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‎MATLAB/antenna_array_processing.m‎

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‎MATLAB/process_experimental_data.m‎

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%% ===================================================================== %%
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% ---------------------- RFID CSV DATA PROCESSING ---------------------- %
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% Author: Nedal M. Benelmekki %
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% Date (DD/MM/YYYY): 11/07/2025 %
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% %
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% Description: %
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% Batch process RFID AoA experiment CSV files, obtained from a COTS RFID %
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% System (Zebra FX7500, AN480 WB Antenna, Belt tag) into a structured %
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% MATLAB dataset. %
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% %
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% Input: %
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% - Filepath to the experimental data's base directory. %
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% %
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% Output: %
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% - rfid_array_data.mat: Structured MATLAB dataset containing processed %
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% phase, RSSI, and phasor data for each measurement configuration %
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% ======================================================================= %
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%% STEP 0: Clean Workspace
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clear; % Clear all variables from workspace
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clc; % Clear console/command window
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%% STEP 1: Configuration
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dataDir = 'filepath'; % TODO: Set base directory path for CSV files
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tagID = '000233b2ddd9014000000000'; % Target RFID tag ID
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c = 3e8; % Speed of light approximation [m/s]
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% Alt.: c = physconst('LightSpeed'); % Speed of light, requires Antenna Toolbox [m/s]
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%% STEP 2: Load Files
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fileList = dir(fullfile(dataDir, '**', '*.csv')); % Recursive search for files in subdirectories, see README.md documentation to understand the file structure and naming convention
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fprintf('Found %d CSV files.\n', length(fileList)); % Debug message: display number of files found
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%% STEP 3: Transfer Data
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allData = []; % Initialize storage array
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for i = 1:length(fileList) % Loop through each found file in STEP 2.
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fname = fileList(i).name; % Get the filename
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fpath = fullfile(fileList(i).folder, fname); % Full path to the file
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try
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% STEP 3.1: Parse filename
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tokens = regexp(fname, '(\d+)_(\d+\.\d)_(\d+\.\d+)_(\d+\.\d+)_(\-?\d+\.\d+).csv', 'tokens'); % Regex to extract date, f0, D, L, W from filename
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if isempty(tokens) % Check if regex matched + error handling
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warning('Skipping file (cannot parse): %s', fname);
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continue;
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end
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tokens = tokens{1}; % Extract tokens from the cell array
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dateStr = tokens{1}; % Date string in format YYYYMMDD
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f0 = str2double(tokens{2}) * 1e6; % Carrier frequency, f0, in Hz
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D = str2double(tokens{3}); % Vertical distance, D, in m
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L = str2double(tokens{4}); % Antenna separation, L, in m
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W = str2double(tokens{5}); % Horizontal tag position, W, in m
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lambda = c / f0; % Wavelength, lambda, in m
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% STEP 3.2: Load CSV
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T = readtable(fpath); % Load CSV file into a table
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% STEP 3.3: Check required columns
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requiredCols = {'phase', 'peakRssi', 'antenna', 'idHex'}; % Define required columns
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if ~all(ismember(requiredCols, T.Properties.VariableNames)) % Verify if all required columns are present
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warning('Skipping file (missing columns): %s', fname); % Error handling (skip the file)
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continue;
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end
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% STEP 3.4: Filter by antenna and tag ID
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t1 = T(T.antenna == 1 & strcmp(T.idHex, tagID), :); % Filter for Antenna 1
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t2 = T(T.antenna == 2 & strcmp(T.idHex, tagID), :); % Filter for Antenna 2
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% STEP 3.5: Sanity check
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if height(t1) < 3 || height(t2) < 3 % Ensure there are enough data points for processing
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warning('Not enough data in file: %s', fname); % Error handling (skip the file)
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continue;
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end
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% STEP 3.6: Unwrap and convert phase to radians
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phi1 = unwrap(deg2rad(t1.phase)); % Unwrap phase for Antenna 1 and convert to radians
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phi2 = unwrap(deg2rad(t2.phase)); % Unwrap phase for Antenna 2 and convert to radians
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% STEP 3.7: Convert RSSI to linear power scale
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rssi1 = t1.peakRssi; % Peak RSSI for Antenna 1
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rssi2 = t2.peakRssi; % Peak RSSI for Antenna 2
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mag1 = sqrt(10.^(rssi1 / 10)); % Convert RSSI to linear scale for Antenna 1
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mag2 = sqrt(10.^(rssi2 / 10)); % Convert RSSI to linear scale for Antenna 2
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% STEP 3.8: Create phasors
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phasor1 = mag1 .* exp(1j * phi1); % Create phasor for Antenna 1
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phasor2 = mag2 .* exp(1j * phi2); % Create phasor for Antenna 2
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% STEP 3.9: Save entry
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entry = struct(); % Initialize a new entry structure
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entry.filename = fname; % Store filename
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entry.date = dateStr; % Store date string
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entry.f0 = f0; % Store carrier frequency
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entry.lambda = lambda; % Store wavelength
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entry.D = D; % Store vertical distance
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entry.L = L; % Store antenna separation
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entry.W = W; % Store horizontal tag position (offset)
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entry.phi1 = phi1; % Store unwrapped phase for Antenna 1
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entry.phi2 = phi2; % Store unwrapped phase for Antenna 2
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entry.rssi1 = rssi1; % Store RSSI for Antenna 1
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entry.rssi2 = rssi2; % Store RSSI for Antenna 2
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entry.phasor1 = phasor1; % Store phasor for Antenna 1
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entry.phasor2 = phasor2; % Store phasor for Antenna 2
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% STEP 3.10: Append to array + Confirmation
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allData = [allData; entry]; % Append the new entry to the allData array
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fprintf('Processed: %s\n', fname); % Debug message: display processed file name
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catch ME
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warning('Error processing file %s: %s', fname, ME.message); % Error handling: catch any errors during processing
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continue;
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end
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end
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%% STEP 4: Save Output
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save('rfid_array_data.mat', 'allData');
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fprintf('\n Saved processed data to rfid_array_data.mat (%d valid entries).\n', length(allData));

‎README.md‎

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# Bayesian-Enhanced-AoA-Estimator
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AoA estimator for passive UHF RFID based on Bayesian regression and classical antenna array signal processing. Combines physics-informed priors with Pyro-based uncertainty quantification.
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## 📊 Dataset Structure
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### 📂 File Naming Convention
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All measurement files follow this standardized naming pattern:
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**File Naming Convention**:
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`YYYYMMDD_FFF.F_D.DDD_L.LLL_W.WWW.csv`
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**Explanation of Components**:
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- `YYYYMMDD` — Date of the experiment (for tracking only; does not affect the measurement).
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- `FFF.F` — Operating frequency in MHz (e.g., 865.7 for 865.7 MHz). Used to compute λ.
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- `D.DDD` — Vertical distance `D` in meters (e.g., 0.700 for 0.700 m).
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- `L.LLL` — Inter-antenna spacing `L` in meters (e.g., 0.287 for 0.287 m).
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- `W.WWW` — Horizontal offset `W` in meters (can be negative, zero, or positive).
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---
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### 📁 Directory Structure
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The dataset is organized into a hierarchical directory structure as follows:
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```
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Distance 1/
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├── Replica 1/
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│ ├── Frequency 1/
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│ ├── Frequency 2/
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│ ├── Frequency 3/
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│ └── Frequency 4/
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├── Replica 2/
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│ ├── Frequency 1/
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│ ├── Frequency 2/
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│ ├── Frequency 3/
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│ └── Frequency 4/
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└── Replica 3/
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├── Frequency 1/
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├── Frequency 2/
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├── Frequency 3/
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└── Frequency 4/
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Distance 2/
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├── Replica 1/
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│ ├── Frequency 1/
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│ ├── Frequency 2/
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│ ├── Frequency 3/
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│ └── Frequency 4/
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├── Replica 2/
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│ ├── Frequency 1/
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│ ├── Frequency 2/
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│ ├── Frequency 3/
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│ └── Frequency 4/
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└── Replica 3/
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├── Frequency 1/
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├── Frequency 2/
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├── Frequency 3/
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└── Frequency 4/
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Distance 3/
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├── Replica 1/
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│ ├── Frequency 1/
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│ ├── Frequency 2/
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│ ├── Frequency 3/
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│ └── Frequency 4/
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├── Replica 2/
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│ ├── Frequency 1/
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│ ├── Frequency 2/
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│ ├── Frequency 3/
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│ └── Frequency 4/
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└── Replica 3/
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├── Frequency 1/
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├── Frequency 2/
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├── Frequency 3/
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└── Frequency 4/
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```
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**Explanation**:
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- **Distance X/**: Represents different vertical distances `D`.
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- **Replica X/**: Represents repeated measurements for the same distance.
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- **Frequency X/**: Represents measurements taken at different operating frequencies.
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## 🧮 MATLAB Implementation
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The repository contains MATLAB scripts for processing RFID data and implementing various AoA estimation algorithms:
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### 📄 `process_experimental_data.m`
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A preprocessing script that:
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- Batch processes RFID experiment CSV files from a COTS RFID system (Zebra FX7500, AN480 WB Antenna, Belt tag)
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- Parses filenames to extract experimental parameters (frequency, distance, antenna spacing, etc.)
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- Unwraps phase measurements and converts to radians
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- Transforms RSSI values to linear power scale
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- Creates complex phasors for antenna signals
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- Organizes data into a structured MATLAB dataset (`rfid_array_data.mat`)
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### 📄 `antenna_array_processing.m`
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A comprehensive end-to-end RFID AoA estimation pipeline that:
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- Implements multiple estimation methods:
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- Phase-difference estimation
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- Classical Delay-and-Sum beamforming (unweighted & RSSI-weighted)
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- MUSIC algorithm for high-resolution AoA
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- Multi-frequency fusion with confidence metrics
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- Provides extensive visualization:
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- AoA vs. tag position plots
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- Spectral analysis and comparison
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- 3D beam pattern visualization
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- Heatmap representations
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- Performs error analysis and method comparison
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- Outputs organized figures and complete analysis reports
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## 📁 Repository Structure
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The repository is organized with the following key directories:
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### `/MATLAB`
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Contains all MATLAB implementation scripts:
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- `process_experimental_data.m`: Preprocessing script for raw CSV data
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- `antenna_array_processing.m`: Complete end-to-end AoA analysis pipeline
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### `/figures`
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Stores generated visualization outputs from the analysis:
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- AoA estimation plots
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- Spectral analysis visualizations
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- 3D beam pattern representations
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- Method comparison charts
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- Error analysis visualizations
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Example figures are included to demonstrate the expected output format.
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### `/results`
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Contains processed data and analysis results:
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- `rfid_array_data.mat`: Preprocessed dataset ready for analysis
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- `complete_analysis.mat`: Comprehensive results from all estimation methods
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- Performance metrics and statistical evaluations
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Example result files are provided to illustrate the data structure.

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