first draft related work

tex/egbib.bib

@@ -2718,9 +2718,40 @@ number = {3},
 pages = {495--513},
 shorttitle = {Proceedings of the IEEE},
 title = {{Data Fusion for Visual Tracking with Particles}},
-url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=1271403},
 volume = {92},
 year = {2004}
 }
 
+@article{Hu2014,
+abstract = {This work addresses the problem of predicting the Remaining Useful Life (RUL) of components for which a mathematical model describing the component degradation is available, but the values of the model parameters are not known and the observations of degradation trajectories in similar components are unavailable. The proposed approach solves this problem by using a Particle Filtering (PF) technique combined with a kernel smoothing (KS) method. This PF-KS method can simultaneously estimate the degradation state and the unknown parameters in the degradation model, while significantly overcoming the problem of particle impoverishment. Based on the updated degradation model (where the unknown parameters are replaced by the estimated ones), the RUL prediction is then performed by simulating future particles evolutions. A numerical application regarding prognostics for Lithium-ion batteries is considered. Various performance indicators measuring precision, accuracy, steadiness and risk of the obtained RUL predictions are computed. The obtained results show that the proposed PF-KS method can provide more satisfactory results than the traditional PF methods.},
+author = {Hu, Yang and Baraldi, Piero and {Di Maio}, Francesco and Zio, Enrico},
+doi = {10.1016/j.ress.2014.10.003},
+file = {:home/toni/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Hu et al. - 2015 - A particle filtering and kernel smoothing-based approach for new design component prognostics.pdf:pdf},
+issn = {09518320},
+journal = {Reliability Engineering and System Safety},
+keywords = {Battery,Kernel smoothing,Parameter estimation,Particle filtering,Prognostics,Remaining useful life},
+IGNOREmonth = {feb},
+pages = {19--31},
+title = {{A Particle filtering and Kernel Smoothing-Based Approach for New Design Component Prognostics}},
+volume = {134},
+year = {2014}
+}
+
+@article{Paul2009,
+abstract = {Solutions for indoor tracking and localization have become more critical with recent advancement in context and location-aware technologies. The accuracy of explicit positioning sensors such as global positioning system (GPS) is often limited for indoor environments. In this paper, we evaluate the feasibility of building an indoor location tracking system that is cost effective for large scale deployments, can operate over existing Wi-Fi networks, and can provide flexibility to accommodate new sensor observations as they become available. This paper proposes a sigma-point Kalman smoother (SPKS)-based location and tracking algorithm as a superior alternative for indoor positioning. The proposed SPKS fuses a dynamic model of human walking with a number of low-cost sensor observations to track 2-D position and velocity. Available sensors include Wi-Fi received signal strength indication (RSSI), binary infra-red (IR) motion sensors, and binary foot-switches. Wi-Fi signal strength is measured using a receiver tag developed by Ekahau, Inc. The performance of the proposed algorithm is compared with a commercially available positioning engine, also developed by Ekahau, Inc. The superior accuracy of our approach over a number of trials is demonstrated.},
+author = {Paul, Anindya S. and Wan, Eric A.},
+doi = {10.1109/JSTSP.2009.2032309},
+isbn = {1932-4553},
+issn = {19324553},
+journal = {IEEE Journal on Selected Topics in Signal Processing},
+keywords = {Bayesian inference,Indoor tracking,Received signal strength indication (RSSI)-based localization,Sigma-point Kalman filter,Sigma-point Kalman smoother,State estimation},
+IGNOREmonth = {oct},
+number = {5},
+pages = {860--873},
+shorttitle = {IEEE Journal of Selected Topics in Signal Processi},
+title = {{RSSI-Based Indoor Localization and Tracking using Sigma-Point Kalman Smoothers}},
+volume = {3},
+year = {2009}
+}
+