Combining Correlated Data from Multiple Classifiers

Abstract

Real time data collected from sensors and then having been processed by multiple classifiers are correlated due to common, inherent noise resulting from the sensor. The data is collected at different ranges and may have different statistical distributions and characteristics. Measurements from different classifiers are fused together to obtain more information about the phenomenon or environment under observation. Since the classifier fusion is attempting to improve the data through processing only, this chapter focuses on object refinement or fusion level 1 within the Joint Directors of Laboratories (JDL) data fusion process model and taxonomy of algorithms. Before fusion, the correlated data is normalized using a variety of normalization techniques such as z-score and min-max normalization. Further, by dynamically weighting and combining the measurements from these classifiers, performance is improved. Weights are found using a Particle Swarm Optimization search algorithm and are a function of required accuracy and degree of correlation. Results are presented for (a) synthetic data generated using conditional multivariate normal distribution with different covariance matrices and (b) NIST BSSR dataset. For correlated classifiers, the performance of the particle swarm optimization technique outperforms the traditional data level fusion i.e., z-normalization and min-max.