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NawfalMotii79
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import numpy as np
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import pandas as pd
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import math
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def generate_radar_csv(filename="pulse_compression_output.csv"):
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"""
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Generate realistic radar CSV data for testing the Python GUI
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"""
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# Radar parameters matching your testbench
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num_long_chirps = 16
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num_short_chirps = 16
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samples_per_chirp = 512 # Reduced for manageable file size
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fs_adc = 400e6 # 400 MHz ADC
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timestamp_ns = 0
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# Target parameters
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targets = [
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{'range': 3000, 'velocity': 25, 'snr': 30, 'azimuth': 10, 'elevation': 5}, # Fast moving target
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{'range': 5000, 'velocity': -15, 'snr': 25, 'azimuth': 20, 'elevation': 2}, # Approaching target
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{'range': 8000, 'velocity': 5, 'snr': 20, 'azimuth': 30, 'elevation': 8}, # Slow moving target
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{'range': 12000, 'velocity': -8, 'snr': 18, 'azimuth': 45, 'elevation': 3}, # Distant target
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]
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# Noise parameters
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noise_std = 5
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clutter_std = 10
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data = []
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chirp_number = 0
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# Generate Long Chirps (30µs duration equivalent)
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print("Generating Long Chirps...")
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for chirp in range(num_long_chirps):
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for sample in range(samples_per_chirp):
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# Base noise
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i_val = np.random.normal(0, noise_std)
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q_val = np.random.normal(0, noise_std)
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# Add clutter (stationary targets)
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clutter_range = 2000 # Fixed clutter at 2km
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if sample < 100: # Simulate clutter in first 100 samples
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i_val += np.random.normal(0, clutter_std)
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q_val += np.random.normal(0, clutter_std)
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# Add moving targets with Doppler shift
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for target in targets:
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# Calculate range bin (simplified)
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range_bin = int(target['range'] / 20) # ~20m per bin
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doppler_phase = 2 * math.pi * target['velocity'] * chirp / 100 # Doppler phase shift
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# Target appears around its range bin with some spread
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if abs(sample - range_bin) < 10:
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# Signal amplitude decreases with range
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amplitude = target['snr'] * (10000 / target['range'])
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phase = 2 * math.pi * sample / 50 + doppler_phase
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i_val += amplitude * math.cos(phase)
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q_val += amplitude * math.sin(phase)
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# Calculate derived values
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magnitude_squared = i_val**2 + q_val**2
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data.append({
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'timestamp_ns': timestamp_ns,
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'chirp_number': chirp_number,
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'chirp_type': 'LONG',
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'sample_index': sample,
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'I_value': int(i_val),
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'Q_value': int(q_val),
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'magnitude_squared': int(magnitude_squared)
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})
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timestamp_ns += int(1e9 / fs_adc) # 2.5ns per sample at 400MHz
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chirp_number += 1
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timestamp_ns += 137000 # Add listening period (137µs)
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# Guard time between long and short chirps
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timestamp_ns += 175400 # 175.4µs guard time
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# Generate Short Chirps (0.5µs duration equivalent)
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print("Generating Short Chirps...")
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for chirp in range(num_short_chirps):
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for sample in range(samples_per_chirp):
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# Base noise
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i_val = np.random.normal(0, noise_std)
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q_val = np.random.normal(0, noise_std)
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# Add clutter (different characteristics for short chirps)
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if sample < 50: # Less clutter for short chirps
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i_val += np.random.normal(0, clutter_std/2)
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q_val += np.random.normal(0, clutter_std/2)
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# Add moving targets with different Doppler for short chirps
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for target in targets:
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# Range bin calculation (different for short chirps)
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range_bin = int(target['range'] / 40) # Different range resolution
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doppler_phase = 2 * math.pi * target['velocity'] * (chirp + 5) / 80 # Different Doppler
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# Target appears around its range bin
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if abs(sample - range_bin) < 8:
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# Different amplitude characteristics for short chirps
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amplitude = target['snr'] * 0.7 * (8000 / target['range']) # 70% amplitude
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phase = 2 * math.pi * sample / 30 + doppler_phase
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i_val += amplitude * math.cos(phase)
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q_val += amplitude * math.sin(phase)
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# Calculate derived values
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magnitude_squared = i_val**2 + q_val**2
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data.append({
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'timestamp_ns': timestamp_ns,
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'chirp_number': chirp_number,
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'chirp_type': 'SHORT',
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'sample_index': sample,
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'I_value': int(i_val),
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'Q_value': int(q_val),
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'magnitude_squared': int(magnitude_squared)
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})
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timestamp_ns += int(1e9 / fs_adc) # 2.5ns per sample
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chirp_number += 1
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timestamp_ns += 174500 # Add listening period (174.5µs)
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# Create DataFrame
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df = pd.DataFrame(data)
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# Save to CSV
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df.to_csv(filename, index=False)
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print(f"Generated CSV file: {filename}")
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print(f"Total samples: {len(df)}")
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print(f"Long chirps: {num_long_chirps}, Short chirps: {num_short_chirps}")
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print(f"Samples per chirp: {samples_per_chirp}")
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print(f"File size: {len(df) // 1000}K samples")
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return df
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def analyze_generated_data(df):
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"""
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Analyze the generated data to verify target detection
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"""
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print("\n=== Data Analysis ===")
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# Basic statistics
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long_chirps = df[df['chirp_type'] == 'LONG']
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short_chirps = df[df['chirp_type'] == 'SHORT']
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print(f"Long chirp samples: {len(long_chirps)}")
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print(f"Short chirp samples: {len(short_chirps)}")
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print(f"Unique chirp numbers: {df['chirp_number'].nunique()}")
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# Calculate actual magnitude and phase for analysis
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df['magnitude'] = np.sqrt(df['I_value']**2 + df['Q_value']**2)
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df['phase_rad'] = np.arctan2(df['Q_value'], df['I_value'])
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# Find high-magnitude samples (potential targets)
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high_mag_threshold = df['magnitude'].quantile(0.95) # Top 5%
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targets_detected = df[df['magnitude'] > high_mag_threshold]
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print(f"\nTarget detection threshold: {high_mag_threshold:.2f}")
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print(f"High magnitude samples: {len(targets_detected)}")
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# Group by chirp type
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long_targets = targets_detected[targets_detected['chirp_type'] == 'LONG']
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short_targets = targets_detected[targets_detected['chirp_type'] == 'SHORT']
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print(f"Targets in long chirps: {len(long_targets)}")
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print(f"Targets in short chirps: {len(short_targets)}")
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return df
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if __name__ == "__main__":
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# Generate the CSV file
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df = generate_radar_csv("test_radar_data.csv")
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# Analyze the generated data
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analyze_generated_data(df)
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print("\n=== CSV File Ready ===")
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print("You can now test the Python GUI with this CSV file!")
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print("The file contains:")
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print("- 16 Long chirps + 16 Short chirps")
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print("- 4 simulated targets at different ranges and velocities")
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print("- Realistic noise and clutter")
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print("- Proper I/Q data for Doppler processing")
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