An improved MCB localization algorithm based on weighted RSSI and motion prediction | Zendy

Chunyue Zhou | Zendy; Hui Tian | Zendy; Baitong Zhong | Zendy

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An improved MCB localization algorithm based on weighted RSSI and motion prediction

Author(s) -

Chunyue Zhou,

Hui Tian,

Baitong Zhong

Publication year - 2020

Publication title -

computer science and information systems

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.244

H-Index - 24

eISSN - 2406-1018

pISSN - 1820-0214

DOI - 10.2298/csis200204020z

Subject(s) - ranging , computer science , node (physics) , sampling (signal processing) , monte carlo method , algorithm , position (finance) , monte carlo localization , signal strength , importance sampling , signal (programming language) , artificial intelligence , wireless sensor network , computer vision , statistics , mathematics , telecommunications , particle filter , computer network , structural engineering , filter (signal processing) , finance , engineering , economics , programming language , kalman filter

Aiming at the problem of low sampling efficiency and high demand for anchor node density of traditional Monte Carlo Localization Boxed algorithm, an improved algorithm based on historical anchor node information and the received signal strength indicator (RSSI) ranging weight is proposed which can effectively constrain sampling area of the node to be located. Moreover, the RSSI ranging of the surrounding anchors and the neighbor nodes is used to provide references for the position sampling weights of the nodes to be located, an improved motion model is proposed to further restrict the sampling area in direction. The simulation results show that the improved Monte Carlo Localization Boxed (IMCB) algorithm effectively improves the accuracy and efficiency of localization.

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