This paper focuses on fusion estimation algorithms weighted by matrices and scalars, and relationship between them is considered. We present new algorithms that address the computation of matrix weights arising from multidimensional estimation problems. The first algorithm is based on the Cholesky factorization of a cross-covariance block-matrix. This algorithm is equivalent to the standard composite fusion estimation algorithm however it is low-complexity. The second fusion algorithm is based on an approximation scheme which uses special steady-state approximation for local cross-covariances. Such approximation is useful for computing matrix weights in real-time. Subsequent analysis of the proposed fusion algorithms is presented, in which examples demonstrate the low-computational complexity of the new fusion estimation algorithms.

We examined how spatial disparity between the auditory and visual stimuli modulated the audio-visual (A-V) prior entry effect. Spatial and temporal proximity of multisensory stimuli are crucial factors for multisensory perception in most cases (e.g. [1],[2]). However our previous research[3],[4] suggested that this well-accepted hypothesis was not applicable to the A-V prior entry effect. In order to examine the effect of the spatial disparity on the A-V prior entry effect, six loudspeakers and two light emitting diodes (LEDs) were used as stimuli. The loudspeakers were located at 10, 25, and 90 degrees from the midline of the participants to both right and left sides. A preceding sound was presented from one of these six loudspeakers. After the preceding sound, two visual targets were presented successively at a short interval and participants judged which visual target was presented first. Two colour changeable ('red' or 'green') LEDs were used for the visual targets and participants judged the order of visual targets by their colour not by their side in order to avoid the response bias as much as possible. The visual targets were situated at 10 degrees or 25 degrees from the participants' midline to both right and left in the Experiment 1. Results showed a biased judgment that the visual target at the sound presented side was presented first. The amplitude of the A-V prior entry effect was greater when the preceding sound source was more apart from the midline of participants. This effect of spatial separation indicated that the clarity of either right or left side of the preceding sound enhanced the amplitude of the A-V prior entry effect (Experiment 2). These results challenge the belief that the spatial proximity of multisensory stimuli is a crucial factor for multisensory perception.

This paper considers the problem of recursive filtering for linear discrete-time systems with uncertain observation. A new approximate adaptive filter with a parallel structure is herein proposed. It is based on the optimal mean square combination of arbitrary number of correlated estimates which is also derived. The equation for error covariance characterizing the mean-square accuracy of the new filter is derived. In consequence of parallel structure of the filtering equations the parallel computers can be used for their design. It is shown that this filter is very effective for multisensor systems containing different types of sensors. A practical implementation issue to consider this filter is also addressed. Example demonstrates the accuracy and efficiency of the proposed filter.