Integration of Unmanned Aerial Vehicles (UAVs) into civil airspace is becoming a fundamental requirement
to satisfy the even more consumer growing demand. The limiting issues for this integration are related to
the development of a reliable Sense and Avoid (SAA) system able to equate the human eye performances. Multisensor
data fusion techniques are generally used in order to overcome single sensor shortcomings. Although much
research addresses toward the realisation of better performing sensors, system degradation could arise from bad
numerical behaviours injected by the specific fusion algorithm. Bayesian estimators are the most widely used techniques
to perform this task but they could be affected by round-off errors. To improve filter instabilities, induced
by ill-conditioned matrices, an alternative numerical approach, based on the Joseph form of the state covariance
matrix update applied to non-linear systems is presented. The novelty of this technique lies on taking advantage
from the higher order accuracy ensured by Sigma-Point Kalman Filters for solving non-linear inference problems,
and using the more numerically robust Joseph update equation
