Radar target profiling and recognition based on TSI-optimized compressive sensing kernel

Keyphrases

• Gaussian Mixture Model 100%
• Recognition-based 100%
• Target Recognition 100%
• Kernel Design 100%
• Radar System 100%
• Compressed Sensing 100%
• Radar Target 100%
• Target Profiling 100%
• Information Theory 50%
• Performance Improvement 50%
• Gaussian Mixture 50%
• Task-irrelevant 50%
• Numerical Simulation 50%
• Training Data 50%
• Power Consumption 50%
• Radar Ranging 50%
• Taylor Expansion 50%
• Low Bandwidth 50%
• Signal of Interest 50%
• Analog-to-digital Converter 50%
• Optimal Sensing 50%
• Random Projection 50%
• Wideband Radar 50%
• Nyquist Rate 50%
• Gradient-based Approach 50%
• Mixture of Probability Distributions 50%
• Compressive Sensing Technique 50%
• Compressive Measurement 50%
• Converter Technology 50%
• Target Pose 50%
• Man-made Target 50%
• Range Profiling 50%
• Pose Angle 50%
• Reduced Sampling Rate 50%
• Effective number of Bits 50%
• Analogue-digital 50%
• Nuisance Parameter 50%
• Cost Consumption 50%
• Range Translation 50%

Engineering

• Radar Systems 100%
• Gaussian Mixture Model 100%
• Radar Target 100%
• Compressive Sensing 100%
• Simulation Result 50%
• Tasks 50%
• Electric Power Utilization 50%
• Computer Simulation 50%
• Sensing Method 50%
• Gaussian Mixture 50%
• Target Pose 50%
• Nuisance Parameter 50%
• Nyquist Rate 50%
• Sampling Rate 50%
• Closed Form 50%
• Analog-to-Digital Converter 50%
• Taylor Series Expansion 50%
• Radar Range 50%

Computer Science

• Compressive Sensing 100%
• Gaussian Mixture Model 100%
• Sampling Rate 50%
• Power Consumption 50%
• Analog-to-Digital Converter 50%
• Random Projection 50%
• Taylor Series Expansion 50%
• Training Data 50%
• Gaussian Mixture 50%
• Numerical Simulation 50%
• Nuisance Parameter 50%