IoT Sensor Fusion

A recursive mathematical algorithm that estimates the state of a dynamic system from a series of noisy measurements. In IoT sensor fusion, it optimally combines predictions from a physical model with real-time observations to minimize the mean squared error.

• Prediction Step: Uses a motion model to project the asset's future state (e.g., location, velocity).
• Update Step: Corrects the prediction using new sensor data, weighted by a calculated Kalman Gain.
• Key Benefit: Provides a smoothed, continuous location estimate even with intermittent GPS pings or accelerometer drift.

A computationally lightweight fusion technique ideal for combining high-frequency but drifting sensors with low-frequency but absolute sensors. It uses a simple high-pass filter on the derivative signal and a low-pass filter on the integral signal.

• Typical Pairing: Fusing a gyroscope (fast angular velocity, drifts over time) with an accelerometer/magnetometer (noisy but absolute reference).
• Implementation: angle = 0.98 * (angle + gyro * dt) + 0.02 * accel_angle
• Advantage: Requires minimal processing power, making it perfect for TinyML deployments on battery-powered trackers.

A non-parametric Bayesian filter that represents the state belief not as a single Gaussian, but as a cloud of discrete particles. Each particle represents a hypothesis of the asset's true state.

• Resampling: Particles are probabilistically resampled based on how well they match the latest sensor observation, concentrating computational effort on the most likely states.
• Global Localization: Can recover from total localization failure (the 'kidnapped robot' problem) without needing an initial estimate.
• Application: Critical for indoor asset tracking where Wi-Fi RSSI or BLE beacon signals are highly multi-modal and non-linear.

Uses neural networks to learn the complex, non-linear relationships between raw sensor streams directly from data, bypassing hand-crafted physical models. End-to-end learning maps multi-modal input (e.g., LiDAR point clouds + camera images) directly to a state output.

• Architectures: Often uses Convolutional Neural Networks (CNNs) for spatial features and Recurrent Neural Networks (RNNs) or Transformers for temporal sequences.
• Feature Extraction: Learns hierarchical features automatically, detecting subtle correlations invisible to classical algorithms.
• Use Case: Fusing vibration, thermal, and acoustic data for predictive maintenance, where failure signatures are too complex to model analytically.

A robust fusion technique used when the correlation between different sensor error sources is completely unknown. It prevents the fused estimate from becoming overconfident by computing a consistent, conservative bound.

• Split Covariance: Separates the known independent error from the unknown correlated error.
• Consistency Guarantee: Mathematically ensures the fused covariance matrix does not underestimate the true error, a critical safety property.
• Application: Fusing data from two independent logistics providers where the cross-correlation of their tracking errors cannot be shared or verified.

Formulates the fusion problem as a large, sparse graph where nodes represent asset states at different times and edges represent constraints from sensor measurements. A non-linear optimizer finds the state configuration that best satisfies all constraints.

• Bundle Adjustment: The standard technique in computer vision for simultaneously optimizing a 3D structure and camera poses.
• Loop Closure: Can detect when an asset revisits a known location and globally correct the entire trajectory history.
• Supply Chain Use: Post-processing multi-sensor data to create a high-fidelity digital twin of a warehouse movement path for forensic analysis.