A design complexity comparison method for loop-based signal processing algorithms: particle filters

This paper presents a method for evaluating design complexity of a class of algorithms with characteristics that are common for many loop-based signal processing real-time applications. The method is not only used for evaluations, but can also be transformed to the actual implementation. The model transforms the data-flow structure of the algorithms to hierarchical pipelined architecture where control structure derivation is straightforward. The proposed method is used to estimate design complexity of two particle filtering algorithm: the sample importance resampling particle filter (SIRF) and the Gaussian Particle Filter (GPF) applied to the bearings-only tracking problem.