The Research on Distributed Fusion Estimation Based on Machine Learning

Abstract

Multi-sensor distributed fusion estimation algorithms based on machine learning are proposed in this paper. Firstly, using local estimations as inputs and estimations of three classic distributed fusion (weighted by matrices, by diagonal matrices and by scalars) as the training sets, three distributed fusion algorithms based on BP network (BP net-based fusion weighted by matrices, by diagonal matrices and by scalar) are proposed and the selection basis of the number of nodes in hidden layer is given. Furthermore, by using local estimations as inputs and centralized fusion estimation as training set, another recurrent netbased distributed fusion algorithm is proposed, in the case that neither true states nor cross-covariance matrices is available. This method is not limited to the linear minimum variance (LMV) criterion, so its accuracy is higher than the classical three distributed fusion algorithms. A radar tracking simulation verifies the effectiveness of the proposed fusion networks. INDEX TERMS Distributed fusion, machine learning, recurrent networks, BP network.

The covariance intersection fusion estimation algorithm weighted by diagonal matrix based on genetic simulated annealing algorithm and machine learning

“…Cybenko et al analyze the approximation performance of BP network and prove that the continuous feedforward neural networks with single hidden layer and sigmoid transfer function can approximate any complex continuous mapping with arbitrary precision [54]. Peng et al [55] take the BP network as the fusion center and proposed a distributed fusion algorithm based on machine learning and hidden layer selection rules. BP is suitable for fusion problems.…”