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This paper deals with data fusion for the purpose of estimation. Three fusion architectures are considered: centralized, distributed, and hybrid. A unified linear model and general framework for these three architectures are established. Optimal fusion rules in the sense of best linear unbiased estimation (BLUE), weighted least squares (WLS), and their generalized versions are presented for cases with either complete, incomplete, or no prior information. These rules are much more general and flexible than previous results. For example, they are in a unified form that are optimal for all the three fusion architectures with arbitrary correlation of local estimates or observation noises across sensors or across time.. They are also in explicit forms convenient for implementation. The relationships among these rules are also presented.

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The spatial density of false measurements is known as clutter density in signal and data processing of targets. It is unknown in practice and its knowledge has a significant impact on the effective processing of target information. This paper presents in the first time a number of theoretically solid estimators for clutter density based on conditional mean, maximum likelihood, and method of moments, respectively. They are computationally highly efficient and require no knowledge of the probability distribution of the clutter density. They can be readily incorporated into a variety of trackers for performance improvement. Simulation verification of the superiority of the proposed estimators to the previously used heuristic ones is also provided.

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This paper is part ofa series ofpublications that deal with evaluation of estimation algorithms. This series introduces andjustifies a variety ofmetrics usefulfor evaluating various aspects ofthe performance ofan estimation algorithm, among other things. This paperfocuses on relative error measures, i.e., those with respect to some references, including the magnitude of the quantity to be estimated, its prior mean, and/or measurement error. It proposes several relative metrics that are particularly good for measuring different aspects of estimation perfornance. They often reveal the inherent error characteristics ofan estimator better than widely used metrics ofthe absolute error The metrics are illustrated via an example of target localization with radar measurements.