# Research on Optimization of Target Positioning Error Based on Unmanned Aerial Vehicle Platform
## 1 Introduction
With the continuous development of population intelligence algorithms, the traditional optimization algorithms now present better results [17]. Some examples include the Particle Swarm Optimizer (PSO) [18], the Sparrow Search Algorithm (SSA) [19], and the Gray Wolf Optimizer (GWO) [20]. Although intelligent algorithms have shown significant advantages in many fields, their applications are increasingly widespread. They have achieved significant solutions to complex problems, optimizing the decision-making process and improving computational efficiency; however, there is still room for improvement [21]. Proposed by [PERSON] et al., the dung beetle algorithm (DBO) [22] performs novel swarm intelligence optimization and mimics the survival behaviors of dung beetles, such as ball rolling, dancing, foraging, stealing, and reproduction. It can solve function optimization problems, search for the optimal value, and perform fast convergence. It has been widely used in path planning [23], engineering applications [24], feature selection [25], and so on. The original DBO has shown better performance in global optimization and engineering application problems, but the algorithm itself still has some flaws. For example, in the initialization stage of the population, randomly generated individuals with dense or sparse distribution will have an impact on the iteration period. The algorithm itself has a single mode of finding the optimum; in the late iteration stage, all the populations are close to the global optimum position, and if the individuals at the global optimum position fall into the local optimum at this time, some of the individuals in the vicinity are also prone to fall into the local optimum region. Up to now, scholars at home and abroad have made improvements to DBO to make it more capable of global optimization or successfully applied DBO to specific fields to show its application value. [PERSON] et al. [26] proposed the MSIDBO optimization algorithm, which incorporates multiple strategies to solve the integer programming solution problem that exists in cross-domain collaborative task allocation models. In order to avoid error allocation by UAVs in target localization, this study proposes an improved dung beetle algorithm (CCDBO) that integrates the optimization of crisscross and 3 Sigma Rule [27], takes the error to be allocated as an optimization parameter, gives the optimal solution under certain constraints, removes the local optimal state, and increases the quality and diversity of the population. These simulations and real experiments prove that the improved dung beetle optimization algorithm is more effective. The key contributions presented in this paper are as follows:  
* We innovatively introduced vertical and horizontal crossover and 3\(\sigma\) strategies. This strategy allows certain dimensions in the population that fall into dimensional local optimality to have a chance to jump out of the iteration, solves the local optimality problem that exists in general intelligent algorithms, and excludes possible outliers. * We evaluated the CCDBO algorithm using simulation and compared the results with other algorithms, proving that the CCDBO algorithm outperforms other algorithms with the objective function set in this paper. * We apply the CCDBO algorithm to the optimization of target positioning error for UAVs and compare the results of this algorithm with other algorithms, and the comparison results show that the CCDBO algorithm has better performance in dealing with this kind of problem. The goals of this study are as follows: (1) Establish a suitable coordinate system to show the UAV target localization process. (2) Introduce the error transfer theory of UAVs in the localization process. (3) Synthesize and consider the error and carry out error allocation using the principle of equal influence, the DBO, and the CCDBO. (4) Verify the superiority of the CCDBO algorithm through simulation and an actual flight experiment. (5) Comprehensively evaluate and analyze the experimental results.