A general weighted total Kalman filter algorithm with numerical evaluation

An applicable algorithm for Total Kalman Filter (TKF) approach is proposed. Meanwhile, we extend it to the case in which we can consider arbitrary weight matrixes for the observation vector, the random design matrix and possible correlation between them. Also the updated dispersion matrix of the predicted unknown is given. This approach makes use of condition equations and straightforward variance propagation rules. It is applicable to data fusion within a dynamic errors-in-variables (DEIV) model, which usually appears in the determination of the position and attitude of mobile sensors. Then, we apply for the first time the TKF algorithm and its extended version named WTKF to a DEIV model and compare the results. The results show the efficiency of the proposed WTKF algorithm. In particular in the case of large weights, WTKF shows approximately 25% improvement in contrast to TKF approach.     

Visibility Analysis In Vision Metrology Network Design

The aim of this paper is to present a method whereby accuracy enhancement of an existing photogrammetric network is achieved through the automatic selection of additional camera stations. The determination of the positions of these 'accuracy fulfilment' camera stations is based upon what has been termed 'visibility uncertainty prediction modelling' of visibility constraints derived from the existing network geometry. Following a review of vision constraints in network design, the concepts of visibility uncertainty prediction and visibility uncertainty spheres are introduced. These provide a mechanism to predict the visibility of current object target points for the new accuracy fulfilment images. This in turn aids in network design improvement. The visibility uncertainty modelling is then illustrated for two close range photogrammetric network configurations, for which the test results demonstrate that the proposed model can reliably predict target visibility with an overall certainty of 75%.