[[abstract]]Mobile location tracking based on the received signal strength (RSS) is known to be easily influenced by the shadowing effects in wireless propagation channels. In this paper, we exploit the correlation among shadowing losses at adjacent locations to improve the performance of location tracking. A location tracking algorithm consisting of a maximum likelihood estimator and a Kalman filter is proposed to jointly track the mobile location and the shadowing losses via the RSS. Under a shadowed propagation environment, the simulation shows that, without knowing the underlying mobility model but only the target's speed information, the shadowing tracking furnishes worthful information for the location tracking algorithm, and the prop...
The paper considers mobile location tracking and trajectory data reduction techniques for applicatio...
We consider the problem of localizing a smartphone user using received signal strength (RSS) measure...
We develop maximum likelihood (ML) methods for location estimation using spatio-temporal received-si...
In wireless sensor networks, knowing the location of the wireless sensors is critical in many remote...
Advancements in information technology and communication systems enabled the development of a wide v...
Abstract — Location estimation and tracking for the mobile devices have attracted a significant amou...
Advancements in information technology and communication systems enabled the development of a wide v...
Advancements in information technology and communication systems enabled the development of a wide v...
Advancements in information technology and communication systems enabled the development of a wide v...
International audienceIn this work, we present an on-board solution for train position tracking that...
International audienceIn this work, we present an on-board solution for train position tracking that...
International audienceIn this work, we present an on-board solution for train position tracking that...
International audienceIn this work, we present an on-board solution for train position tracking that...
Received Signal Strength (RSS) localization is widely used due to its simplicity and availability in...
Abstract—In cognitive radio networks (CRNs), secondary users must be able to accurately and reliably...
The paper considers mobile location tracking and trajectory data reduction techniques for applicatio...
We consider the problem of localizing a smartphone user using received signal strength (RSS) measure...
We develop maximum likelihood (ML) methods for location estimation using spatio-temporal received-si...
In wireless sensor networks, knowing the location of the wireless sensors is critical in many remote...
Advancements in information technology and communication systems enabled the development of a wide v...
Abstract — Location estimation and tracking for the mobile devices have attracted a significant amou...
Advancements in information technology and communication systems enabled the development of a wide v...
Advancements in information technology and communication systems enabled the development of a wide v...
Advancements in information technology and communication systems enabled the development of a wide v...
International audienceIn this work, we present an on-board solution for train position tracking that...
International audienceIn this work, we present an on-board solution for train position tracking that...
International audienceIn this work, we present an on-board solution for train position tracking that...
International audienceIn this work, we present an on-board solution for train position tracking that...
Received Signal Strength (RSS) localization is widely used due to its simplicity and availability in...
Abstract—In cognitive radio networks (CRNs), secondary users must be able to accurately and reliably...
The paper considers mobile location tracking and trajectory data reduction techniques for applicatio...
We consider the problem of localizing a smartphone user using received signal strength (RSS) measure...
We develop maximum likelihood (ML) methods for location estimation using spatio-temporal received-si...