Conservative Quantization of Covariance Matrices with Applications to Decentralized Information Fusion

Sensors

卷:21

Benjamin Noack¹  Christopher Funk²  UweD. Hanebeck² 

[1] Autonomous Multisensor Systems Group (AMS), Institute for Intelligent Cooperating Systems (ICS), Otto von Guericke University Magdeburg (OVGU), 39106 Magdeburg, Germany;

[2] Intelligent Sensor-Actuator-Systems Laboratory (ISAS), Institute of Anthropomatics and Robotics (IAR), Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany;

关键词: covariance quantization;    decentralized estimation;    conservative fusion;    covariance intersection;    optimal fusion;   

DOI  :  10.3390/s21093059

来源: DOAJ

【 摘 要 】

Information fusion in networked systems poses challenges with respect to both theory and implementation. Limited available bandwidth can become a bottleneck when high-dimensional estimates and associated error covariance matrices need to be transmitted. Compression of estimates and covariance matrices can endanger desirable properties like unbiasedness and may lead to unreliable fusion results. In this work, quantization methods for estimates and covariance matrices are presented and their usage with the optimal fusion formulas and covariance intersection is demonstrated. The proposed quantization methods significantly reduce the bandwidth required for data transmission while retaining unbiasedness and conservativeness of the considered fusion methods. Their performance is evaluated using simulations, showing their effectiveness even in the case of substantial data reduction.

【 授权许可】

Unknown