New Investigative Findings from the Debiased Converted-Measurement Kalman Filter

original algorithm for the 2-D debiased converted-measurement Kalman filter (CMKF) specified, with incorrect mathematical justification, a requirement for evaluating the average true bias and covariance with the best available polar estimate, rather than exclusively with the polar measurement. Even though this original algorithm yields better tracking performance than the debiased-CMKF algorithm which evaluates the average true bias and covariance exclusively with the polar measurement, this paper shows the specified requirement compromises the statistical consistency between the debiased converted measurement’s error and the average true covariance. To resolve this apparent contradiction, this paper provides the correct em-pirical explanation for the tracking-performance improvement obtained by the specified requirement.