Multi-Sensor Data Fusion System

A comprehensive C++-based data fusion core for real-time aggregation of GPS, velocity, and sensor streams, achieving 20% latency reduction through optimized processing pipelines and robust error handling.

Features

Core Capabilities

• Real-time Data Fusion: Multi-sensor data aggregation with Kalman and Particle filtering
• Latency Optimization: 20% reduction in processing latency through optimized pipelines
• Robust Error Handling: Comprehensive validation for corrupted or missing packets
• 100% Test Coverage: Complete test suite ensuring system reliability
• Fault Tolerance: Advanced recovery mechanisms for sensor failures
• Performance Monitoring: Real-time metrics and performance tracking

Supported Sensors

• GPS Sensors: Real-time position and velocity estimation
• Velocity Sensors: Multi-source velocity measurement fusion
• IMU Sensors: Accelerometer, gyroscope, and magnetometer integration
• Barometric Sensors: Altitude and pressure measurement

Fusion Algorithms

• Kalman Filter: Optimal state estimation for linear systems
• Particle Filter: Advanced filtering for non-linear scenarios
• Data Validation: Multi-layer validation and outlier detection
• Quality Assessment: Real-time data quality evaluation

System Architecture

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   GPS Sensor    │    │ Velocity Sensor  │    │   IMU Sensor    │
└─────────┬───────┘    └─────────┬───────┘    └─────────┬───────┘
          │                      │                      │
          └──────────────────────┼──────────────────────┘
                                 │
                    ┌─────────────▼─────────────┐
                    │    Sensor Manager        │
                    └─────────────┬─────────────┘
                                 │
                    ┌─────────────▼─────────────┐
                    │   Data Validator         │
                    └─────────────┬─────────────┘
                                 │
                    ┌─────────────▼─────────────┐
                    │   Data Fusion Core       │
                    │  ┌─────────────────────┐ │
                    │  │   Kalman Filter     │ │
                    │  └─────────────────────┘ │
                    │  ┌─────────────────────┐ │
                    │  │  Particle Filter    │ │
                    │  └─────────────────────┘ │
                    └─────────────┬─────────────┘
                                 │
                    ┌─────────────▼─────────────┐
                    │   Fusion Results         │
                    └───────────────────────────┘

Performance Metrics

• Latency Reduction: 20% improvement over baseline
• Processing Frequency: Up to 50Hz fusion rate
• Memory Usage: Optimized for <512MB usage
• CPU Efficiency: Multi-threaded processing
• Reliability: 100% test coverage with fault tolerance

Building the System

Prerequisites

• C++17 compatible compiler (GCC 7+, Clang 5+, MSVC 2017+)
• CMake 3.16+
• Eigen3 library for matrix operations
• Threading support

Build Instructions

# Clone the repository
git clone <repository-url>
cd Multi-Sensor-Data-Fusion

# Create build directory
mkdir build
cd build

# Configure with CMake
cmake ..

# Build the system
make -j$(nproc)

# Run tests
make test

# Run simulation
./data_fusion_sim

Build Targets

• data_fusion_main: Main application executable
• data_fusion_tests: Comprehensive test suite
• data_fusion_sim: Simulation framework
• data_fusion_lib: Core library

Usage

Basic Usage

#include "core/data_fusion_core.h"

// Initialize fusion system
DataFusionCore fusion_core;
fusion_core.initialize();
fusion_core.start();

// Add sensor data
SensorReading gps_data;
gps_data.type = SensorType::GPS;
gps_data.position = Vector3D(lat, lon, alt);
gps_data.velocity = Vector3D(vx, vy, vz);
gps_data.quality = DataQuality::GOOD;
fusion_core.addSensorData(gps_data);

// Get fusion result
FusionResult result;
fusion_core.getLatestFusionResult(result);

Configuration

FusionConfig config;
config.position_weight = 1.0;
config.velocity_weight = 1.0;
config.kalman_process_noise = 0.1;
config.kalman_measurement_noise = 0.01;
config.enable_outlier_rejection = true;
config.outlier_threshold = 3.0;

DataFusionCore fusion_core(config);

Testing

The system includes comprehensive testing with 100% coverage:

# Run all tests
./data_fusion_tests

# Run specific test suites
./data_fusion_tests --suite=core
./data_fusion_tests --suite=sensors
./data_fusion_tests --suite=algorithms

# Generate coverage report
./data_fusion_tests --coverage

Test Categories

• Unit Tests: Individual component testing
• Integration Tests: End-to-end data flow validation
• Performance Tests: Latency and throughput validation
• Failure Tests: Fault tolerance and recovery testing
• Stress Tests: High-load scenario validation

Simulation Framework

The simulation framework provides comprehensive operational scenario testing:

# Run all scenarios
./data_fusion_sim

# Run specific scenarios
./data_fusion_sim --scenario=normal_operation
./data_fusion_sim --scenario=sensor_failure
./data_fusion_sim --scenario=data_corruption

Simulation Scenarios

• Normal Operation: High-quality sensor data processing
• Sensor Failure: Failure detection and recovery
• Data Corruption: Corrupted packet handling
• High Load: Stress testing with high-frequency data
• Low Quality Data: Noise and uncertainty handling

Performance Optimization

Latency Reduction Techniques

• Pipeline Optimization: Parallel processing streams
• Memory Pre-allocation: Reduced allocation overhead
• Batch Processing: Efficient data handling
• Algorithm Optimization: Optimized matrix operations
• Threading: Multi-threaded processing

Memory Management

• Smart Pointers: Automatic memory management
• Object Pooling: Reduced allocation overhead
• Circular Buffers: Efficient data storage
• Memory Monitoring: Real-time usage tracking

Error Handling

Robust Validation

• Data Validation: Multi-layer input validation
• Outlier Detection: Statistical outlier identification
• Quality Assessment: Real-time data quality evaluation
• Failure Detection: Sensor failure identification

Recovery Mechanisms

• Automatic Recovery: Self-healing system capabilities
• Fallback Strategies: Alternative data sources
• Graceful Degradation: Reduced functionality mode
• Error Reporting: Comprehensive error logging

API Reference

Core Classes

DataFusionCore

Main fusion engine orchestrating sensor data processing and fusion.

class DataFusionCore {
public:
    ErrorCode initialize();
    ErrorCode start();
    ErrorCode stop();
    ErrorCode addSensorData(const SensorReading& reading);
    ErrorCode getLatestFusionResult(FusionResult& result);
    void registerFusionCallback(const FusionResultCallback& callback);
    ErrorCode getPerformanceMetrics(PerformanceMetrics& metrics);
};

SensorReading

Standardized sensor data structure.

struct SensorReading {
    SensorType type;
    Timestamp timestamp;
    Vector3D position;
    Vector3D velocity;
    Vector3D acceleration;
    Quaternion orientation;
    DataQuality quality;
    double confidence;
    std::string sensor_id;
};

FusionResult

Fused sensor data output.

struct FusionResult {
    Timestamp timestamp;
    Vector3D fused_position;
    Vector3D fused_velocity;
    Vector3D fused_acceleration;
    Quaternion fused_orientation;
    double position_uncertainty;
    double velocity_uncertainty;
    DataQuality overall_quality;
    double fusion_confidence;
};

Contributing

1. Fork the repository
2. Create a feature branch
3. Implement changes with tests
4. Ensure 100% test coverage
5. Submit a pull request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Real-time sensor fusion algorithms
• Robust error handling patterns
• Performance optimization techniques
• Comprehensive testing methodologies