Self-tuning filtering for multi-sensor data fusion based on forget factor algorithms

The existing algorithms of data fusion will face the problem of data saturation when interrupted by noises with large variance. Multi-sensor data fusion, which can address this issue, is examined in this paper. The forget factor (FF) method was introduced into the data fusion algorithm to avoid the data saturation phenomenon. A proof for the sequence equivalence theory was given, which showed that two data sequences with different orders can be equivalent to a single sequence whose order is the same as the higher one. In the simulations, an optimal fusion method was used to show the advantages of the algorithm for parameter estimation under large-variance noises.