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Browse filesThe RadPat-50K dataset can be regenerated from first principles using standard array factor formulations. The following procedure was used:
1. Environment Setup
Install Python (≥3.9) along with the required packages: numpy, scipy, matplotlib, and pandas.
2. Array Configuration
A uniform linear array (ULA) was considered with varying numbers of elements and inter-element spacings. The number of elements was selected from
N = {4, 8, 12, 16, 24, 32, 48, 64}.
Inter-element spacings were chosen from values such as 0.25λ, 0.5λ, 0.75λ, and 1λ.
Steering angles were selected from −60°, −45°, −30°, −15°, 0°, 15°, 30°, 45°, and 60°.
3. Weighting Schemes
Multiple excitation tapers were applied to control sidelobe levels and beamwidth. The following weighting functions were used: uniform, binomial, cosine, Kaiser, Hamming, Hann, Blackman, and exponential.
4. Pattern Computation
For each configuration, the array factor was computed and radiation patterns were generated in both rectangular (dB vs. angle) and polar coordinates. Derived antenna parameters such as gain, directivity, and half-power beamwidth (HPBW) were extracted. Sidelobe levels (SLL) were not included.
5. Variations for Diversity
To simulate practical conditions, additional variations were introduced:
o ⚡ Amplitude noise
o 📐 Phase noise
o 🎯 Steering jitter
For these variations, only CSV metadata files were generated
- Metadata_10K.csv +0 -0
- Metadata_50K.csv +0 -0
- Polar_10K.zip +3 -0
- Polar_50K.zip +3 -0
- Rect_10K.zip +3 -0
- Rect_50K.zip +3 -0
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