PC4-Paranoid Filter: Adaptive Physics-Constrained Sensor Fusion Dataset

This dataset accompanies the development and stress-testing of the PC4-Paranoid Filter —
a physics-constrained, environment-agnostic adaptive sensor fusion algorithm.

The PC4-Paranoid Filter is designed as a universal pre-processor for noisy, adversarial,
or partially corrupted sensor arrays, inspired by biological perceptual filtering
mechanisms preceding higher-level cognition.

Unlike classical Kalman-based approaches, PC4 does not require environment-specific
pre-tuning of noise covariances and remains stable under:
• sensor drift
• freeze faults
• anti-phase corruption
• partial sensor failure
• snap-back transients

The dataset contains:
• reference Python implementations of PC4 filter variants
• stress-test scripts reproducing all reported scenarios
• comparative benchmarks against Kalman filtering
• full experimental figures corresponding to Figures 1–29

All experiments are deterministic and reproducible.
The dataset is intended for control systems research, robotics, autonomous vehicles,
neuroscience-inspired signal processing, and adaptive AI systemssensor fusion
adaptive filtering
fault tolerance
control systems
biomorphic filtering
kalman comparison
autonomous systems
robotics
signal processing
neuroscience-inspired AI