Random Weighting Method for Multisensor Data Fusion

Abstract

This paper presents a new data fusion method by adopting random weighting estimation for optimal weighted fusion of multisensor observation data. This method adjusts in real time the weights of individual sensors according to variations in estimated sensor variances to obtain optimal weight distribution. Theories of random weighting estimation are established for optimal data fusion through optimal weighting distribution. Algorithms of random weighting estimation are developed to calculate sensor variances for determination of optimal random weighting factors. The fusion result in least mean square error is achieved directly from multisensor observation data, without requirement of any prior knowledge on unknown parameters. The mean square error estimated by the proposed method is not only smaller than from each individual sensor, but also smaller than by the mean of multisensor observation data.