Target locating estimation algorithm based on adaptive scaled unscented Kalman filter

被引：0

作者：

School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing 100044, China ^{[1
]}

机构：

[1] School of Electronics and Information Engineering, Beijing Jiaotong University

来源：

Binggong Xuebao | 2013年 / 5卷 / 561-566期

关键词：

Adative filtering; Information processing; Location; Unscented Kalman filter; Wireless sensor network;

D O I：

10.3969/j.issn.1000-1093.2013.05.008

中图分类号：

学科分类号：

摘要：

To improve the accuracy and real-time performance of the the Received Signal Strength Indication (RSSI)-based location system, an estimation algorithm based on adaptive scaled Unscented Kalman Filter (UKF) is proposed. By analyzing the RSSI location model, this new algorithm convents the location problem into estimation of nonlinear system model. It can estimate the target position and the RSSI channel attenuation parameter simultaneously by using the scaled symmetric sampling and the modified Sage-Husa noise statistic estimaters. The experimental and simulation results show that, compared with standard UKF, the proposed algorithm can effectively reduce the state estimation error, improve the filter stability and provide more better accuracy for target location.

引用

页码：561 / 566

页数：5

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