摘要
利用容积卡尔曼滤波来设计粒子滤波器的重要性密度函数,并将当前的测量信息迭代到贯序重要性采样(SIS)过程中,进而提出一种基于迭代容积粒子滤波的RSSI(received signal strength indicator)蒙特卡罗定位算法.该算法使用迭代容积粒子滤波对目标位置和无线信道衰减参数同时进行估计,采用迭代的方式对测量方程进行更新,进一步提高无线信道衰减参数的估计精度.仿真结果表明,基于迭代容积粒子滤波的RSSI蒙特卡罗定位算法对比基于无味粒子滤波的RSSI定位算法,能够有效降低室内无线定位的误差.
A received signal strength indicator （RSSI） Monte-Carol localization algorithm based on iterated cubature particle filter is proposed, in which the importance density function of particle filter is designed with cubature Kalman filter, and current measurement information is introduced into sequential importance sampling （SIS） routine by iterating. This algorithm utilizes iterated cubature particle filter （ICPF） to estimate the target＇s position and the wireless channel attenuation parameter simultaneously, updates the measurement equation with iteration, and gets a more accurate estimation of attenuation param- eter for wireless channel. The experimental results demonstrate that compared with the RSSI parameter estimation algorithm based on unscented particle filter, and there is an improvement of error for the received signal strength indicator Monte-Carol localization algorithm based on ICPF in the indoor wireless localization.
出处
《信息与控制》
CSCD
北大核心
2013年第5期632-638,共7页
Information and Control
基金
国家自然科学基金资助项目(61172130)
