Conservative Quantization of Covariance Matrices with Applications to Decentralized Information Fusion

Abstract

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.

Gaussian Mixture Cardinalized Probability Hypothesis Density(GM-CPHD): A Distributed Filter Based on the Intersection of Parallel Inverse Covariances

“…In parallel, relevant scholars also carried out relevant research on the application of covariance intersection to the above-mentioned filter. At this stage, covariance intersection is mainly applied to the Kalman filter [ 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 ]. Qi, W. [ 25 ] (2020) applied BCI fusion and fast SCI fusion to a time-varying Kalman filter in their research and suggested that this method should solve the high-dimensional nonlinear optimization problem; however, the algorithm’s operation implies great computational complexity and quantity.…”