Abstract

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).

Highlights

  • The cooperative operation among manned and unmanned platforms is becoming increasingly demanding with the development of navigation, communication, and intelligent control technologies, etc

  • To qualitatively and quantitatively analyse the observability of our cooperative localization system, different from the method in Fallon’s paper, we applied a Piece-Wise Constant System (PWCS) method to analyze the observability of the cooperative navigation system, which can qualitatively shows the observability based on the rank of Observability Matrix (OM), and quantitatively presents the degree of observability based on its eigenvalues

  • When auxiliary node 2 is added with the peak shift error, auxiliary node 3 is added with non-Gaussian noise or the noise that exceeds the measurement error threshold, the localization errors and azimuth estimation errors of the pedestrian in Figure 13 show that Adaptive Ant Colony Optimization Particle Filter (AACOPF) convergences faster than Particle Filter (PF), and the localization accuracy is higher than PF

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Summary

Introduction

The cooperative operation among manned and unmanned platforms is becoming increasingly demanding with the development of navigation, communication, and intelligent control technologies, etc. Reference high [12] uses of geometric translationfeatures and rotation to estimate positioning error the positioning error of in aircraft These commonly employed location methods nodes entail location methods entail additional nodes, causing waste of resources and introducing extra additional auxiliary nodes, causing auxiliary waste of resources and introducing extra costs, computational load, costs, computationalload load,ofand and communication thecommunication entire network. A cooperative localization method with only one auxiliary node is proposed based on the Adaptive Ant Colony Optimization Particle Filter (AACOPF) [22] and dead reckoning (DR) [23]. When the topology network containing more than one auxiliary node, the particle filter is able to perform collaborative localization by virtue of adaptive weight adjustment, no extra treatment is needed with the addition of other sensors, thereby permitting a plug and play mechanism, as opposed to traditional schemes bases on the EKF and UKF which, in this situation, necessitate re-linearization or even remodelling. To qualitatively and quantitatively analyse the observability of our cooperative localization system, different from the method in Fallon’s paper, we applied a Piece-Wise Constant System (PWCS) method to analyze the observability of the cooperative navigation system, which can qualitatively shows the observability based on the rank of Observability Matrix (OM), and quantitatively presents the degree of observability based on its eigenvalues

Adaptive Cooperative Localization Problem
Kinematic Model
Measurement Model Based on UWB
Algorithm Overview
Experimental Sets
Car-to-Trolley
Results
The with trolley trolley
Conclusions
Full Text
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