation architectures. At first, the results are studied for a two node network which is then expanded for a network containing more number of nodes. Having more nodes in the network allows us to study the properties of the network including the effect of size and shape of the network on accuracy of the nodes.

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Robust all-source positioning of UAVs based on belief propagation

Cited by 12 publications

References 31 publications

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

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

Gaussian Parameterized Information Aided Distributed Cooperative Underwater Positioning Algorithm

Cooperative Uas Localization Using Lowcost Sensors

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