<title>Best hypothesis target tracking and sensor fusion</title>

Abstract

The concept of using the best hypothesis in the minimum mean square error (MMSE) sense is introduced in this paper to provide alternative data association algorithms for tracking single or multiple targets with data from one or more senors. The concept of using the best hypothesis in estimation is also applied to the task of multiple model filtering. The motivation for using the estimate based on the single best hypothesis in the MMSE sense are tow 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, there is the desire to minimize the mean square of the estimation invovling hypotheses due to discrete possibilities, 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 decision might increase the probability of kill. Also in processing feature for use in target typing, soft decisions may degrade performance more than would a reasonable hard decision algorithm. 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 elan toward algorithms that take advantage of this enhanced capability to provide improved performance based on the specific needs of each application. The emphasis of this paper is on algorithms for data and track association and multiple model filtering using single frame association methods but these methods can be extended to multiple data frame approaches.