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Export MUSIC Table from SeaSondeRCS Object

Source: R/SeaSondeRCS_MUSIC.R
seasonder_exportMUSICTable.Rd

This function generates a table containing detailed MUSIC detection data from a SeaSondeRCS object. The output table includes geographic coordinates, signal parameters, and other metadata for each MUSIC detection.

Usage

seasonder_exportMUSICTable(seasonder_cs_object)

Arguments

seasonder_cs_object

A SeaSondeRCS object containing MUSIC detection data and related metadata.

Value

A data frame with the following columns:

  • datetime: Timestamp of the data.

  • longitude: Geographic longitude of the detection.

  • latitude: Geographic latitude of the detection.

  • range_cell: Range cell number.

  • range: Range in kilometers.

  • doppler_bin: Doppler bin number.

  • doppler_freq: Doppler frequency.

  • radial_velocity: Radial velocity in m/s.

  • signal_power: Signal power.

  • bearing: Geographic bearing in degrees.

  • bearing_raw: Original MUSIC bearing in degrees.

  • noise_level: Noise level in dB.

  • signal_power_db: Signal power in dB.

  • SNR: Signal-to-noise ratio in dB.

  • DOA_peak_resp_db: DOA peak response in dB.

Details

This function performs the following operations:

  1. Retrieves the timestamp (nDateTime) from the header of the SeaSondeRCS object. Defaults to as.POSIXct(0) if unavailable.

  2. Initializes an empty data frame with predefined columns.

  3. Retrieves MUSIC detection data, processes the Direction of Arrival (DOA) and geographic coordinates (lonlat), and unnests these fields.

  4. Converts MUSIC bearings to geographic bearings using the associated Antenna Pattern Matrix (APM) object.

  5. Computes additional metrics such as signal power in dB, signal-to-noise ratio (SNR), and DOA peak response in dB.

  6. Appends the timestamp to the table and reorders columns for clarity.

See also

Examples

# \donttest{
  # Load sample CSS and APM files
  cs_file  <- system.file("css_data/CSS_TORA_24_04_04_0700.cs", package = "SeaSondeR")
  apm_file <- system.file("css_data/MeasPattern.txt",       package = "SeaSondeR")
  apm_obj  <- seasonder_readSeaSondeRAPMFile(apm_file)
#> seasonder_createSeaSondeRAPM:  APM object created successfully.
  # Create SeaSondeRCS object with APM
  cs_obj <- seasonder_createSeaSondeRCS(cs_file, seasonder_apm_object = apm_obj)
#> new_SeaSondeRCS:  SeaSondeRCS object created successfully.
FOR <- seasonder_getSeaSondeRCS_FOR(cs_obj)
cs_obj <- seasonder_setSeaSondeRCS_FOR(cs_obj,FOR[4:5])

  # Run MUSIC algorithm (in FOR context) if MUSIC data is available:
  cs_obj <- seasonder_runMUSICInFOR(cs_obj)
#> seasonder_runMUSIC:  MUSIC algorithm started.
#> seasonder_runMUSIC:  MUSIC algorithm finished.
  # Export MUSIC table
  music_table <- seasonder_exportMUSICTable(cs_obj)
  print(music_table)
#> # A tibble: 7 × 15
#>   datetime            longitude latitude range_cell range doppler_bin
#>   <dttm>                  <dbl>    <dbl>      <int> <dbl>       <int>
#> 1 2024-04-04 07:00:00     -8.80     42.2          1 0.187         333
#> 2 2024-04-04 07:00:00     -8.80     42.2          1 0.187         334
#> 3 2024-04-04 07:00:00     -8.80     42.2          1 0.187         334
#> 4 2024-04-04 07:00:00     -8.80     42.2          1 0.187         342
#> 5 2024-04-04 07:00:00     -8.80     42.2          1 0.187         342
#> 6 2024-04-04 07:00:00     -8.80     42.2          1 0.187         343
#> 7 2024-04-04 07:00:00     -8.80     42.2          2 0.374         343
#> # ℹ 9 more variables: doppler_freq <dbl>, radial_velocity <dbl>,
#> #   signal_power <dbl>, bearing <dbl>, bearing_raw <dbl>, noise_level <dbl>,
#> #   signal_power_db <dbl>, SNR <dbl>, DOA_peak_resp_db <dbl>
# }