The concept of selecting the best hypothesis in the minimum mean square error (MMSE) sense was introduced in 1999 to provide alternative data association algorithms for data association with hard decisions using data from one or more sensors. The motivations for using the estimate based on the best hypothesis in the MMSE sense are two-fold. First, there are situations where there is a natural preference to make hard decisions rather than soft decisions. Secondly, given that a state estimate is based on a single hypothesis as in a typical hard decision, there is the desire to minimize the mean square of the estimation error, since that is a common metric in evaluating performance. For example, for estimation that involves data association, the traditional MMSE criterion leads to so called soft decisions that may not be appropriate for an interceptor with a small region of lethality while, in contrast, hard decisions might increase the probability of a successful engagement. In addition, in processing features for use in target typing, classification or discrimination, soft decisions may degrade performance more than would a reasonable hard decision.
While the best hypothesis method may be preferred for certain applications, the improved performance might be at the expense of increased processing load. Since the capability of available processors is increasing rapidly, emphasis can be expected to lean toward algorithms that take advantage of this enhanced capability to provide improved performance based on the specific needs of a target tracking application.
The emphasis of this paper is on the use of data from multiple sensors in multiple-frame methods for data association, such as in multiple hypothesis tracking, using as the criteria the best hypothesis in the MMSE sense rather than the most probable hypothesis or the traditional MMSE that leads to soft decisions.