Abstract
Collaborative positioning in many applications has broad prospects especially in the complex and weak environment. However, complicated and changeable environment has brought challenges to robust and precision fusion filter methods. To this end, this paper put forward the collaborative positioning algorithm based on adaptive Kalman filtering (CPAKF) according to the maximum likelihood criterion which can adaptively adjust process noise covariance and observation noise covariance, make the fusion filtering adapt to the changeable and complex noise environment, and have a certain anti-interference performance. Then, the pollution collaborative positioning algorithm (PCP) is presented which can achieve isolation of pollution nodes, make the other nodes clear by collaborative positioning and improve the accuracy of all peer nodes in the network ultimately. Simulation analysis of multi-use standalone as well as collaborative positioning based on the traditional kalman and adaptive kalman filtering. Compared to the traditional standalone kalman-based positioning algorithm (SKF), the collaborative positioning algorithm based on adaptive kalman filtering (CPAKF) is much better. Besides, the PCP with much smother curve can avoid pollution nodes affecting others which performs best among three positioning algorithms.
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References
Mauricio AC, Francesco S, Roberto G et al (2010) Spirito, hybrid GNSS-ToA localization and tracking via cooperative unscented kalman filter. In: IEEE 21st international symposium on personal, indoor and mobile radio communications workshops, pp 272–276
Huang B, Yao Z, Cui X et al (2015) Dilution of precision analysis for GNSS collaborative positioning. IEEE Trans Veh Technol 64(5):427–437
Mourikis AI, Roumeliotis SI (2006) Performance analysis of multi-robot cooperative localization. IEEE Trans Robot 22(4):666–681
Cardinali R, De Nardis L, Di Benedetto MG et al (2006) UWB ranging accuracy in high- and low-data-rate applications. IEEE Trans Microwave Theory Tech 54:1865–1875
Garello R, Lo Presti L, Corazza GE et al (2012) Peer-to-peer cooperative positioning part I: GNSS-aided acquisition. InsideGNSS 7(2):55–63
Garello R, Samson J, Spirito MA et al (2012) Peer-to-peer cooperative positioning part II: hybrid devices with GNSS and terrestrial ranging capability. InsideGNSS 7(4)
Caceres MA, Penna F, Wymeersch H et al (2011) Hybrid cooperative positioning based on distributed belief propagation. IEEE J Sel Areas Commun 29(10):1948–1958
Wymeersch H, Lien J, Win MZ (2009) Cooperative localization in wireless networks. Proc IEEE 97(2):427–450
Acknowledgments
We are grateful to the reviewers for their comments and suggestions. This work was supported by the key laboratory project of science and technology innovation of Shaanxi Province (Grant no. 2013SZS15-K01).
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© 2016 Springer Science+Business Media Singapore
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Zhang, L., Lian, B., Yan, H. (2016). Research on Ranging/GNSS Localization Based on Pollution Collaborative Positioning via Adaptive Kalman Filter. In: Sun, J., Liu, J., Fan, S., Wang, F. (eds) China Satellite Navigation Conference (CSNC) 2016 Proceedings: Volume II. Lecture Notes in Electrical Engineering, vol 389. Springer, Singapore. https://doi.org/10.1007/978-981-10-0937-2_30
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DOI: https://doi.org/10.1007/978-981-10-0937-2_30
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