Optimal State Estimation via Adaptive Fuzzy Particle Filter

• Autorzy: Sąsiadek Jurek , Bitlmal Hamdan Alatresh

Identyfikatory

DOI

10.14313/PAR_250/5

Warianty tytułu

PL

Optymalne szacowanie stanu za pomocą adaptacyjnego filtra cząstek rozmytych

• Języki publikacji: EN

Abstrakty

EN

Particle Filters (PF) accomplish nonlinear system estimation and have received high interest from numerous engineering domains over the past decade. The main problem of PF is to degenerate over time due to the loss of particle diversity. One of the essential causes of losing particle diversity is sample impoverishment (most of particle’s weights are insignificant) which affects the result from the particle depletion in the resampling stage and unsuitable prior information of process and measurement noise. To address this problem, a new Adaptive Fuzzy Particle Filter (AFPF) is used to improve the precision and efficiency of the state estimation results. The error in AFPF state is avoided from diverging by using Fuzzy logic. This method is called tuning weighting factor (α) as output membership function of fuzzy logic and input memberships function is the mean and the covariance of residual error. When the motion model is noisier than measurement, the performance of the proposed method (AFPF) is compared with the standard method (PF) at various particles number. The performance of the proposed method can be compared by keeping the noise level acceptable and convergence of the particle will be measured by the standard deviation. The simulation experiment findings are discussed and evaluated.

PL

Adaptacyjny filtr cząstek rozmytych (AFPF) służy do poprawy precyzji i wydajności wyników szacowania stanu. Metoda ta nazywana jest dostrajaniem współczynnika ważenia (α), ponieważ wyjściowa funkcja przynależności logiki rozmytej, a wejściowa funkcja przynależności jest średnią i kowariancją błędu resztowego. Wydajność proponowanej metody jest porównywana przez utrzymanie dopuszczalnego poziomu hałasu, a zbieżność cząstki będzie mierzona przez odchylenie standardowe. Wyniki eksperymentu symulacyjnego są omawiane i oceniane.

Słowa kluczowe

EN

mobile robot tracking; adaptive fuzzy particle filter; fuzzy logic; sensor fusion

PL

śledzenie robotów mobilnych; adaptacyjny filtr cząstek rozmytych; logika rozmyta; fuzja czujników

• Wydawca: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów PIAP
• Czasopismo: Pomiary Automatyka Robotyka
• Rocznik: 2023
• Tom: R. 27, nr 4
• Strony: 5--12
• Opis fizyczny: Bibliogr. 15 poz., rys., tab., wykr., wzory

Twórcy

autor

Sąsiadek Jurek

• Department of Mechanical and Aerospace Engineering, Carleton University, Ottawa, Ontario, Canada
• CBK PAN, Warsaw, Poland

• https://orcid.org/0000-0003-0455-2745

autor

Bitlmal Hamdan Alatresh

• Department of Mechanical and Aerospace Engineering, Carleton University, Ottawa, Ontario, Canada

• https://orcid.org/0009-0006-3959-0214

Bibliografia

• 1. Evensen G., Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods for forecast error statistics, “Journal of Geophysical Research”, Vol. 99, No. C5, 1994, 10143-10162, DOI: 10.1029/94jc00572.
• 2. Julier S.J., Uhlmann J.K., A new extension of the Kalman filter to nonlinear systems, “Signal Processing, Sensor Fusion, and Target Recognition VI”, Vol. 2, 1997, 54-65, DOI: 10.1117/12.280797.
• 3. Haykin S., Huber K., Chen Z., Bayesian sequential state estimation for MIMO wireless communication, Proceedings of the IEEE, Vol. 92, No. 3, 2004, 439-454, DOI: 10.1109/jproc.2003.823143.
• 4. Van Der Merwe R., Doucet A., De Freitas N., Wan E., The Unscented Particle Filter, Dept. Eng., Univ. Cambridge, Cambridge, U.K., Tech. Rep. CUED/F-INFENG/TR 380, 2000.
• 5. Kotecha J.H., Djuric P.M., Gaussian sum particle filtering, “IEEE Trans. Signal Process”, Vol. 51, No. 10, 2003, 2602-2612, DOI: 10.1109/TSP.2003.816754.
• 6. Musso C., Oudjane N., Gland F., Improving Regularized Particle Filters, Sequential Monte Carlo Methods in Practice, Springer, 2002, 247-271, DOI: 10.1007/978-1-4757-3437-9_12.
• 7. Ding J.W., Tang Y.Q., Liu W., Huang Y.Z., Huang K.Q., Tracking by local structural manifold learning in a new SSIR particle filter, “Neurocomputing”, Vol. 161, 2015, 277-289, DOI: 10.1016/j.neucom.2015.02.027.
• 8. Al-Isawi M.A., Sąsiadek J.Z., Navigation and control of a space robot capturing moving target, “11th International Workshop on Robot Motion and Control (RoMoCo)”, 2017, DOI: 10.1109/RoMoCo.2017.8003908.
• 9. Al-Isawi M.A., Sąsiadek J.Z., Guidance and Control of a Robot Capturing an Uncooperative Space Target, “Journal of Intelligent and Robotic Systems”, Vol. 93 No. 3-4, 2019, 713-721, DOI: 10.1007/s10846-018-0874-9.
• 10. Sasiadek J.Z., Sensor fusion, “Annual Reviews in Control”, Vol. 26, No. 2, 2002, 203-228, DOI: 10.1016/S1367-57-88(02)00045-7.
• 11. Van Trees H.L., Bell K.L., A Tutorial on Particle Filters for Online Nonlinear/NonGaussian Bayesian Tracking, [In:] Bayesian Bounds for Parameter Estimation and Non-linear Filtering/Tracking, IEEE, 2007, 723-737, DOI: 10.1109/9780470544198.ch73.
• 12. Alatresh Bitlmal H., Al-Isawi M.A., Sąsiadek J.Z., Real Time Localization and Mapping of a Mobile Robot using Visual Features, “Proceedings of GSRD International Conference”, 2020.
• 13. Dong X., Ai L., Jiang R., Motion estimation of indoor robot based on image sequences and improved particle filter, “Multimedia Tools and Applications”, Vol. 78, No. 21, 2019, 29747-29763, DOI: 10.1007/s11042-018-6383-9.
• 14. Heilig A., Mamaev I., Hein B., Malov D., Adaptive particle filter for localization problem in service robotics, “MATEC Web of Conferences”, Vol. 161, 01004, 2018, DOI: 10.1051/matecconf/201816101004.
• 15. Yang L., Lu Z., Fuzzy Grey Prediction-Based Particle Filter for Object Tracking, “Mathematical and Computational Applications”, Vol. 21, No. 3, 2016, DOI: 10.3390/mca21030037.