Software-Defined UHF RFID Asset Tracking in Metallic Aircraft Cabins via IMU-Assisted Adaptive Kalman Filtering and Distilled Edge Intelligence

Passive Ultra-High Frequency (UHF) Radio Frequency Identification (RFID) systems deployed in metallic commercial aircraft cabins suffer from severe multipath fading, non-stationary channel dynamics, and operator gait-induced signal jitter. Addressing these challenges without physical airframe modifications or regulatory recertification remains a critical operational bottleneck. This paper presents an edge-native, software-defined framework that integrates micro-electromechanical system (MEMS) inertial measurements with an IMU-assisted Adaptive Kalman Filter (AKF) and a distilled surrogate decision tree. The proposed algorithm extracts localized motion energy (𝐸IMU) to dynamically scale the measurement noise covariance (𝑅𝑘) prior to physical-layer signal corruption, thereby eliminating phase lag and power hunting. For deterministic edge execution on COTS handheld devices, surrogate model distillation compresses a parent Random Forest ensemble into an 8.2KB 13-leaf decision tree (depth 5) yielding 0.12ms inference latency. Empirical validation across 17 operational sessions in Airbus A320, Boeing 737, and Airbus A321 cabins (10,720 valid reads) demonstrates a 99.45% mean RSSI jitter reduction (95%CI:[99.21%,99.63%]) and a 7.30× suppression of transmit power oscillations. Statistically, asset detection completeness is fully preserved (0.791 vs. 0.795 baseline, 𝑧=0.281,𝑝=0.779). Operating entirely within standard handheld software runtimes, this approach bypasses Supplemental Type Certificate (STC) requirements while ensuring robust aerospace asset visibility.