Decorrelation-Based Data Association Algorithm for Heterogeneous Sensors System

Autor: Rong Bo Ke, Yun Bo Kong, Guo Sheng Chen, Hua Bing Wang, Chuan Guo Lu, Xin Xi Feng
Rok vydání: 2014
Předmět:
Zdroj: Applied Mechanics and Materials. 722:334-337
ISSN: 1662-7482
DOI: 10.4028/www.scientific.net/amm.722.334
Popis: A decorrelation-based data association algorithm for heterogeneous sensors system is proposed, which first fusions multiple measurements to estimate a target position using the pseudo linear estimation method, then a decorrelation-based data association model is built and the unscented transform is proposed to compute the mutual covariance between measurements and the pseudo ones. Meanwhile, taking the real-time problem into consideration, when calculating the cross covariance, the minimum skew simplex sample is used to reduce the complexity of the algorithm.
Databáze: OpenAIRE