基于人工鱼群优化的BP神经网络WiFi指纹室内定位方法

WiFi fingerprint indoor location method with BP neural network based on improved artificial fish swarm optimization algorithm

针对传统的基于反向传播(BP)神经网络室内定位算法存在着低精度和慢收敛问题,且考虑到室内环境复杂,通常存在多径效应,无法使用信号强度衰减测距模型进行精确定位,提出一种改进的人工鱼群优化的BP神经网络WiFi指纹室内定位算法.利用人工鱼群觅食和寻优方式来提高全局寻优搜索的速度和能力,采用改进的人工鱼群算法(IAFSA)优化选取室内定位BP神经网络的权值和阈值,有效避免了传统BP神经网络的预测值易陷入局部最优的缺点,同时利用高斯滤波对信号进行去噪处理,建立采样点获取到的信号强度值(RSSI)与位置坐标的关系.实验结果证明所提方法与传统的BP神经网络方法相比,平均定位误差减少了0.75 m,平均定位精度提高32.2%,提高了定位可靠性,算法具有更好的稳定性.

In view of the traditional indoor localization algorithm based on BP neural network existing low precision and sconvergence speed.considering the sophisticated indoor environment,there is usually the multipath effect,in addition that the signal attenuation model unsuitable for accurate positioning,this paper proposes an improved artificial fish optimization WiFi fingerprint indoor localization algorithm of BP neural network.the foraging and searching methods of artificial fish are used to improve the speed and ability of global optimization,using the improved artificial fish algorithm(IAFSA)to optimize the selection of weights and thresholds of BP neural network,which effectively avoid the disadvantage that predicted value of traditional BP neural network is easily plunged into partial optimum,the signal is denoised by gaussian filter in advance.At the same time,the relationship between the signal strength value(RSSI)obtained by the sampling point and the position coordinate is established.Experimental results show that compared with the traditional BP neural network method,the proposed method of this paper reduces the average positioning error by 0.75 m,and the average positioning accuracy is improved by about 32.2%.The algorithm of this paper improves the reliability of positioning and has better stability.