Narrow Results

The technique for order performance by similarity to ideal solution (TOPSIS) is one of the most well-known methods in multiple criteria decision making (MCDM) problems. The classical TOPSIS method employs a similarity index to rank alternatives. However, the chosen alternative sometimes does not have the shortest distance to the positive ideal solution (PIS) and remotest distance from the negative ideal solution (NIS), simultaneously. Besides, in some cases, TOPSIS cannot assign a unique rank to alternatives. The purpose of this paper is to propose a new similarity TOPSIS index based on the relative distance to the best and worst points. In the proposed method, by treating the separations of an alternative from the PIS and the NIS as negative criterion and positive criterion, respectively, we reduce the original MCDM problem to a new one with two criteria. The proposed index, based on different weights, in optimistic, pessimistic, and apathetic cases, easily determines the score of each alternative. Finally, we illustrate the proposed index using four numerical examples. The results are compared with those published in the literature.

Considering the decision maker’s psychological state will influence their evaluation result in the risky multi-attribute decision-making problem, and the uncertainty of evaluation information. In this paper, we will propose a behavioral risky multiple attribute decision making with interval type-2 fuzzy ranking method and TOPSIS method. The interval type-2 fuzzy set is used to express the uncertainty of evaluation information, the prospect theory is applied to describe people’s psychological state in the processing of risk decision making. First, we define a new ranking method for interval type-2 fuzzy set to compare the interval type-2 fuzzy evaluation information and the expectation. Second, we give a relative distance for interval type-2 fuzzy set to get the distance between the interval type-2 fuzzy evaluation information and expectation. Third, we use the prospect theory, the new defined ranking method and the new defined distance formula to obtain the comprehensive prospect value. Fourth, we use the improved TOPSIS method and the comprehensive prospect value to rank the alternatives. Based on the above-mentioned steps, we give the solution for risky interval type-2 fuzzy multiple attribute decision-making problem, which named as the behavioral risky multiple attribute decision making with interval type-2 fuzzy ranking method and TOPSIS method. Finally, we use an example to show the rationality of this method.

Cooperative localization (CL) utilizing multiple unmanned aerial vehicles (UAVs) has become the current development trend and research hotspot. The traditional distance-based CL method typically uses principle of the spherical intersection to achieve UAV high-precision positioning. However, this distance-based CL strategy requires several leader UAVs at the known positions. Thus, a cooperative localization method for leader–follower multiple UAVs using continuous relative ranging information is proposed in this paper. Aiming at the UAV formation consists of a leader UAV node with the known position and at least a follower UAV that needs to be located. Using the Inertial short-time high-precision relative position constraint and the continuous relative distance constraints with the leader UAV nodes, the closed-form solution of follower UAV’s three-dimensional position can be obtained using spherical intersection principle. On this basis, we derive and construct follower UAV’s position observation equation. The developed strategy requires a single known position leader UAV node to realize high-precision CL, which can effectively solve the traditional distance-based collaborative location problem in case the number of leader UAVs is small. The simulation and experimental results demonstrate that the proposed method significantly improves follower UAV’s positioning accuracy, and neglects the limitation of the traditional distance-based CL method on the number of leader UAV nodes at known positions.