Online Bias Estimation for Single-Platform Airborne Radar Using Bias-Subspace Information-Guided MAP-EKF

Systematic measurement biases are a persistent source of degradation in airborne radar target tracking. Online bias estimation is especially difficult for single-platform operations because only one measurement stream is available, and the target state and radar biases are coupled in the same nonlinear observation model. Under weakly observable geometries, directly augmenting bias states into a recursive filter may lead to slow convergence, while fixed or overly frequent batch optimization may inject updates from weakly informative windows. To address this problem, this paper proposes a Bias Subspace Information-Guided MAP-EKF (maximum a posteriori–extended Kalman filter) (BI-MAP-EKF) for online bias estimation in single-platform airborne radar. A nine-dimensional augmented state jointly describes target motion and range, azimuth, and elevation biases. A posterior Cramér–Rao lower bound (PCRLB) is constructed for this state, where the EKF prior, windowed radar measurements, and process noise propagation are jointly considered. The bias subspace is then extracted by Schur complement, and the resulting azimuth bias PCRLB is used to decide whether the MAP update is reliable enough for EKF injection. The scheduler also includes a cooldown interval and a maximum-window safeguard, which respectively limit excessive updates under informative maneuvers and prevent indefinite waiting under weak geometries. Fifty-run Monte Carlo simulations under different maneuvering conditions show that the proposed scheduler is particularly effective in weakly informative geometries, where it improves azimuth bias and horizontal position estimation while reducing the number of accepted MAP refinements compared with the tested fixed-period MAP and moving-horizon estimation (MHE) baselines. In stronger maneuvering scenarios, it provides a competitive accuracy–cost trade-off rather than uniformly outperforming aggressive MHE in every channel.