A new complex evidence theory

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 pro-cess the uncertain information in the face of multidimensional characteristic data and peri-odic 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 com-plex mass function. The modulus of the complex mass function indicates the degree of sup-port 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 sat-isfies 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.(c) 2022 Elsevier Inc. All rights reserved.

Automated summary

This paper extends the Dempster-Shafer evidence theory to the complex domain to effectively describe and process uncertain information in multidimensional characteristic data and periodic data with phase angle changes. It introduces the complex mass function and other basic concepts to describe uncertainty and supplements the complex Dempster rule of combination. A method to generate complex mass function and apply it to target recognition is proposed, showing improved recognition rate compared to the traditional mass function approach.