基于MVO-SVR的室内指纹定位算法

Indoor fingerprint positioning algorithm based on MVO-SVR

针对室内定位过程中由非视距和环境干扰导致的定位精度不高的问题,提出一种基于多元宇宙优化支持向量回归的室内指纹定位算法。首先通过基于超宽带通信技术的双边双向测距算法计算得到测距信息;然后利用测距值作为指纹特征并建立指纹库,使用SVR算法构建定位坐标和测距值之间的映射关系;最后使用MVO优化算法寻优SVR算法的cost和γ参数,以提升定位精度。实验显示,选择径向基函数作为SVR模型的核函数能够有效提高定位精度,并将MVO-SVR的结果与三边定位、随机森林算法、极致梯度提升算法、SVR的结果进行对比和分析,X方向平均绝对误差分别降低了20.12%、54.43%、60.66%和16.21%,Y方向平均绝对误差分别降低了79.57%、54.18%、59.29%和38.17%,平均定位误差Ep分别降低了60.73%、54.38%、60.01%和22.84%,且MVO-SVR算法在X和Y方向平均绝对误差均达到了厘米级。结果证明:基于MVO-SVR的室内指纹定位算法明显提升了定位精度,在复杂室内环境中具有良好的应用前景。

Aiming at the problem of low positioning accuracy caused by non-line-of-sight and environmental interference in indoor positioning process,an indoor fingerprint positioning algorithm based on The Multi-Verse Optimizer-Support Vector Regression algorithm has been proposed.Firstly,the ranging values are calculated through double-side two-way ranging algorithm with ultra�wideband communication technology.Then,the ranging values are utilized as the fingerprint features to construct a fingerprint database,based on fingerprint database SVR algorithm is adopted to establish the mapping relationship between the positioning coordinates and the ranging values.Finally,the MVO algorithm is proposed to optimize the parameters of cost andγin SVR algorithm to improve the accuracy of the positioning results.Experimental results demonstrate that the Radial Basis Function is used as the kernel function in the SVR model to significantly improve positioning accuracy.The results of MVO-SVR were compared and analyzed with those of Trilateration,Random Forest,eXtreme Gradient Boosting,and SVR algorithms.In the X direction,the average absolute error is reduced by 20.12%,54.43%,60.66%,and 16.21%,respectively;in the Y direction,it is reduced by 79.57%,54.18%,59.29%,and 38.17%,respectively.The average positioning error Ep is decreased by 60.73%,54.38%,60.01%,and 22.84%,respectively.Moreover,the average absolute errors in both the X and Y directions for the MVO-SVR algorithm reach the centimeter level.The results confirm that the indoor fingerprinting positioning algorithm based on MVO-SVR significantly enhances positioning accuracy and demonstrates promising application potential in complex indoor environments.