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 filteringmechanisms preceding higher-level cognition. Unlike classical Kalman-based approaches, PC4 does not require environment-specificpre-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 fusionadaptive filteringfault tolerancecontrol systemsbiomorphic filteringkalman comparisonautonomous systemsroboticssignal processingneuroscience-inspired AI