Performance analysis of statistical optimal data fusion algorithms

Data fusion is widely used in biometric, multi-media signal and image processing, and wireless sensor networks. Optimal fusion techniques are developed to perform fusion under noisy environments. However, the statistical analysis carried out to evaluate the relative merits of these optimal methods is very less. The aim of this paper is to fill this gap by evaluating four statistical optimal data fusion methods namely, the linearly constrained least squares (LCLS) fusion method, the covariance intersection (CI) fusion method, the linearly constrained least absolute deviation (CLAD) fusion method, and the constrained least square (CLS) fusion method. The CLS fusion method presented here is an improved version of the CLAD fusion method. We further analyze the performances of these four methods in terms of optimality, unbiased estimation, robustness, and complexity. Simulations are used to validate the performance of these fusion algorithms.