【摘要】 针对无线网络室内位置指纹定位中存在定位精度低,跳跃性较大的问题,提出一种烟花算法优化支持向量机的室内定位模型,对多次采集到的接收信号强度进行高斯滤波去除奇异值,通过烟花算法优化SVM的参数,建立室内无线定位优化模型.实验对比证明,烟花算法比粒子群算法更能提高SVM的优化速率及室内无线网络定位精度.更多还原

【Abstract】 Aiming at the problem of low positioning accuracy and large span of wireless network on the indoor fingerprint location,we presented a new indoor positioning model based on modified Support Vector Machine(SVM).Firstly,in order to remove the singular value,we use Gauss Filter to process the received the signal strength values that are collected several times.Secondly,the parameters of support vector machine are optimized by Fireworks Algorithm.Finally,we establish the indoor wireless location model with FWA-SVM.Through experimental comparison,we prove that the fireworks algorithm is more accurate than the Particle Swarm Optimization(PSO) algorithm in the SVM optimization rate and positioning accuracy.更多还原