One of the most important open issues is that the classical conflict coefficient in D-S evidence theory （DST） cannot correctly determine the conflict degree between two pieces of evidence. This drawback greatly limits the use of DST in real application systems. Early researches mainly focused on the improvement of Dempster’s rule of combination （DRC）. However, the current research shows it is very important to define new conflict coefficients to determine the conflict degree between two or more pieces of evidence. The evidential sources of information are considered in this work and the definition of a conflict measure function （CMF） is proposed for selecting some useful CMFs in the next fusion work when sources are available at each instant. Firstly, the definition and theorems of CMF are put forward. Secondly, some typical CMFs are extended and then new CMFs are put forward. Finally, experiments illustrate that the CMF based on Jousselme and its similar ones are the best suited ones.
The mapping from the belief to the probability domain is a controversial issue, whose original purpose is to make （hard） decision, but for contrariwise to erroneous widespread idea/claim, this is not the only interest for using such mappings nowadays. Actually the probabilistic transformations of belief mass assignments are very useful in modern multitarget multisensor tracking systems where one deals with soft decisions, especially when precise belief structures are not always available due to the existence of uncertainty in human being’s subjective judgments. Therefore, a new probabilistic transformation of interval-valued belief structure is put forward in the generalized power space, in order to build a subjective probability measure from any basic belief assignment defined on any model of the frame of discernment. Several examples are given to show how the new transformation works and we compare it to the main existing transformations proposed in the literature so far. Results are provided to illustrate the rationality and efficiency of this new proposed method making the decision problem simpler.
