Background: I am interested in localizing a sound source from a suite of audio recorders. Each audio array consists of 6 directional microphones spaced evenly every 60 degrees (0, 60, 120, 180, 240, 300 degrees). I am interested in finding the neighboring pair of microphones with the maximum set of signal strengths. Data consists of a time stamp, antenna number and bearing, and signal strength. Below I have attached a simplified dataset.
df <- data.frame(ant.bearing = seq(0,300, by=60), sig = c(98, 60, 44, 67, 58, 91), ts=1)
Goals: From this dataset, I would like use a function to extract the two neighboring antennas with the maximal set of signal strengths (i.e. antennas with bearings 0 and 300 degrees in above sample code) while accounting for the fact that this data is circular in nature and antennas 0 and 300 are neighbors. Output would be the two rows of data that satisfy the above task e.g. rows 1 and 6 in the above case.
What I've tried:
direction.finder <- function(dat){
# finding bearing with max signal strength
max <- dat[dat$sig == max(dat$sig),][1,]
# finding signal strengths of neighbor antennas and pulling out which has highest
left = dat[dat$ant.bearing==max$ant.bearing-60,]
right = dat[dat$ant.bearing==max$ant.bearing+60,]
if(max$ant.bearing==0)
left = dat[dat$ant.bearing==300,]
if(max$ant.bearing==300)
right = dat[dat$ant.bearing==0,]
sub = right
if(left$sig > right$sig)
sub = left
dat <- rbind(max, sub)
}
This current function serves as an okay workaround for my task but its not ideal. Any suggestions or tips for improving the functionality of my code would be greatly appreciated.
I would compute all the pairs of rows in df:
(pairs <- cbind(1:nrow(df), c(2:nrow(df), 1)))
# [,1] [,2]
# [1,] 1 2
# [2,] 2 3
# [3,] 3 4
# [4,] 4 5
# [5,] 5 6
# [6,] 6 1
You can find the best pairing with which.max:
(best.row <- which.max(df$sig[pairs[,1]] + df$sig[pairs[,2]]))
# [1] 6
Finally, you can look up the corresponding antenna bearings:
df$ant.bearing[pairs[best.row,]]
# [1] 300 0
If you had missing values, you could slightly adjust the code by creating NA values for the missing entries:
# Data
df <- data.frame(ant.bearing = seq(0,180, by=60), sig = c(44, 67,88, 52), ts=2)
# ant.bearing sig ts
# 1 0 44 2
# 2 60 67 2
# 3 120 88 2
# 4 180 52 2
(pairs <- cbind(1:6, c(2:6, 1)))
# [,1] [,2]
# [1,] 1 2
# [2,] 2 3
# [3,] 3 4
# [4,] 4 5
# [5,] 5 6
# [6,] 6 1
sig <- rep(NA, 6)
sig[1+df$ant.bearing/60] <- df$sig
sig
# [1] 44 67 88 52 NA NA
Now the rest of the process is similar:
(best.row <- which.max(sig[pairs[,1]] + sig[pairs[,2]]))
# [1] 2
60*(pairs[best.row,]-1)
# [1] 60 120