A new complex evidence theory

Abstract

Dempster-Shafer evidence theory is widely used in the field of information fusion since it satisfies weaker conditions than probability theory. Nevertheless, the description space of the current evidence theory is only real space, and it cannot effectively describe and process the uncertain information in the face of multidimensional characteristic data and periodic data with phase angle changes. Thus, in this paper, evidence theory is extended to the complex Dempster-Shafer evidence theory. The mass function that is used to describe the uncertain information extends from the real space to the complex space, named as complex mass function. The modulus of the complex mass function indicates the degree of support for the proposition. Moreover, other basic concepts that are used to describe uncertainty information are also defined and discussed. To perfect the complex evidence theory, the complex Dempster rule of combination is supplemented. The complex Dempster rule of combination is an extension of Dempster rule of combination, which satisfies the commutative and associative laws just as Dempster rule of combination does, and it can degenerate into Dempster rule of combination. This paper also proposes a method to generate complex mass function and apply it to target recognition. The recognized results show that compared with the mass function, the target recognition rate is larger by using the complex mass function.