基于遗传算法优化的支持向量回归的室内定位算法

基于zigbee接收信号强度指标的室内定位由于成本低,硬件功耗低,易于实现而受到越来越多的关注。为了提高zigbee技术的室内定位精度,减少环境因素的不利影响,本文提出了一种遗传算法优化支持向量回归的室内定位方法。该算法分为离线采集和在线预测两个阶段,离线采集进行指纹数据库的建立,在线预测则根据训练模型进行位置预测。首先所有的采集数据通过卡尔曼滤波进行处理,然后通过遗传算法优化支持向量回归(GA-SVR)的惩罚参数 、RBF核宽度 和损失函数变量 ,从而使支持向量回归达到最好的位置预测性能。在实际场景中的实验结果表明,与PSO-SVR, GS-SVR, SVR和WKNN算法相比,该算法具有较好的定位性能。

Improved Indoor Location Method Based on Support Vector Regression

Indoor positioning based on the received signal strength index(RSSI) of zigbee had received more and more attention due to its low cost, low hardware power consumption and easy implementation. In order to improve the indoor positioning accuracy of zigbee technology and reduce the adverse effects of environmental factors, this paper proposes an indoor positioning method based on genetic algorithm optimization support vector regression. The algorithm is divided into two stages: offline collection and online prediction. Offline collection is used to establish the fingerprint database, and online prediction is based on the training model for position prediction. Firstly, the all collected data is processed by Kalman filter, and then the penalty parameter , RBF kernel width and loss function variable of support vector regression (GA-SVR) are optimized by genetic algorithm, so that the support vector regression reaches the best position prediction performance. The experimental results in the actual scene show that compared with the PSO-SVR, GS-SVR, SVR and WKNN algorithms, the algorithm has better positioning performance.