Source-reliability-adaptive distributed information fusion

A source-reliability-adaptive distributed non-linear estimation method based on distributed Soft-Data-Constrained Multi-Model Particle Filtering (SDCMMPF) and applicable to a number of distributed state estimation problems is proposed. The proposed method requires only local data exchange among neighbouring sensor nodes, it therefore provides enhanced reliability, scalability, and ease of deployment. In particular, by taking into account the estimate reliability of each sensor node at any point in time, it yields a more robust distributed estimation. To perform the Multi-Model Particle Filtering (MMPF) in an adaptive distributed manner, a Gaussian approximation of the particle cloud obtained at each sensor node along with a weighted Consensus Propagation (CP) based distributed data aggregation scheme are deployed to dynamically re-weight the particles´ weights. The filtering approach in this paper is a soft-data constrained variant of the multi-model particle filter presented in our earlier work, and is capable of processing both soft human-generated data and conventional hard sensory data. In case of permanent noise in the estimation provided by a sensor node, due to either a faulty sensing device or misleading soft data, the contribution of that node in the weighted consensus process is immediately reduced in order to alleviate its effect on the estimation provided by the neighbouring nodes and the entire network. The robustness of the proposed method is demonstrated through simulation results for an agile target tracking task.