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This paper presents a decentralized positioning and tracking method based on recursive weighted least-squares optimization for wireless sensor networks. The proposed algorithm—weighted extended Kalman filter—is derived by minimizing a recursive-in-time objective function and then applying it in an iterative decentralized manner. The target location is calculated iteratively by taking a weighted average...
This paper presents adaptive algorithms for estimating the location of a mobile terminal (MT) based on radio propagation modeling (RPM), Kalman filtering (KF), and radio-frequency identification (RFID) assisting for indoor wireless local area networks (WLANs). The location of the MT of the extended KF positioning algorithm is extracted from the constant-speed trajectory and the radio propagation model...
In this paper, we propose a modified incremental subgradient (MIG) algorithm with a variable step size for positioning and tracking a target in wireless sensor networks. The proposed positioning scheme formulates location estimation as a nonlinear least-squares problem using the received signal strength, and then applies the MIG algorithm with a fixed step size to solve the problem. This scheme can...
In this paper, we propose a weighted incremental subgradient (WIG) algorithm for positioning and tracking in wireless sensor networks. We formulate the location estimation of a target as a weighted least squares (WLS) problem by taking weights of the local estimates based on the reliability information of distance estimation, and then solve the WLS problem in an iterative, decentralized manner using...
This paper presents a new scheme for positioning and tracking mobile nodes based on adaptive weighted interpolation and alpha-beta (alpha-beta) filtering in wireless sensor networks. The proposed positioning method formulates location estimation as a weighted least squares problem, which can be solved in an iterative, decentralized manner. With such estimated location information, an alpha-beta tracking...
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