2.1 KaltoaClockSync
KaltoaClockSync objects hold correction values that can be used to synchronize receiver clocks within an array.
Additionally, it can also hold receiver position corrections.
Below is a plot of our receiver array.
Generally, it’s best to use a single receiver as a reference clock.
Here, we’ll use the center receiver 62074, as this has good connectivity to all the surrounding ones.
# Show connectivity
# (number of detections between receiver pairs)
SyncTagConnectivity(exdata_clocksync) |> summary()## receive_id
## send_id 62041 62042 62074 62368 62581 62584
## 62041 0 123 114 114 111 52
## 62042 118 0 107 78 106 89
## 62074 117 118 0 110 109 133
## 62368 117 95 110 0 111 131
## 62581 112 114 111 113 0 15
## 62584 110 119 111 113 95 0
We’ll need to specify a data.frame providing the order for the pairwise clock drift corrections, sync_order, and a vector of times to calculate drift corrections for, offset_times.
# Create a data.frame holding the order of paired clock synchronizations
sync_order = data.frame(
emit = c("62074", "62074", "62074", "62074", "62368"),
detect = c("62368", "62581", "62041", "62042", "62584"))
sync_order## emit detect
## 1 62074 62368
## 2 62074 62581
## 3 62074 62041
## 4 62074 62042
## 5 62368 62584The receivers listed column detect will be synchronized to match the clocks in their emit column on the same row.
Here, receiver "62074" will be used as a reference for the first four receivers "62581", "62368", "62041", and "62042".
After those pairwise synchronizations, receiver "62368" will then be used as a reference to calibrate "62584".
Note that the order matters here, and all receivers in your array should be included in your sync_order table as either a reference—column emit—or a clock to be corrected—column detect.
Take your time on setting up the sync_order table.
Receivers pairs with poor sync-tag connectivity will result in poor quality drift corrections.
# Make a vector of times to calculate clock drift corrections for.
offset_times <- seq(
as.POSIXct("2021-05-08 18:00:00", tz = "CET"),
as.POSIXct("2021-05-09 06:00:00", tz = "CET"), by = 3600)
offset_times## [1] "2021-05-08 18:00:00 CEST" "2021-05-08 19:00:00 CEST"
## [3] "2021-05-08 20:00:00 CEST" "2021-05-08 21:00:00 CEST"
## [5] "2021-05-08 22:00:00 CEST" "2021-05-08 23:00:00 CEST"
## [7] "2021-05-09 00:00:00 CEST" "2021-05-09 01:00:00 CEST"
## [9] "2021-05-09 02:00:00 CEST" "2021-05-09 03:00:00 CEST"
## [11] "2021-05-09 04:00:00 CEST" "2021-05-09 05:00:00 CEST"
## [13] "2021-05-09 06:00:00 CEST"Next, we’ll create the KaltoaClockSync model.
This holds our parameters for clock synchronization.
# Transmission are assumed to travel no farther
# than 500 meters
max_trns_lat <- 500 / 1500
# Initialize model
sync_model <- KaltoaClockSync(
sync_order = sync_order,
max_transmission_latency = max_trns_lat,
clock_times = offset_times,
detlst = exdata_clocksync)
print(sync_model)## KaltoaClockSync Object
## --- Sync Order
## 1: 62074 ⟶ 62368
## 2: 62074 ⟶ 62581
## 3: 62074 ⟶ 62041
## 4: 62074 ⟶ 62042
## 5: 62368 ⟶ 62584
##
## --- Parameters
## Max allowable transmission latency: 0.3 s
## Detection error beta: 32.0
## Detection-emission grouping threshold: 0.5 s
## Transmission speed: 1500.0 m s⁻¹
##
## 13 clock times ranging from 2021-05-08 18:00:00 CEST to 2021-05-09 06:00:00 CEST
##
## --- Clock drift ranges
## 62074⟶62368: 0.0000 s -- 0.0000 s
## 62074⟶62581: 0.0000 s -- 0.0000 s
## 62074⟶62041: 0.0000 s -- 0.0000 s
## 62074⟶62042: 0.0000 s -- 0.0000 s
## 62368⟶62584: 0.0000 s -- 0.0000 s
##
## --- Clock offsets
## 62041: 0.0000 s
## 62042: 0.0000 s
## 62074: 0.0000 s
## 62368: 0.0000 s
## 62581: 0.0000 s
## 62584: 0.0000 s
##
## --- Position offsets
## 62041: 0.0 m, 0.0 m
## 62042: 0.0 m, 0.0 m
## 62074: 0.0 m, 0.0 m
## 62368: 0.0 m, 0.0 m
## 62581: 0.0 m, 0.0 m
## 62584: 0.0 m, 0.0 mKaltoaClockSync objects have a number of convenience functions that will allow us to check the effects of our corrections on the underlying sync-tag data set.
We’ll see examples of these in the following sections.