Multiplatform Bias-Mitigation Pseudolinear Kalman Filter for Bearings-Only Target Tracking under Measurement Constraints

The pseudolinear Kalman filter (PLKF) offers a stable and computationally efficient solution for bearings-only target tracking (BOTT), but it suffers from significant estimation bias under high-noise conditions due to the coupling between the measurement matrix and noise. Existing bias-mitigation methods often rely on small-noise assumptions and seldom address multiplatform cooperative fusion under communication constraints. To address these issues, this article proposes a dual-stage bias-mitigation PLKF (BM-PLKF) to enhance tracking accuracy. By analyzing the statistical correlation in the pseudolinear model, we reformulate a weighted cost function to minimize the total error variance. An improved Kalman gain is derived by introducing an adaptive inflation factor to dynamically scale the pseudolinear covariance, followed by a deterministic bias compensation stage. Building on the BM-PLKF, we further develop a multiplatform measurement-constrained PLKF (MPMC-PLKF) for collaborative tracking. This framework treats sparse and delayed measurements from remote platforms as geometric equality constraints and utilizes an orthogonal projection onto the constraint manifold to regularize the local state estimate. Numerical simulations and real-world UAV flight experiments demonstrate that the proposed BM-PLKF and MPMC-PLKF significantly outperform conventional PLKF, bias-compensated (BC)-PLKF, and IVKF in terms of root mean square error (RMSE) and bias reduction, maintaining superior robustness and stability under severe measurement noise and delayed communication constraints.