scholarly journals Coordinated Target Tracking by Distributed Unscented Information Filter in Sensor Networks with Measurement Constraints

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Yintao Wang ◽  
Junbing Li ◽  
Qi Sun
Keyword(s):  
Sensor Networks ◽  
Target Tracking ◽  
Sensor Node ◽  
Second Phase ◽  
Consistent Estimate ◽  
Consensus Protocol ◽  
Time Step ◽  
Sigma Point ◽  
Information Filter ◽  
Information Filters

Tracking a target in a cluttered environment is a representative application of sensor networks. In this paper, we develop a distributed approach to estimate the motion states of a target using noisy measurements. Our method consists of two parts. In first phase, using the unscented sigma-point transformation techniques and information filter framework, a class of algorithms denoted as unscented information filters was developed to estimate the states of a target to be tracked. These techniques exhibit robustness and accuracy of sigma-point filters for nonlinear dynamic inference while being as easily fused as the information filters. In the second phase, we proposed a novel consensus protocol which allows each sensor node to find a consistent estimate of the value of the target. Under this protocol, the final estimate of the value of the target at each time step is iteratively updated only by fusing the neighbors’ measurements when one sensor node is out of the measurement scope of the target. Performance of the distributed unscented information filter is demonstrated and discussed on a target tracking task.

Target Tracking in Sensor Networks Using Additive Divided Difference Information Filtering Method

2013 ◽  
Vol 433-435 ◽  
pp. 503-509
Author(s):  
Deok Jin Lee ◽  
Kil To Chong ◽  
Dong Pyo Hong
Keyword(s):  
Sensor Networks ◽  
Target Tracking ◽  
Information Fusion ◽  
Divided Difference ◽  
Information Filtering ◽  
Nonlinear Estimation ◽  
Fusion Algorithm ◽  
Distributed Sensor Networks ◽  
Distributed Sensor ◽  
Information Filter

This paper represents a new multiple sensor information fusion algorithm in distributed sensor networks using an additive divided difference information filter for nonlinear estimation and tracking applications. The newly proposed multi-sensor fusion algorithm is derived by utilizing an efficient new additive divided difference filtering algorithm with embedding statistical error propagation method into an information filtering architecture. The new additive divided difference information filter achieves not only the accurate nonlinear estimation solution, but also the flexibility of multiple information fusion in distributed sensor networks. Performance comparison of the proposed filter with the nonlinear information filters is demonstrated through a target-tracking application.

scholarly journals Distributed Particle Flow Filter for Target Tracking in Wireless Sensor Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Junjie Wang ◽  
Lingling Zhao ◽  
Xiaohong Su
Keyword(s):  
Sensor Networks ◽  
Target Tracking ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Particle Flow ◽  
Consensus Protocol ◽  
Distributed Target ◽  
Two Phases ◽  
Limited Sensing ◽  
Distributed Tracking

We propose, in this paper, a fully distributed tracking algorithm based on particle flow filter over sensor networks based on the max-consensus. The presented distributed particle flow filter is particularly suitable for the sensor network with limited sensing range and consists of two phases: the estimation phase and consensus phase. The local estimation results are obtained via particle flow filter in the estimation phase; then the sensor nodes agree on the best estimation based on max-consensus protocol in the consensus phase. Numerical simulations and comparisons with other distributed target tracking algorithms are carried out to show the effectiveness and feasibility of our approach.

scholarly journals Detecting Anonymous Target and Predicting Target Trajectories in Wireless Sensor Networks

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 719
Author(s):  
P. Leela Rani ◽  
G. A. Sathish Kumar
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Target Tracking ◽  
Target Detection ◽  
Sensor Node ◽  
Minimum Energy ◽  
Maximum Error ◽  
Base Station ◽  
Position Estimation ◽  
Wireless Sensor

Target Tracking (TT) is an application of Wireless Sensor Networks (WSNs) which necessitates constant assessment of the location of a target. Any change in position of a target and the distance from each intermediate sensor node to the target is passed on to base station and these factors play a crucial role in further processing. The drawback of WSN is that it is prone to numerous constraints like low power, faulty sensors, environmental noises, etc. The target should be detected first and its path should be tracked continuously as it moves around the sensing region. This problem of detecting and tracking a target should be conducted with maximum accuracy and minimum energy consumption in each sensor node. In this paper, we propose a Target Detection and Target Tracking (TDTT) model for continuously tracking the target. This model uses prelocalization-based Kalman Filter (KF) for target detection and clique-based estimation for tracking the target trajectories. We evaluated our model by calculating the probability of detecting a target based on distance, then estimating the trajectory. We analyzed the maximum error in position estimation based on density and sensing radius of the sensors. The results were found to be encouraging. The proposed KF-based target detection and clique-based target tracking reduce overall expenditure of energy, thereby increasing network lifetime. This approach is also compared with Dynamic Object Tracking (DOT) and face-based tracking approach. The experimental results prove that employing TDTT improves energy efficiency and extends the lifetime of the network, without compromising the accuracy of tracking.

Diffusion nonlinear Kalman filter with intermittent observations

2017 ◽  
Vol 232 (15) ◽  
pp. 2775-2783 ◽  
Author(s):  
Guoqing Wang ◽  
Ning Li ◽  
Yonggang Zhang
Keyword(s):  
Kalman Filter ◽  
Sensor Networks ◽  
State Estimation ◽  
Target Tracking ◽  
Estimation Accuracy ◽  
Nonlinear State Estimation ◽  
Information Filter ◽  
Cubature Kalman Filter ◽  
Nonlinear Kalman Filter ◽  
Nonlinear State

In this article, we consider the distributed nonlinear state estimation over sensor networks under the diffusion Kalman filter paradigm, where data only exchanges among the neighbourhoods of sensors. We first obtain a novel nonlinear Kalman filter with intermittent observations based on cubature Kalman filter. After that, its equivalent information filter is derived, and the proposed diffusion cubature Kalman filter with intermittent observations is designed based on this information filter. The effectiveness of proposed algorithms is demonstrated by a typical target tracking example, and our algorithm has similar estimation accuracy when comparing with existing algorithms while consuming less computation and communication resources.

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