Cooperative Target Localization and Tracking with Incomplete Measurements | Zendy

Yi Zhang | Zendy; Yinya Li | Zendy; Guoqing Qi | Zendy; Andong Sheng | Zendy

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Cooperative Target Localization and Tracking with Incomplete Measurements

Author(s) -

Yi Zhang,

Yinya Li,

Guoqing Qi,

Andong Sheng

Publication year - 2014

Publication title -

international journal of distributed sensor networks

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.324

H-Index - 53

eISSN - 1550-1477

pISSN - 1550-1329

DOI - 10.1155/2014/906764

Subject(s) - computer science , tracking (education) , cramér–rao bound , kalman filter , range (aeronautics) , mean squared error , upper and lower bounds , algorithm , square root , monte carlo method , artificial intelligence , estimation theory , mathematics , statistics , psychology , mathematical analysis , pedagogy , materials science , geometry , composite material

This study investigates a problem on target localization and tracking for two cases where either the slant range information of dual stations is lost or the slant range information of one station and the pitch angle information of the other one are missing. The models of cooperative localization with incomplete measurements are presented and the Kalman filtering algorithm is applied for target tracking. For improving tracking precision, a strategy of observers path planning based on the gradient of circular error probability (CEP) is integrated into the Kalman filtering algorithm. Several numerical examples are used to illustrate the tracking performance of the proposed algorithm with the corresponding root mean square error (RMSE) and Cramer-Rao lower bound (CRLB). The Monte Carlo simulation results validate the effectiveness of the presented algorithm.

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