P2P Ranging-Based Cooperative Localization Method for a Cluster of Mobile Nodes Containing IR-UWB PHY

Abstract In Wireless Sensor Networks(WSNs), the location services are the basis of many application scenarios. However, for the range-based localization method, the localization accuracy and the system robustness of the distributed localization system are difficult to guarantee, due to the uncertainty of the distance estimation and position calculation are affected by the node state and communication uncertainty. In this paper, we propose the distributed localization method based on anchor node selection and Particle Filter optimization. In this method, we analyze the uncertainty of error propagation in the Least-squares method and find that there is a proportional relation between localization error and uncertainty propagation. According to this relationship, we propose the corresponding optimization criterion methods of anchor nodes. To optimize the initial localization results, we present the distributed localization method based on anchor node optimal selection and Particle Filter. Simulation results show that the methods we proposed could effectively improve the localization accuracy of the mobile nodes and the robustness of the system.

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Cooperative Localization in Wireless Networks

Advances in Wireless Technologies and Telecommunication - Positioning and Navigation in Complex Environments ◽

10.4018/978-1-5225-3528-7.ch006 ◽

2018 ◽

pp. 232-273 ◽

Cited By ~ 1

Author(s):

Yunlong Wang ◽

Yang Cai ◽

Yuan Shen

Keyword(s):

Wireless Networks ◽

Lower Bound ◽

Wireless Network ◽

Distributed Algorithms ◽

High Accuracy ◽

Location Based Services ◽

Cooperative Localization ◽

Mobile Nodes ◽

Position Information ◽

Localization Algorithms

This chapter describes cooperative localization in wireless networks, where mobile nodes with unknown positions jointly infer their positions through measuring and exchanging information with each other. The technique of cooperation localization, efficiently even in harsh propagation environment, enables amounts of location-based services that rely on high-accuracy position information of mobile nodes. After a brief introduction of cooperative localization, the Cramer-Rao lower bound is given as a standard metric for performance. Then the information in the temporal and spatial domain is illustrated with geometrical interpretations. Two classes of cooperative localization algorithms, namely, centralized and distributed algorithms, are presented to show the implementation of the cooperative localization in a wireless network. Then the performance of cooperative localization under non-line-of-sight condition is analyzed. Lastly, numerical results are given to illustrate the performance of cooperative localization algorithms.

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A Gossip-Based AOA Distributed Localization Algorithm for Wireless Sensor Networks

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.577.841 ◽

2014 ◽

Vol 577 ◽

pp. 841-846 ◽

Cited By ~ 1

Author(s):

Wen Cui ◽

Shao Chuan Wu ◽

Yu Ze Wang

Keyword(s):

Wireless Sensor Networks ◽

Sensor Networks ◽

Wireless Sensor ◽

Cooperative Localization ◽

Central Processor ◽

Localization Algorithm ◽

Angle Of Arrival ◽

Position Information ◽

Localization Method ◽

Gossip Algorithm

Position information of individual nodes is useful in implementing functions such as routing in wireless sensor networks. Distributed localization method has been widely concerned in the case of no central processor is available to handle the calculations in the networks. In this paper, we focus on distributed localization techniques based on angle of arrival information between neighboring nodes. A novel distributed cooperative localization method based on gossip algorithm is proposed, which can obviously improve the positioning performance of the previous methods.

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Cooperative localization method for multi-robot based on PF-EKF

Science in China Series F Information Sciences ◽

10.1007/s11432-008-0041-1 ◽

2008 ◽

Vol 51 (8) ◽

pp. 1125-1137 ◽

Cited By ~ 4

Author(s):

Ling Wang ◽

JianWei Wan ◽

YunHui Liu ◽

JinXin Shao

Keyword(s):

Cooperative Localization ◽

Localization Method ◽

Multi Robot

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A Novel Cooperative Localization Method Based on IMU and UWB

Sensors ◽

10.3390/s20020467 ◽

2020 ◽

Vol 20 (2) ◽

pp. 467 ◽

Cited By ~ 3

Author(s):

Yongqiang Han ◽

Chenchen Wei ◽

Rong Li ◽

Jingzhe Wang ◽

Huan Yu

Keyword(s):

Particle Filter ◽

Dead Reckoning ◽

Outdoor Environment ◽

Ultra Wideband ◽

Measurement Unit ◽

Cooperative Localization ◽

Signal Loss ◽

Localization Method ◽

Comparison Results ◽

Minimum Number

In this paper, a range-based cooperative localization method is proposed for multiple platforms of various structures. The localization system of an independent platform might degrade or fail due to various reasons such as GPS signal-loss, inertial measurement unit (IMU) accumulative errors, or emergency reboot. It is a promising approach to solve this problem by using information from neighboring platforms, thus forming a cooperative localization network that can improve the navigational robustness of each platform. Typical ranging-based ultra-wideband (UWB) cooperative localization systems require at least three auxiliary nodes to estimate the pose of the target node, which is often hard to meet especially in outdoor environment. In this work, we propose a novel IMU/UWB-based cooperative localization solution, which requires a minimum number of auxiliary nodes that is down to 1. An Adaptive Ant Colony Optimization Particle Filter (AACOPF) algorithm is customized to integrate the dead reckoning (DR) system and auxiliary nodes information with no prior information required, resulting in accurate pose estimation, while to our knowledge the azimuth have not been estimated in cooperative localization for the insufficient observation of the system. We have given the condition when azimuth and localization are solvable by analysis and by experiment. The feasibility of the proposed approach is evaluated through two filed experiments: car-to-trolley and car-to-pedestrian cooperative localization. The comparison results also demonstrate that ACOPF-based integration is better than other filter-based methods such as Extended Kalman Filter (EKF) and traditional Particle Filter (PF).

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