Nearest-neighbour clutter removal for estimating features in spatial point processes. A density-based algorithm for discovering clusters in large spatial databases with noise. Efficient algorithms for non-parametric clustering with clutter. (1998) Incremental clustering for mining in a data warehousing environment. In this note, we show that there exists a compatibility problem in the derivation of the mean and covariance of the converted measurement errors in [1], and then present a modification to the computation of them, in which both the mean and the covariance are computed strictly conditioned on the measurements. In [1], the authors showed that the exact compensation for the bias in the classical sensor-to-Cartesian conversion is multiplicative and depends on the cosine of the angle measurement errors, and then presented an unbiased converted measurements Kalman filtering (UCMKF) algorithm by using the multiplicative bias compensation factors. While appreciating the work in [1], there exists a compatibility problem in the derivation of the mean and covariance of the converted measurement errors, as pointed out in [2]. In [1], the mean of the converted measurement errors was computed conditioned on the true range, bearing (azimuth) and elevation angles of the target, but for the corresponding covariance, owing to the unavailability of the true values, the authors proposed to compute it conditioned on the measurements directly. This can be shown in detail as follows.