smlmkalman (version 0.1.1)

crescent_kf: crescent_kf

Description

A two-dimensional Kalman Filter for use in image segmentation of filamentous structures. Uses a novel "crescent sampling" method to generate the mean z value from local conditions, so time domain can be inferred from local point distribution.

Usage

crescent_kf(x, p_length,
            x_hat_s, sigma_s,
            B_s, d,
            Q, R,
            alpha, beta, gamma,
            overwrite, verbose)

Value

A list containing four elements:

x_hat

A two-column dataframe of x- and y-values predicted by the Kalman Filter.

sigma

A two-column dataframe of covariance error in x and y.

search

A two-column dataframe of z search radius in x and y.

allocated

A three-column dataframe, with the first two columns being equal to the input data (x), and the third column being a numeric ID of unobserved (allocated[,3] = 0) and observed (allocated[,3] >= 1) value, where the value of the ID indicates at which timestep they were observed.

Arguments

x

The dataset to be tracked - a two-column matrix or dataframe of x and y values.

p_length

The maximum number of predictions to be made.

x_hat_s

A vector containing two elements: the initial x- and y-values of the tracking agent.

sigma_s

A vector containing four elements: the initial error covariance matrix of the tracking agent (typically in the format c(x, 0, 0, y), where x and y are the error in x and y, and the non-diagonal elements are assumed to be 0)

B_s

A value from 0 to 360 indicating the initial angle of trajectory of the tracking agent in degrees (0 = east, 90 = north, 180 = west, 270 = south)

d

A value indicating the "step-length" of the Kalman Filter in the B direction at each timestep.

Q

A vector containing four elements: the process noise covariance matrix (typically in the format c(x, 0, 0, y), where x and y are the noise in x and y, and the non-diagonal elements are assumed to be 0)

R

A vector containing four elements: the measurement noise covariance matrix (typically in the format c(x, 0, 0, y), where x and y are the noise in x and y, and the non-diagonal elements are assumed to be 0)

alpha

A value indicating the multiplier for the expansion of the error covariance matrix during z sampling.

beta

A value indicating the minimum number of datapoints in the z sampling crescents which validates the timestep.

gamma

A value indicating the maximum number of rounds of alpha expansion of the error covariance matrix during z sampling (if gamma is reached before the crescent contains datapoints >= beta, the function exits prematurely)

overwrite

A TRUE/FALSE statement which dictates whether or not, after z sampling, the error covariance matrix should be overwritten by the z sampling matrix (FALSE by default, when TRUE may cause unexpected errors/tracking paths!)

verbose

A TRUE/FALSE statement that dictates whether the current place in the function loop should be printed to the terminal.

Author

Andrew Buist

Examples

#Generate loop-de-loop spline
data = as.data.frame(matrix(nrow = 1000, ncol = 3))
data[1:150,3] = 0
data[151:420,3] = c(1:270)
data[421:580,3] = 270
data[581:850,3] = c(270:1)
data[851:1000,3] = 0

data[1,1:2] = c(1,0)

library(spdep)
for(i in 2:1000){
  data[i,1:2] = c(data[(i-1),1] + 1, data[(i-1),2])
  angle =  (data[i,3]*(pi/180))
  data[i,1:2] = (Rotation(as.matrix(data[i,1:2] - data[(i-1),1:2], nrow = 1, ncol = 2), angle)
  + data[(i-1),1:2])
}

#Randomly generate data around spline
data_loopy = as.data.frame(matrix(nrow = 10000, ncol = 2))
for(i in 1:1000){
  data_loopy[((i-1)*10 + 1):(i*10),1] = rnorm(10, data[i,1],5)
  data_loopy[((i-1)*10 + 1):(i*10),2] = rnorm(10, data[i,2],5)
}

#Plot randomly generated data
plot(data_loopy, cex = 0.1)
#Add spline line in red
lines(data[,1:2], col = "red", lwd = 2)

#Peform Kalman Filtering
test =  crescent_kf(x = data_loopy, p_length = 1000, x_hat_s = c(data_loopy[1,1],data_loopy[1,2]),
                    sigma_s = c(10,0,0,10), B_s = 0, d = 0.75,
                    alpha = 1.1, beta = 20, gamma = 15)
#Add prediction line in green
lines(test$x_hat, lwd = 2, col = "green")

#Add legend
legend('topleft', col = c("red", "green"),
       legend = c("Actual", "Predicted"), pch = 16)