Research on Adaptive SVR Indoor Location Based on GA Optimization

Abstract

Indoor positioning based on the received signal strength index of ZigBee has received more and more attention due to its low cost, low hardware power consumption and easy implementation. However, due to the existence of multi-path and shadow effects, traditional positioning algorithms often cannot achieve better positioning results. Support vector regression can use these relative errors as the characteristics of the fingerprint database to establish a regression map between the RSSI values and the positioning coordinates. Based on this, this paper proposes a genetic algorithm optimization support vector regression (GA-SVR) method to solve the problem of low ZigBee positioning accuracy. The penalty vector $$C$$C, RBF kernel width $$\\sigma$$σ, and loss function variable $$\\in$$∈ of support vector regression are optimized by genetic algorithm in the proposed method, so that support vector regression achieves the best position prediction performance. Firstly, the training data is clustered by Gaussian hybrid clustering algorithm to establish a fingerprint database. Then the GA-SVM classifier is used to classify the test points. Finally, the coordinates of the test points are regressed and predicted by the GA-SVR model. The simulation and experiment in the actual scene prove the effectiveness of the proposed method. The experimental results show that compared with PSO-SVR, GS-SVR, PLS-SVR, SVR and WKNN algorithms, the GA-SVR algorithm has higher positioning accuracy.