Multiple Sensor Skewed Covariance Target Localization

A cost-effective approach to improve tracking system accuracy is to employ two or more inexpensive sensors. For example, a radar that exhibits good range, but relatively poor cross-range accuracy may be operated in concert with a camera possessing inverted properties. For each separate sensor, we assume that the target location error is represented by a bivariate Gaussian distribution with an elliptical constant probability contour. The problem posed is that of fusing data from these sensors to produce single-frame location coordinate estimates for a target. One of the methods proposed in the past is covariance intersection which assumes that all sensor measurements are uncorrelated. In this paper, we present a generalized method that provides localization coordinate estimation when sensor estimate distributions are skewed. We show that for a variety of practically important cases, our method offers substantially improved performance over conventional approaches. Based on our results, it is possible to segment the tracking volume into sub-volumes.