Abstract: The present techniques relate to a method for a parallel implementation of a sequential Monte Carlo (SMC) method of modelling an industrial process on a distributed memory architecture, and a system for implementing the same. The method may comprise receiving, from at least one sensor, a measurement of at least one parameter within the physical system, wherein the at least one parameter is related to the true state of the physical system; and implementing, on a server comprising a distributed memory architecture, a sequential Monte Carlo (SMC) process using a plurality of statistically independent particles and the at least one measured parameter to estimate the true state of the physical system, wherein the distributed memory architecture has a plurality of cores each of which are ranked. The method and architecture provide an efficient parallel implementation by effectively parallelising a redistribute step which may be considered to be a constituent part of a resampling step.

Abstract: Signal processing with reduced combinatorial complexity for tracking evolving phenomena such as radar tracks associated with weighted measurement parameters includes selecting a current phenomenon and obtaining a set of measurement parameters associated with it. Beginning at a start node providing a first parent node having an identity, an identity for a child node of the patent is produced from the sets of parameters, the parent identity and a parameter selected from the set and corresponding to the child. This is iterated for other parameters in the set. Child nodes of like identity for the phenomenon are treated as a single node with multiple parameter relationships associated with at lest one parent node, whereas child nodes with differing identities are represented as separate nodes. The process is iterated for other phenomena and associated sets of measurement parameters, but child nodes of a previously processed phenomenon are not treated as parent nodes of a phenomenon processed immediately following.