Electro-optical (EO) camera systems are commonly used in target detection and tracking applications. Such camera systems typically comprise a suite of sensors, such as narrow/wide field of view (FOV) cameras, that provide target-originated angular measurements. To estimate the position of a point target in Cartesian space, existing techniques in literature employ the non-linear measurement mapping from the focal-plane array (FPA) to azimuth and elevation space. A common assumption made in using this conversion is that azimuth and elevation measurement errors have the same standard deviation, are uncorrelated, and are uniform across the camera’s FOV.

This paper presents an approach to derive the azimuth and elevation statistics, including the cross-correlation of their errors. This approach converts the raw target measurements and their covariance in the image space (FPA) to the angular space for subsequent use in Cartesian state filtering. This conversion has been validated to be unbiased and consistent, and results show that the line-of-sight (LOS) angle error variances and their correlations are in fact variable, with magnitudes dependent on the target’s location in the FPA. The correct LOS angle covariance matrices should be used in Cartesian state estimation and errors between the azimuth and elevation. We demonstrate a multi-sensor fusion case where the LOS angle covariance matrices of our proposed approach are used to derive the final composite target position estimate and its corresponding error covariances. The composite estimates produced from our proposed approach are proven to be statistically efficient. Compared to the use of the uncorrelated and constant LOS angle covariances, there is significant improvement to the error modeling of the fused Cartesian position covariance.