A RSSI-Based Distributed Weighted Search Localization Algorithm for WSNs

Yao, Yingbiao; Han, Qi; Xu, Xiaorong; Jiang, Nanlan

doi:10.1155/2015/293403

Cited by 12 publications

(8 citation statements)

References 28 publications

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“…In maneuvering target tracking, the effect of the latest information should be emphasized much more. The recursive modified Sage-Husa estimator [10] can be obtained as shown in (30), 1 1…”

Section: Improved Adaptive Ukfmentioning

confidence: 99%

Maneuvering Target Tracking Using Current Statistical Model Based Adaptive UKF for Wireless Sensor Network

Peng¹,

Yang²,

Liu³

2015

jcm

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This paper presents Current statistical model based Adaptive Unscented Kalman Filter (CAUKF) for maneuvering target tracking, which is based on Received Signal Strength Indication (RSSI). In order to introduce the Kalman filter, the state-space model, which uses RSSI values as the measurement equation, needs to be obtained. Thus a current statistical model for maneuvering target based on the path loss model is presented. To avoid the negative influence of current statistical model's limited acceleration, the functional relation between the maneuvering status of target and the estimation of the neighboring position information is applied to carry out the adaptation of the process noise covariance Q(k). Then, a novel idea of modified Sage-Husa estimator is introduced to adapt the process noise covariance matrix Q(k), while the adaptive measurement noise covariance matrix R(k) is implemented by a fuzzy inference system. The experimental results show that the final improved CAUKF is an algorithm with faster response and better tracking accuracy especially in maneuvering target tracking. Index Terms-Maneuvering target tracking, adaptive unscented kalman filter, current statistical model, wireless sensor network, received signal strength indication I. INTRODUCTION The development pace of target tracking research is highly tied up with the advancement of Wireless Sensor Network (WSN) and wireless technologies. As sensor nodes in WSN become smaller and stronger, the ability of information processing is much stronger and wireless network operation management is also more intelligent. At present, many target tracking algorithms for wireless system have been proposed. Because Radio Frequency Identification (RFID) based tracking technology is lowcost and operable, so it is used widely in practical applications. The particular interest is the ability to track targets carrying active RFID tags, by exploiting metrics of their periodic transmissions such as Time of Arrival (TOA), Time Difference of Arrival (TDOA), Angle of Arrival (AOA), and Received Signal Strength Indication (RSSI) [1]. The traditional RSSI based tracking method

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“…In maneuvering target tracking, the effect of the latest information should be emphasized much more. The recursive modified Sage-Husa estimator [10] can be obtained as shown in (30), 1 1…”

Section: Improved Adaptive Ukfmentioning

confidence: 99%

Maneuvering Target Tracking Using Current Statistical Model Based Adaptive UKF for Wireless Sensor Network

Peng¹,

Yang²,

Liu³

2015

jcm

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“…But the drawback of RSSI based localization is the relatively lower accuracy. Many localization algorithms have been introduced to improve the localization accuracy [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Investigations on real time RSSI based outdoor target tracking using kalman filter in wireless sensor networks

Vadivukkarasi

Kumar

2020

IJECE

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Target tracking is essential for localization and many other applications in Wireless Sensor Networks (WSNs). Kalman filter is used to reduce measurement noise in target tracking. In this research TelosB motes are used to measure Received Signal Strength Indication (RSSI). RSSI measurement doesn’t require any external hardware compare to other distance estimation methods such as Time of Arrival (TOA), Time Difference of Arrival (TDoA) and Angle of Arrival (AoA). Distances between beacon and non-anchor nodes are estimated using the measured RSSI values. Position of the non-anchor node is estimated after finding the distance between beacon and non-anchor nodes. A new algorithm is proposed with Kalman filter for location estimation and target tracking in order to improve localization accuracy called as MoteTrack InOut system. This system is implemented in real time for indoor and outdoor tracking. Localization error reduction obtained in an outdoor environment is 75%.

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“…By analyzing radio signals intensively, several works have introduced radio-based localization algorithms in literature [16][17][18]. Namely, radio signals are processed to predict the current target position.…”

Section: Introductionmentioning

confidence: 99%

“…The strategy proposes to insightfully learn the channel model to draw localization inference about the target position. Similarly, another RSSI-(received signal strength indicator-) based node localization scheme is proposed in [17]. The distributed weighted search localization algorithm (WSLA) was constructed, combining a node localization precision classification scheme and weight-based searches.…”

Section: Introductionmentioning

confidence: 99%

Self-Navigating UAVs for Supervising Moving Objects over Large-Scale Wireless Sensor Networks

Van

Trung

2020

International Journal of Aerospace Engineering

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Increasingly inexpensive unmanned aerial vehicles (UAVs) are helpful for searching and tracking moving objects in ground events. Previous works either have assumed that data about the targets are sufficiently available, or they solely rely on on-board electronics (e.g., camera and radar) to chase them. In a searching mission, path planning is essentially preprogrammed before taking off. Meanwhile, a large-scale wireless sensor network (WSN) is a promising means for monitoring events continuously over immense areas. Due to disadvantageous networking conditions, it is nevertheless hard to maintain a centralized database with sufficient data to instantly estimate target positions. In this paper, we therefore propose an online self-navigation strategy for a UAV-WSN integrated system to supervise moving objects. A UAV on duty exploits data collected on the move from ground sensors together with its own sensing information. The UAV autonomously executes edge processing on the available data to find the best direction toward a target. The designed system eliminates the need of any centralized database (fed continuously by ground sensors) in making navigation decisions. We employ a local bivariate regression to formulate acquired sensor data, which lets the UAV optimally adjust its flying direction, synchronously to reported data and object motion. In addition, we also construct a comprehensive searching and tracking framework in which the UAV flexibly sets its operation mode. As a result, least communication and computation overhead is actually induced. Numerical results obtained from NS-3 and Matlab cosimulations have shown that the designed framework is clearly promising in terms of accuracy and overhead costs.

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A RSSI-Based Distributed Weighted Search Localization Algorithm for WSNs

Cited by 12 publications

References 28 publications

Maneuvering Target Tracking Using Current Statistical Model Based Adaptive UKF for Wireless Sensor Network

Maneuvering Target Tracking Using Current Statistical Model Based Adaptive UKF for Wireless Sensor Network

Investigations on real time RSSI based outdoor target tracking using kalman filter in wireless sensor networks

Self-Navigating UAVs for Supervising Moving Objects over Large-Scale Wireless Sensor Networks

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