Systematic Inconsistencies Compensation via Inter-node Measurements for UAV-based Target Localization: Algorithm and Analysis

Passive localization systems rely on collaborative measurements from distributed devices to locate non-cooperative targets, where clock synchronization and receiver gain consistency are critical for accuracy. However, oscillator variations and RF channel processing differences introduce systematic inconsistencies, particularly in local clock and gain between receivers, which significantly degrade Time Difference of Arrival (TDOA) and Differential Received Signal Strength (DRSS) measurement accuracy. To address this challenge, we propose a compensation framework leveraging inter-node measurements. The framework includes: (1) a least squares (LS)-based systematic inconsistency estimation algorithm, enhanced with Kalman filtering (KF) for dynamic tracking; and (2) an iterative Taylor-series expansion localization algorithm that operates on compensated measurements. Simulation results demonstrate that the LS estimator approaches the Cramér-Rao Lower Bound (CRLB), while the KF extension robustly tracks time-varying inconsistencies. Notably, even minor inconsistencies cause severe accuracy degradation: 1 $\mu$ s clock offset induces $\sim 70 \mathrm{~m}$ error, while 1 dB gain mismatch adds $\sim 30 \mathrm{~m}$ error. After compensation, localization errors reduce to $\lt 5 \mathrm{~m}$ (LS) and sub-meter precision (KF) in typical scenarios.