openSkies (version 1.2.1)

findFlightPhases: Find the phases of a flight based on altitude, vertical rate and speed

Description

Identifies the different phases of a flight based on the altitude, vertical rate and speed of the aircraft reported in a time series of state vectors. Identification of flight phases is performed using a fuzzy logic approach as described in https://arc.aiaa.org/doi/10.2514/1.I010520. Currently, five different phases are considered: ground, climb, cruise, descent and level flight. Flight phase identification can also be performed by calling the detect_phases method of an openSkiesFlight object.

Usage

findFlightPhases(times, altitudes, verticalRates, speeds, window=60)

Value

A character vector where each element indicates the phase corresponding to each of the time points.

Arguments

times

vector of times in seconds corresponding to the altitude, vertical rate and speed values.

altitudes

vector of altitude values in meters

verticalRates

vector of vertical rate values in meters/second.

.

speeds

vector of speed values (i.e., the speed at which the aircraft is moving with respect to the ground) in meters/second

.

window

time window in seconds to compute mean values before detecting flight phases. It is recommended to apply a window in order to reduce the impact of spurious wrong values, but window application can be effectively turned off by setting this argument to 1

.

Examples

# In the following example, we will retrieve all state vectors for a flight 
# along route SCX624, from Harlingen to Minneapolis. We will then identify 
# the different phases of the flight, and plot it together with altitude values.
# Note that when retrieving the state vectors, the username and password should 
# be substituted by your own, for which you should have received authorization 
# to access the OpenSky Trino interface

if (FALSE) {
state_vectors <- getIntervalStateVectors(aircraft = "ab3da7",
                                         startTime = "2021-12-12 04:20:00",
                                         endTime = "2021-12-12 07:40:00",
                                         username="your_username",
                                         password="your_password")

flights <- state_vectors$split_into_flights()
length(flights)

# Only one flight identified in the time period, as expected

flight <- flights[[1]]

# Let's extract the data required for detection of flight phases

data <- flight$state_vectors$get_values(c("requested_time", "baro_altitude",
                                          "vertical_rate", "velocity"))
data$requested_time <- data$requested_time - data$requested_time[1]

# We can now identify flight phases. We will use a time window of 60 s

phases <- findFlightPhases(times=data$requested_time,
                           altitudes=data$baro_altitude,
                           verticalRates=data$vertical_rate,
                           speeds=data$velocity,
                           window=60)

# We can now plot the phases together with the altitude values

library(ggplot2)
data <- cbind(data, phases)
ggplot(data[!is.na(data$baro_altitude), ], aes(x = requested_time, y = baro_altitude)) +
    geom_line() +
    geom_point(aes(color=phases))     
}