View source: R/robustFiltering_ekfToA.R
ekf_toa | R Documentation |
EKF for tracking with time-of-arrival (:= ToA) estimates
ekf_toa(r_ges, theta_init, BS, parameter = NULL)
r_ges |
: measured distances as a M x N matrix |
theta_init |
: initial state estimate |
BS |
: base station positions |
parameter |
: |
th_hat : state estimates
P_min : apriori covariance
P : aposteriors covariance
File location: robustFiltering_ekfToA.R
"Robust Statistics for Signal Processing" Zoubir, A.M. and Koivunen, V. and Ollila, E. and Muma, M. Cambridge University Press, 2018.
"Robust Tracking and Geolocation for Wireless Networks in NLOS Environments." Hammes, U., Wolsztynski, E., and Zoubir, A.M. IEEE Journal on Selected Topics in Signal Processing, 3(5), 889-901, 2009.
library(zeallot) library(Matrix) library(MASS) library(pracma) library(tensorA) library(Rrobustsp) data("robfilexamp") data("ekf_parameter") ekf <- tmp$ekf rekf <- tmp$rekf names(ekf) <- dimnames(ekf)[[1]] names(rekf)<- dimnames(rekf)[[1]] theta_init <- tmp$theta.init rekf$break.cond <- rekf$'break'[1,1] rekf$c1 <- rekf$c1[1,1] rekf$c2 <- rekf$c2[1,1] rekf$var.est <- rekf$var.est[1,1] rekf$dim <- rekf$dim[1,1] rekf$max.iters <- rekf$max.iters[1, 1] rekf$x1 <- rekf$x1[1,1] # %<-% is the unpacking assignment from library zeallot c(ekf_th, p_th, pm_th, param_th) %<-% ekf_toa(tmp$measureddistances, theta_init, tmp$BS, ekf)
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