Article citationsMore>>

Huo, L. and Wang, Z. (2017) A Target Tracking Algorithm using Grey Model Predicting Kalman Filter in Wireless Sensor Networks. 2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), Exeter, 21-23 June 2017, 604-610.

has been cited by the following article:

• TITLE: Localizing Jammer in an Indoor Environment by Estimating Signal Strength and Kalman Filter

  AUTHORS: Waleed Aldosari, Mohamed Zohdy

  KEYWORDS: Jammer Detecting, Kalman Filter (KF), Linear Prediction (LP), Centroid Localization (CL), Jammer Received Signal Strength (JRSS), Multipath Signal, Indoor Location

  JOURNAL NAME: Wireless Engineering and Technology, Vol.9 No.2, April 26, 2018

  ABSTRACT: Localizing a jammer in an indoor environment in wireless sensor networks becomes a significant research problem due to the ease of blocking the communication between legitimate nodes. An adversary may emit radio frequency to prevent the transmission between nodes. In this paper, we propose detecting the position of the jammer indoor by using the received signal strength and Kalman filter (KF) to reduce the noise due to the multipath signal caused by obstacles in the indoor environment. We compare our work to the Linear Prediction Algorithm (LP) and Centroid Localization Algorithm (CL). We observed that the Kalman filter has better results when estimating the distance compared to other algorithms.