Title :
Improving the Gaussian Sum Particle filtering by MMSE constraint
Author_Institution :
Xian Inst. of Post & Telecommun., Xian
Abstract :
In the paper, we proposed a new modification to the Gaussian sum particle filtering (GSPF) by incorporating a minimum mean square error constraint (MMSE-GSPF) in tracking parameter variation of dynamic state space model, the algorithm complexity is reduced yet. In addition, the MMSE-GSPF also shows better performance in tracking of time-varying parameters than the original Gaussian sum particle filtering.
Keywords :
Gaussian processes; least mean squares methods; particle filtering (numerical methods); Gaussian sum particle filtering; MMSE constraint; dynamic state space model; minimum mean square error constraint; time-varying parameter; Decision support systems; Filtering algorithms; Gaussian noise; Hidden Markov models; Mean square error methods; Particle tracking; Predictive models; Signal processing algorithms; State estimation; State-space methods; Dynamic State Space model; Gaussian Sum Particle filtering; Gaussian Sum filtering; MMSE-GSPF;
Conference_Titel :
Signal Processing, 2008. ICSP 2008. 9th International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-2178-7
Electronic_ISBN :
978-1-4244-2179-4
DOI :
10.1109/ICOSP.2008.4697062
