Zheng Weimin, Pang Senyuan, Liu Ning, Chai Qingwei, Xu Lindong
College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China.
Sensors (Basel). 2023 Jul 10;23(14):6282. doi: 10.3390/s23146282.
Indoor localization has broad application prospects, but accurately obtaining the location of test points (TPs) in narrow indoor spaces is a challenge. The weighted K-nearest neighbor algorithm (WKNN) is a powerful localization algorithm that can improve the localization accuracy of TPs. In recent years, with the rapid development of metaheuristic algorithms, it has shown efficiency in solving complex optimization problems. The main research purpose of this article is to study how to use metaheuristic algorithms to improve indoor positioning accuracy and verify the effectiveness of heuristic algorithms in indoor positioning. This paper presents a new algorithm called compact snake optimization (cSO). The novel algorithm introduces a compact strategy to the snake optimization (SO) algorithm, which ensures optimal performance in situations with limited computing and memory resources. The performance of cSO is evaluated on 28 test functions of CEC2013 and compared with several intelligent computing algorithms. The results demonstrate that cSO outperforms these algorithms. Furthermore, we combine the cSO algorithm with WKNN fingerprint positioning and RSSI positioning. The simulation experiments demonstrate that the cSO algorithm can effectively reduce positioning errors.
室内定位具有广阔的应用前景，但在狭窄的室内空间中准确获取测试点（TP）的位置是一项挑战。加权K近邻算法（WKNN）是一种强大的定位算法，能够提高测试点的定位精度。近年来，随着元启发式算法的快速发展，它在解决复杂优化问题方面展现出了效率。本文的主要研究目的是探讨如何使用元启发式算法提高室内定位精度，并验证启发式算法在室内定位中的有效性。本文提出了一种名为紧凑蛇优化（cSO）的新算法。该新颖算法将紧凑策略引入到蛇优化（SO）算法中，确保在计算和内存资源有限的情况下具有最佳性能。在CEC2013的28个测试函数上评估了cSO的性能，并与几种智能计算算法进行了比较。结果表明，cSO优于这些算法。此外，我们将cSO算法与WKNN指纹定位和RSSI定位相结合。仿真实验表明，cSO算法能够有效减少定位误差。
