Pseudo Measurement Transformation Estimator (PLE) Exhibits Excellent Error Convergence

In target passive tracking systems, the Pseudo Measurement Transformation Estimator (PLE) is extensively employed due to its excellent error convergence properties. However, the estimator produces biased results because of correlations between equivalent noise and system states, which may compromise tracking accuracy. To address this limitation, the Strong Tracking Filter (STF) has been developed, featuring adaptive estimation bias correction and rapid state variation tracking capabilities to enhance tracking precision. Algorithm implementation typically involves calculating time-varying fading factors using orthogonal principles to maintain residual orthogonality, while updating covariance matrices through suboptimal scaling factors. STF has achieved remarkable success in nonlinear system time-delay estimation, fault diagnosis, and fault-tolerant control applications, leading to its widespread adoption. Furthermore, STF demonstrates robust scalability and adaptability, making it suitable for more complex tracking scenarios where conventional filters may underperform.