Documentation
Skaha-Drone
Skaha-IrrigationLand-Surface ModelSkaha-DroneSkaha-Survey
Skaha-Drone
Skaha-IrrigationLand-Surface ModelSkaha-DroneSkaha-Survey
  • Introduction: Digital L-Band (1.4 GHz) Polarimeter for UAVs
  • Microwave Polarimetry
    • Radio Wave Polarization
  • Technical Description
    • Mounting Options
    • Antenna
    • Control Unit
    • RF Signal Chain
    • A/D Converter and Digital Correlator
    • Receiver Noise Temperature
    • Internal Calibration
    • Radio Interference Filter
  • Ground Station and User Interface
    • Ground Station
    • Labels and Tags
    • Status
    • Settings
    • Rawdata
    • Map
    • Internal
  • Online Processing
    • Introduction
    • Sanity Check and Filtering
    • Gain Calibration
    • Conversion to Volumetric Water Content
    • Data Storage in Google Drive
  • Working with the Sensor and Data
    • HDF5 File Structure
    • Python Scripts
    • Test Observations
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  1. Online Processing

Gain Calibration

Second step of the data processing chain.

PreviousSanity Check and FilteringNextConversion to Volumetric Water Content

Last updated 1 year ago

A gain calibration, as described , is performed. First, the internal calibration load is applied, with t_kelvin being the physical temperature of the calibration load:

data_hh = t_kelvin * statistics.median(np.array(data[2][ind]) / np.array(data[0][ind]))
data_vv = t_kelvin * statistics.median(np.array(data[3][ind]) / np.array(data[1][ind]))
data_u = t_kelvin * statistics.median(np.array(data[4][ind]) / (np.sqrt(np.array(data[0][ind])) * np.sqrt(np.array(data[1][ind]))))
data_v = t_kelvin * statistics.median(np.array(data[5][ind]) / (np.sqrt(np.array(data[0][ind])) * np.sqrt(np.array(data[1][ind]))))

And then gain drifts due to variations in the receiver temperature are removed, with t being the physical temperature of the PCB (we use the physical temperature of the calibration load as it is measured on the PCB) in degrees Celsius:

LNA_fact_hh = -0.0004132 * data_hh + 0.4057
LNA_fact_vv = -0.0004132 * data_vv + 0.4057
offset_hh = LNA_fact_hh * t
offset_vv = LNA_fact_vv * t
data_hh -= offset_hh
data_vv -= offset_vv

data_hh2 = 1.778 * data_hh - 175.9
data_vv2 = 1.778 * data_vv - 175.9
here