An Efficient Particle Filter for the OOSM Problem in Nonlinear Dynamic Systems
Ming Li, Wei Yi, Qi Yang, Lingjiang Kong · 2018
In this paper, the out of sequence measurement (OOSM) problem with arbitrary lags in nonlinear dynamic systems is considered. We develop an efficient particle filtering (E- PF) algorithm based on the exact Bayesian solution. Generally, by introducing some reasonable Gaussian assumptions, a general Gaussian smoother is derived to compute the expected smoothing pdfs instead of using the particle smoother, which makes the E-PF computation efficient and applicable for most nonlinear cases. Meantime, for E-PF, only the estimates and covariances for a predetermined maximum number of lags are stored, the storage resource is also effectively saved. In the simulation, a two-dimensional target tracking example is given, the numerical results show that the tracking performance of our algorithm is quite close to the A-PF algorithm proposed by Zhang et al., while the computation cost is significantly reduced.