Funk Christopher, Noack Benjamin, Hanebeck Uwe D
Intelligent Sensor-Actuator-Systems Laboratory (ISAS), Institute of Anthropomatics and Robotics (IAR), Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany.
Autonomous Multisensor Systems Group (AMS), Institute for Intelligent Cooperating Systems (ICS), Otto von Guericke University Magdeburg (OVGU), 39106 Magdeburg, Germany.
Sensors (Basel). 2021 Apr 28;21(9):3059. doi: 10.3390/s21093059.
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.
网络系统中的信息融合在理论和实现方面都带来了挑战。当需要传输高维估计值和相关误差协方差矩阵时，有限的可用带宽可能会成为瓶颈。估计值和协方差矩阵的压缩可能会危及诸如无偏性等理想特性，并可能导致不可靠的融合结果。在这项工作中，提出了估计值和协方差矩阵的量化方法，并展示了它们与最优融合公式和协方差交集的用法。所提出的量化方法在保留所考虑融合方法的无偏性和保守性的同时，显著降低了数据传输所需的带宽。通过仿真评估了它们的性能，结果表明即使在大量数据缩减的情况下它们也很有效。
