ekf_toa_robust: ekf_toa

In Mufabo/Rrobustsp: Robust Signal Processing

ekf_toa

Description

Robust EKF for tracking with time-of-arrival (:= ToA) estimates based on M-estimation.

Usage

ekf_toa_robust(r_ges, theta_init, BS, parameter = NULL)

Arguments

r_ges	: measured distances as a M x N matrix
theta_init	: initial state estimate
BS	: base station positions
parameter	:

Value

th_hat : state estimates

P_min : apriori covariance

P : aposteriors covariance

References

"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.

Examples

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_robust(tmp$measureddistances, theta_init, tmp$BS, ekf)

Mufabo/Rrobustsp documentation built on June 11, 2022, 10:41 p.m.