Radar Data Dealiasing

This repository provides MATLAB scripts for radar data dealiasing, focusing on processing velocity (vel) and reflectivity (dbz) data. The main objective is to correct aliased velocity data caused by the Nyquist velocity limit, using algorithms techniques for post-processing.

Key Features

• Dealiasing Algorithms: Correct aliased velocity data caused by the Nyquist velocity limit.
• Expansion and Filtering: Includes neighborhood-based filtering and expansion algorithms for better continuity.
• FFT Analysis: Leverages FFT to identify and correct phase and amplitude inconsistencies.
• Interactive Visualization: Optional interactive mode for step-by-step visualization.
• Comprehensive Functionality: Modular functions for preprocessing, processing, and analysis.

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How It Works

1. Aliased Velocity Data

Aliased velocity occurs when radar measurements exceed the Nyquist limit, resulting in discontinuous sinusoidal patterns.

2. Velocity Data de-aliasing

Velocity Data is post processed through two (2) algorithms. The first is a collection of mathematical approaches, and the second is a geometrical approach.

3. Dealiased Velocity Data

The dealiasing process corrects these patterns, restoring accurate velocity measurements.

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Repository Contents

Main Script

• main_script.m: The main entry point for running the dealiasing workflow. It includes:
  • Initial preprocessing of radar data (vel, dbz, lat, lon, etc.).
  • Execution of the dealiasing algorithms (dealise_velocities).
  • Generation of plots and visualizations for aliased and dealiased velocities.

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Functions

Core Algorithms

• dealise_velocities.m: Combines mathematical and expansion algorithms for comprehensive dealiasing.
• dealise_filter.m: Applies initial sine-based dealiasing.
• correctAmplitude360Optimized.m: Corrects 360-degree amplitude wrapping to maintain phase continuity.
• deleted_rays.m: Identifies and removes problematic rays.

Filtering and Expansion

• speed.m: Filters reflectivity (dbz) based on velocity data (vel).
• box_filter.m: Applies a neighborhood-based filter to smooth velocity and reflectivity data.
• expansion_algorithm.m: Expands valid velocity regions into surrounding NaN areas.

Data Preparation and Reconstruction

• make_full.m: Fills gaps in the velocity field.
• locate_initial.m: Aligns the initial velocity profile for consistent reconstruction.
• calc_perimeters.m: Computes the perimeter of regions for velocity expansion.
• identifyBaselineRays.m: Identifies key rays for baseline corrections.

Auxiliary Functions

• sine_folding.m: Evaluates sine fitting and folding for dealiasing.
• sine_folding_results.m / sine_folding_results_plus90.m: Generates folded sine waves for comparison.
• eliminate_isolated.m: Removes isolated points in the velocity matrix.
• find_best_shift.m: Finds optimal shifts for aligning velocity data.

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Data Requirements

Example:

• sample_data.mat: Contains variables:
  • vel: Aliased velocity matrix.
  • dbz: Reflectivity matrix.
  • lat, lon: Latitude and longitude for visualization.
  • Azimuth, Distance, Elevation: Radar metadata.

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Usage

Sample Input Data

Prepared radar data in .mat file:

load('sample_data.mat');

Run the Main Script

Execute the dealiasing workflow by running:

main_script;

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Visualization

Figures and Plots

The script generates visualizations for:

1. Aliased Velocities: Initial radar velocities with aliasing.
2. Dealiased Velocities: Corrected velocities using mathematical and expansion algorithms.
3. Phases and Amplitudes: Visual comparisons of FFT-derived phases and amplitudes.
4. Step-by-Step Sine Visualization: Interactive plots for analyzing aliased and dealiased sines row by row.

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Contribution

Feel free to contribute to this repository by:

• Improving the existing algorithms.
• Adding new features for enhanced dealiasing or visualization.
• Reporting issues or suggesting improvements.