Received Signal Strength Indication Difference Location Algorithm Based on Kalman Filter

Youlin GENG; Chengbo XIE; Chuan YIN; Lantu GUO; Xianyi WANG

doi:10.11999/JEIT180268

Volume 41 Issue 2

Jan. 2019

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Youlin GENG, Chengbo XIE, Chuan YIN, Lantu GUO, Xianyi WANG. Received Signal Strength Indication Difference Location Algorithm Based on Kalman Filter[J]. Journal of Electronics & Information Technology, 2019, 41(2): 455-461. doi: 10.11999/JEIT180268

Citation:

Youlin GENG, Chengbo XIE, Chuan YIN, Lantu GUO, Xianyi WANG. Received Signal Strength Indication Difference Location Algorithm Based on Kalman Filter[J]. Journal of Electronics & Information Technology, 2019, 41(2): 455-461. doi: 10.11999/JEIT180268

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Received Signal Strength Indication Difference Location Algorithm Based on Kalman Filter

doi: 10.11999/JEIT180268

1.
College of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310018, China
2.
China Research Institute of Radioware Propagation, Qingdao 266107, China

Received Date: 2018-03-21
Rev Recd Date: 2018-08-13

Available Online: 2018-08-31

Publish Date: 2019-02-01

Abstract

Abstract

The signal source position can only be estimated by passive monitoring of the signal in terms of that the signal monitored by the spectrum monitoring system can not be controlled and there is no prior knowledge. To address this issue, based on Received Signal Strength Indication Difference (RSSID) and using Kalman filtering, a location algorithm is proposed to improve its localization accuracy. The proposed algorithm transforms the RSSID between two base stations into the ratio of the distance from the location of the signal source to the two base stations, and the distances to construct the matrix of location equations is obtained according to the ratio, and then the least square method to find the signal source position is obtained. The simulation results show that the proposed algorithm has better performance than the classical RSSI localization algorithm, reducing the impact of environmental factors on the positioning accuracy, and better meet the positioning service needing fewer parameters. This algorithm can be effectively applied to the spectrum monitoring system. In addition, Kalman algorithm can effectively improve the system's positioning accuracy, and achieve the expected positioning effect.
- Location algorithm,
- Received Signal Strength Indication (RSSI),
- Kalman filter

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References(19)

References

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