An Effective and Robust Decentralized Target Tracking Scheme in Wireless Camera Sensor Networks

Fu, Pengcheng; Cheng, Ying; Tang, Hongying; Li, Baoqing; Pei, Jun

doi:10.3390/s17030639

Cited by 12 publications

(8 citation statements)

References 29 publications

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“…In this paper, the root mean-squared error in position at each timestep,

, and its average,

, are adopted as the indications of tracking accuracy, since it yields a combined measurement of the bias and variance of a filter estimate [ 33 , 34 ]. The

is given by

and the

at timestep k yields

where

is the estimated vehicle position in timestep k at i -th Monte Carlo run.…”

Section: Simulation Resultsmentioning

confidence: 99%

A Robust Adaptive Unscented Kalman Filter for Nonlinear Estimation with Uncertain Noise Covariance

Zheng

2018

Sensors

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The Unscented Kalman filter (UKF) may suffer from performance degradation and even divergence while mismatch between the noise distribution assumed as a priori by users and the actual ones in a real nonlinear system. To resolve this problem, this paper proposes a robust adaptive UKF (RAUKF) to improve the accuracy and robustness of state estimation with uncertain noise covariance. More specifically, at each timestep, a standard UKF will be implemented first to obtain the state estimations using the new acquired measurement data. Then an online fault-detection mechanism is adopted to judge if it is necessary to update current noise covariance. If necessary, innovation-based method and residual-based method are used to calculate the estimations of current noise covariance of process and measurement, respectively. By utilizing a weighting factor, the filter will combine the last noise covariance matrices with the estimations as the new noise covariance matrices. Finally, the state estimations will be corrected according to the new noise covariance matrices and previous state estimations. Compared with the standard UKF and other adaptive UKF algorithms, RAUKF converges faster to the actual noise covariance and thus achieves a better performance in terms of robustness, accuracy, and computation for nonlinear estimation with uncertain noise covariance, which is demonstrated by the simulation results.

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“…In this paper, the root mean-squared error in position at each timestep,

, and its average,

, are adopted as the indications of tracking accuracy, since it yields a combined measurement of the bias and variance of a filter estimate [ 33 , 34 ]. The

is given by

and the

at timestep k yields

where

is the estimated vehicle position in timestep k at i -th Monte Carlo run.…”

Section: Simulation Resultsmentioning

confidence: 99%

A Robust Adaptive Unscented Kalman Filter for Nonlinear Estimation with Uncertain Noise Covariance

Zheng

2018

Sensors

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“…The proposed energy consumption model is based on the power and activation time of different functional modules: Sensor, Microprocessor, and Transceiver [ 27 ]. Thus, there are three main aspects consuming energy for task nodes, namely, target sensing, data processing and data communication [ 28 ]. Task cluster nodes, in active state, always have their modules on to acquire and process information data about the target and transmit or receive data, which results in most of the energy consumption.…”

Section: Problem Formulation and System Modelsmentioning

confidence: 99%

A Novel Loss Recovery and Tracking Scheme for Maneuvering Target in Hybrid WSNs

Qian

Liu

2018

Sensors

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Tracking a mobile target, which aims to timely monitor the invasion of specific target, is one of the most prominent applications in wireless sensor networks (WSNs). Traditional tracking methods in WSNs only based on static sensor nodes (SNs) have several critical problems. For example, to void the loss of mobile target, many SNs must be active to track the target in all possible directions, resulting in excessive energy consumption. Additionally, when entering coverage holes in the monitoring area, the mobile target may be missing and then its state is unknown during this period. To tackle these problems, in this paper, a few mobile sensor nodes (MNs) are introduced to cooperate with SNs to form a hybrid WSN due to their stronger abilities and less constrained energy. Then, we propose a valid target tracking scheme for hybrid WSNs to dynamically schedule the MNs and SNs. Moreover, a novel loss recovery mechanism is proposed to find the lost target and recover the tracking with fewer SNs awakened. Furthermore, to improve the robustness and accuracy of the recovery mechanism, an adaptive unscented Kalman filter (AUKF) algorithm is raised to dynamically adjust the process noise covariance. Simulation results demonstrate that our tracking scheme for maneuvering target in hybrid WSNs can not only track the target effectively even if the target is lost but also maintain an excellent accuracy and robustness with fewer activated nodes.

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“…Existing studies, including those on image processing-based target tracking, [10][11][12][13] target tracking-oriented node scheduling, [14][15][16][17] and energy-efficient target tracking, [18][19][20][21] have focused on WCSNs in which the direction of nodes is nonadjustable. Single target tracking only needs to consider the localization of single targets in the network, whereas multitarget tracking also needs to consider the scheduling among multiple nodes in the network to optimize network performance.…”

Section: Introductionmentioning

confidence: 99%

“…Single target tracking only needs to consider the localization of single targets in the network, whereas multitarget tracking also needs to consider the scheduling among multiple nodes in the network to optimize network performance. Existing studies, including those on image processing-based target tracking, [10][11][12][13] target tracking-oriented node scheduling, [14][15][16][17] and energy-efficient target tracking, [18][19][20][21] have focused on WCSNs in which the direction of nodes is nonadjustable. Several researchers have studied node scheduling or target tracking algorithms for WCSNs with adjustable node directions, such as object coverage control with camera rotation 22,23 and target tracking based on rotating camera nodes.…”

Section: Introductionmentioning

confidence: 99%

Mobile target tracking algorithm for wireless camera sensor networks with adjustable monitoring direction of nodes

Zhou

Yuan

et al. 2019

Int J Communication

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Wireless camera sensor networks (WCSNs) possess a powerful physical environment monitoring capability. Camera nodes with adjustable monitoring directions further improve their flexibility. This study focuses on tracking multiple mobile targets to investigate the node scheduling and target location evaluation strategy of WCSNs on the basis of rotating nodes. By referring to existing research, this study improves the camera node monitoring and rotation model and proposes three network performance evaluation indicators. The proposed algorithm schedules nodes and their monitoring directions by using the unutilized energy of the nodes and the number of monitored targets. It also predicts the moving trends of the targets and selects active nodes by using the locations and linear speeds of the targets. Experimental results show that the proposed algorithm has a high target tracking accuracy. Compared with traditional target tracking algorithms, the proposed algorithm can effectively reduce the number of active nodes, balance the energy consumption between nodes, and prolong network lifetime.

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An Effective and Robust Decentralized Target Tracking Scheme in Wireless Camera Sensor Networks

Cited by 12 publications

References 29 publications

A Robust Adaptive Unscented Kalman Filter for Nonlinear Estimation with Uncertain Noise Covariance

A Robust Adaptive Unscented Kalman Filter for Nonlinear Estimation with Uncertain Noise Covariance

A Novel Loss Recovery and Tracking Scheme for Maneuvering Target in Hybrid WSNs

Mobile target tracking algorithm for wireless camera sensor networks with adjustable monitoring direction of nodes

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