2014
DOI: 10.1155/2014/562380
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Cooperative Localization of Multi-UAVs via Dynamic Nonparametric Belief Propagation under GPS Signal Loss Condition

Abstract: Self-localization is critical for many unmanned aerial vehicles (UAVs) tasks such as formation flight, path planning, and activity coordination. Traditionally, UAV can locate itself using GPS combined with some inertial sensors. However, due to the complex flight environment or failure of the GPS receiver, the UAV may lose its GPS signal and fail to locate itself, resulting in devastating consequence. In this paper, we will consider the problem of cooperative localization among multiple UAVs, in which the UAVs… Show more

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Cited by 25 publications
(15 citation statements)
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References 19 publications
(50 reference statements)
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“…CP has demonstrated to be useful for positioning of mobile platforms navigating in challenging GNSS, as well as GNSS-denied environments ([1,2,3,4]), where at least some of the nodes can achieve an acceptable level of positioning accuracy using GNSS, while other nodes are in GNSS-denied environment(s), and the nodes in the network are well-connected to each other and inter-node range information is available ([1,2,3,5]). The CP approach relies on information exchange in an inter-connected network of multiple nodes that could be static (called anchor/infrastructure nodes) or dynamic (such as Unmanned Aerial Vehicles (UAVs), pedestrians, cars) in nature ([1,2,3,4,6,7,8,9,10,11,12,13]). Each node shares information about its own state, as well as relative information with respect to its neighbouring nodes.…”
Section: Introductionmentioning
confidence: 99%
“…CP has demonstrated to be useful for positioning of mobile platforms navigating in challenging GNSS, as well as GNSS-denied environments ([1,2,3,4]), where at least some of the nodes can achieve an acceptable level of positioning accuracy using GNSS, while other nodes are in GNSS-denied environment(s), and the nodes in the network are well-connected to each other and inter-node range information is available ([1,2,3,5]). The CP approach relies on information exchange in an inter-connected network of multiple nodes that could be static (called anchor/infrastructure nodes) or dynamic (such as Unmanned Aerial Vehicles (UAVs), pedestrians, cars) in nature ([1,2,3,4,6,7,8,9,10,11,12,13]). Each node shares information about its own state, as well as relative information with respect to its neighbouring nodes.…”
Section: Introductionmentioning
confidence: 99%
“…The shared information in such an inter-connected network is used for localization of all dynamic (or even static) nodes, even in GNSS denied environments. Based on the strategy adopted for processing, a CL framework can be divided into two broad categories: centralized [8][9][10][11] and distributed [11][12][13][14][15][16][17][18][19][20][21][22][23][24]. Centralized networks employ a central processor that is responsible for collecting, computing and communicating the states of all nodes in a network.…”
Section: Introductionmentioning
confidence: 99%
“…An incorrect estimation or ignoring these correlations has been demonstrated to cause divergence problems in distributed systems [12,26,27]. Therefore, specific algorithms such as the Covariance Intersection Filter (CIF) [17,21], Split Covariance Intersection Filter (SCIF) [20], and Belief Propagation (BP) [16,22,23] have been developed that either estimate or avoid the unknown correlation. Filters such as the CIF and SCIF utilize an optimization method to explicitly estimate the correlations in the states of the nodes using covariance minimization of the fused state.…”
Section: Introductionmentioning
confidence: 99%
“…The proprioceptive and relative measurement data, accurate landmark and groundtruth position data from a motion capture system are collected. More details can be 4 The relative measurement improves the position tracking accuracy, but is not able to bound the overall uncertainty [8]. Table 2.…”
Section: Sensor Performance Effect On Similarity Conversionmentioning
confidence: 99%
“…As a novel localization strategy, cooperative localization (CL) provides higher positioning accuracy over independent localization (IL) where each agent solves its localization by itself. In some application scenes, where not all agents have accuracy localization due to the environment or hardware level, the advantages of CL are prominent [1][2][3][4].…”
Section: Introductionmentioning
confidence: 99%