RadPat-50K: A Large-Scale Benchmark for Antenna Array Radiation Patterns
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2025
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| _version_ | 1866902257841209344 |
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| author | Dr.Bhuma, Chandra Mohan Dr.Sure, Pallaviram Dr.Painam, Surendra Kumar Dr.Chandrasekhar Rao, Jetti Dr.U, Srinivasa Rao |
| author_facet | Dr.Bhuma, Chandra Mohan Dr.Sure, Pallaviram Dr.Painam, Surendra Kumar Dr.Chandrasekhar Rao, Jetti Dr.U, Srinivasa Rao |
| contents | <p><strong><span> </span></strong><strong><span> RadPat-50K: A Large-Scale Benchmark for Antenna Array Radiation Patterns</span></strong></p> <p><strong><span>RadPat-50K is a large-scale synthetic dataset of 50,000 radiation patterns generated for Uniform Linear Arrays (ULA).<br>Each sample includes polar plots and rectangular plots of the array factor, along with metadata for design and performance parameters.</span></strong></p> <p><strong><span> </span></strong><strong><span> Dataset Highlights</span></strong></p> <ul> <li><strong><span>Size: 50,000 radiation patterns</span></strong></li> <li><strong><span>Array Elements (N): 4, 8, 12, 16, 24, 32, 48, 64</span></strong></li> <li><strong><span>Element Spacing (λ): 0.25, 0.5, 0.75, 1.0</span></strong></li> <li><strong><span>Steering Angles (°): –60, –45, –30, –15, 0, 15, 30, 45, 60</span></strong></li> <li><strong><span>Weighting Schemes: </span></strong> <ul> <li><strong><span>Uniform</span></strong></li> <li><strong><span>Binomial</span></strong></li> <li><strong><span>Cosine</span></strong></li> <li><strong><span>Kaiser</span></strong></li> <li><strong><span>Hamming</span></strong></li> <li><strong><span>Hann</span></strong></li> <li><strong><span>Blackman</span></strong></li> <li><strong><span>Exponential</span></strong></li> </ul> </li> <li><strong><span>Variations (for diversity):</span></strong> <ul> <li><strong><span>⚡</span></strong><strong><span> Amplitude noise</span></strong></li> <li><strong><span> </span></strong><strong><span> Phase noise</span></strong></li> <li><strong><span> </span></strong><strong><span> Steering jitter</span></strong></li> </ul> </li> </ul> <p><strong><span> </span></strong><strong><span> Applications</span></strong></p> <p><strong><span>This dataset is well-suited for research in machine learning, deep learning, and signal processing, including:</span></strong></p> <ul> <li><strong><span> </span></strong><strong><span> Antenna pattern classification</span></strong></li> <li><strong><span> ️</span></strong><strong><span> Beamforming analysis</span></strong></li> <li><strong><span> </span></strong><strong><span> Grating lobe detection</span></strong></li> <li><strong><span> ️</span></strong><strong><span> Data-driven array design</span></strong></li> </ul> <div> </div> <p><strong><span> </span></strong><strong><span> Metadata per Record</span></strong></p> <p><strong><span>Each radiation pattern entry includes:</span></strong></p> <ul> <li><strong><span>Antenna parameters: number of elements, element spacing, steering angle, weighting scheme</span></strong></li> <li><strong><span>Noise parameters: amplitude noise, phase noise, steering jitter</span></strong></li> </ul> <p><strong><span> </span></strong><strong><span> Performance Metrics</span></strong></p> <ul> <li><span> </span><span> <strong>Directivity</strong></span></li> <li><span> </span><span> <strong>Half-Power Beamwidth (HPBW)</strong></span></li> <li><strong><span><span> </span><span> Main Lobe Angle</span></span></strong> <p> </p> </li> </ul> <p><strong><span> </span></strong><strong><span> Benchmark Potential</span></strong></p> <p><strong><span>RadPat-50K provides a standardized benchmark for:</span></strong></p> <ul> <li> <ul> <li><strong><span>Training and evaluating classification & regression models</span></strong></li> <li><strong><span>Developing robust antenna array designs under noise conditions</span></strong></li> <li><strong><span>Advancing AI-driven RF and antenna research</span></strong></li> </ul> </li> </ul> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17067881 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | RadPat-50K: A Large-Scale Benchmark for Antenna Array Radiation Patterns Dr.Bhuma, Chandra Mohan Dr.Sure, Pallaviram Dr.Painam, Surendra Kumar Dr.Chandrasekhar Rao, Jetti Dr.U, Srinivasa Rao Antenna Radiation Patterns, Machine Learning, Deep Learning <p><strong><span> </span></strong><strong><span> RadPat-50K: A Large-Scale Benchmark for Antenna Array Radiation Patterns</span></strong></p> <p><strong><span>RadPat-50K is a large-scale synthetic dataset of 50,000 radiation patterns generated for Uniform Linear Arrays (ULA).<br>Each sample includes polar plots and rectangular plots of the array factor, along with metadata for design and performance parameters.</span></strong></p> <p><strong><span> </span></strong><strong><span> Dataset Highlights</span></strong></p> <ul> <li><strong><span>Size: 50,000 radiation patterns</span></strong></li> <li><strong><span>Array Elements (N): 4, 8, 12, 16, 24, 32, 48, 64</span></strong></li> <li><strong><span>Element Spacing (λ): 0.25, 0.5, 0.75, 1.0</span></strong></li> <li><strong><span>Steering Angles (°): –60, –45, –30, –15, 0, 15, 30, 45, 60</span></strong></li> <li><strong><span>Weighting Schemes: </span></strong> <ul> <li><strong><span>Uniform</span></strong></li> <li><strong><span>Binomial</span></strong></li> <li><strong><span>Cosine</span></strong></li> <li><strong><span>Kaiser</span></strong></li> <li><strong><span>Hamming</span></strong></li> <li><strong><span>Hann</span></strong></li> <li><strong><span>Blackman</span></strong></li> <li><strong><span>Exponential</span></strong></li> </ul> </li> <li><strong><span>Variations (for diversity):</span></strong> <ul> <li><strong><span>⚡</span></strong><strong><span> Amplitude noise</span></strong></li> <li><strong><span> </span></strong><strong><span> Phase noise</span></strong></li> <li><strong><span> </span></strong><strong><span> Steering jitter</span></strong></li> </ul> </li> </ul> <p><strong><span> </span></strong><strong><span> Applications</span></strong></p> <p><strong><span>This dataset is well-suited for research in machine learning, deep learning, and signal processing, including:</span></strong></p> <ul> <li><strong><span> </span></strong><strong><span> Antenna pattern classification</span></strong></li> <li><strong><span> ️</span></strong><strong><span> Beamforming analysis</span></strong></li> <li><strong><span> </span></strong><strong><span> Grating lobe detection</span></strong></li> <li><strong><span> ️</span></strong><strong><span> Data-driven array design</span></strong></li> </ul> <div> </div> <p><strong><span> </span></strong><strong><span> Metadata per Record</span></strong></p> <p><strong><span>Each radiation pattern entry includes:</span></strong></p> <ul> <li><strong><span>Antenna parameters: number of elements, element spacing, steering angle, weighting scheme</span></strong></li> <li><strong><span>Noise parameters: amplitude noise, phase noise, steering jitter</span></strong></li> </ul> <p><strong><span> </span></strong><strong><span> Performance Metrics</span></strong></p> <ul> <li><span> </span><span> <strong>Directivity</strong></span></li> <li><span> </span><span> <strong>Half-Power Beamwidth (HPBW)</strong></span></li> <li><strong><span><span> </span><span> Main Lobe Angle</span></span></strong> <p> </p> </li> </ul> <p><strong><span> </span></strong><strong><span> Benchmark Potential</span></strong></p> <p><strong><span>RadPat-50K provides a standardized benchmark for:</span></strong></p> <ul> <li> <ul> <li><strong><span>Training and evaluating classification & regression models</span></strong></li> <li><strong><span>Developing robust antenna array designs under noise conditions</span></strong></li> <li><strong><span>Advancing AI-driven RF and antenna research</span></strong></li> </ul> </li> </ul> |
| title | RadPat-50K: A Large-Scale Benchmark for Antenna Array Radiation Patterns |
| topic | Antenna Radiation Patterns, Machine Learning, Deep Learning |
| url | https://doi.org/10.5281/zenodo.17067881 |