Overview

RadarSensing is a comprehensive upper computer software platform designed for mmWave radar applications. The system integrates real-time data acquisition, advanced signal processing, time-frequency visualization, vital signs monitoring, and intelligent motion recognition into a unified interface, providing researchers and developers with powerful tools for radar-based sensing applications.

Core Features

Real-Time Data Acquisition

• Multi-Radar Support: Compatible with TI AWR series, IWR series, and other mmWave radar platforms
• High-Speed Streaming: Real-time data capture at up to 1000+ frames per second
• Configurable Parameters: Adjustable chirp configuration, sampling rate, and frame structure
• Data Recording: Automatic data logging with timestamp and metadata

Time-Frequency Signal Visualization

• Range-Doppler Maps: Real-time heatmap visualization of range and velocity information
• Range-Angle Processing: 2D/3D spatial mapping with beamforming algorithms
• Spectrogram Analysis: Time-frequency domain representation for signal analysis
• Customizable Display: Multiple visualization modes with adjustable color schemes and scaling

Vital Signs Monitoring

• Heart Rate Detection: Non-contact cardiac rhythm monitoring with high accuracy
• Respiratory Rate: Real-time breathing pattern analysis and rate calculation
• Heart Rate Variability (HRV): Advanced cardiac health assessment metrics
• Multi-Target Tracking: Simultaneous monitoring of multiple subjects
• Medical-Grade Accuracy: Validated against clinical standards for healthcare applications

Motion Recognition & Classification

• Gesture Recognition: Real-time hand gesture classification with machine learning
• Activity Detection: Human activity recognition (walking, sitting, falling, etc.)
• Gait Analysis: Detailed biomechanical movement assessment
• Custom Training: User-defined gesture and motion pattern learning
• Multi-Class Classification: Support for 50+ predefined motion categories

Technical Architecture

Signal Processing Pipeline

1. Raw Data Preprocessing: Noise reduction, calibration, and filtering
2. FFT Processing: Range and Doppler FFT with windowing functions
3. CFAR Detection: Constant False Alarm Rate target detection
4. Tracking Algorithms: Kalman filtering for multi-target tracking
5. Feature Extraction: Advanced signal features for classification

Machine Learning Integration

• Deep Learning Models: CNN/RNN architectures for pattern recognition
• Real-Time Inference: Optimized models for low-latency processing
• Transfer Learning: Pre-trained models adaptable to specific applications
• Model Training Tools: Built-in dataset management and training utilities

Hardware Integration

• USB/Ethernet Interface: Multiple connection options for radar modules
• GPIO Control: External trigger and synchronization support
• Multi-Threading: Parallel processing for real-time performance
• Memory Management: Efficient buffer handling for continuous operation

Applications

Healthcare Monitoring	Smart Home	Security & Surveillance

Use Cases

• Medical Monitoring: Non-contact patient vital signs in hospitals and clinics
• Elderly Care: Fall detection and health monitoring for assisted living
• Smart Buildings: Occupancy sensing and energy management
• Automotive: In-cabin monitoring and driver state assessment
• Research & Development: Academic research and algorithm prototyping

Key Advantages

User-Friendly Interface

• Intuitive GUI: Modern interface with drag-and-drop configuration
• Real-Time Feedback: Instant visualization of processing results
• Customizable Layouts: Flexible workspace arrangement for different applications
• Export Capabilities: Data export in multiple formats (CSV, MAT, HDF5)

Performance Optimization

• GPU Acceleration: CUDA support for intensive signal processing
• Multi-Core Processing: Parallel algorithms utilizing all CPU cores
• Memory Efficiency: Optimized memory usage for long-term operation
• Low Latency: Sub-millisecond processing delays for real-time applications

Extensibility

• Plugin Architecture: Modular design for custom algorithm integration
• API Support: RESTful API for external system integration
• Scripting Interface: Python/MATLAB scripting for automation
• Open Source Components: Extensible with community contributions

This comprehensive platform bridges the gap between raw radar data and practical applications, making advanced radar sensing accessible to researchers, developers, and industry professionals.