Dynamic clustering in wireless sensor network for target tracking based on the fisher information of modified Kalman filter

2016 ◽  
Author(s):  
Feng Wang ◽  
Xuemei Bai ◽  
Bin Guo ◽  
Chen Liu
Keyword(s):  
Kalman Filter ◽  
Wireless Sensor Network ◽  
Target Tracking ◽  
Sensor Network ◽  
Fisher Information ◽  
Wireless Sensor ◽  
Dynamic Clustering ◽  
Modified Kalman Filter

scholarly journals A Constant Gain Kalman Filter for Wireless Sensor Network and Maneuvering Target Tracking

2021 ◽  
Author(s):  
Peeyush Awasthi ◽  
Ashwin Yadav ◽  
Naren Naik ◽  
Mudambi Ramaswamy Ananthasayanam
Keyword(s):  
Kalman Filter ◽  
Steady State ◽  
Wireless Sensor Network ◽  
Target Tracking ◽  
Sensor Network ◽  
The State ◽  
Wireless Sensor ◽  
State Equations ◽  
Maneuvering Target Tracking ◽  
Noise Statistics

One of the well-known approaches to target tracking is the Kalman filter. The problem of applying the Kalman Filter in practice is that in the presence of unknown noise statistics, accurate results cannot be obtained. Hence the tuning of the noise covariances is of paramount importance in order to employ the filter. The difficulty involved with the tuning attracts the applicability of the concept of Constant Gain Kalman Filter (CGKF). It has been generally observed that after an initial transient the Kalman Filter gain and the State Error Covariance P settles down to steady state values. This encourages one to consider working directly with steady state or constant Kalman gain, rather than with error covariances in order to obtain efficient tracking. Since there are no covariances in CGKF, only the state equations need to be propagated and updated at a measurement, thus enormously reducing the computational load. The current work first applies the CGKF concept to heterogeneous sensor based wireless sensor network (WSN) target tracking problem. The paper considers the Standard EKF and CGKF for tracking various manoeuvring targets using nonlinear state and measurement models. Based on the numerical studies it is clearly seen that the CGKF out performs the Standard EKF. To the best of our knowledge, such a comprehensive study of the CGKF has not been carried out in its application to diverse target tracking scenarios and data fusion aspects.

scholarly journals Energy-Efficient Constant Gain Kalman Filter Based Tracking in Wireless Sensor Network

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Kirti Hirpara ◽  
Keyur Rana
Keyword(s):  
Energy Efficiency ◽  
Kalman Filter ◽  
Wireless Sensor Network ◽  
Target Tracking ◽  
Sensor Network ◽  
Energy Efficient ◽  
Base Station ◽  
Wireless Sensor ◽  
Tracking Accuracy ◽  
Prediction Techniques

Target tracking is one of the most widely used applications of wireless sensor network (WSN). Efficient usage of energy is a key issue in WSN application such as target tracking. Another important criterion is a tracking accuracy that can be achieved by using appropriate tracking mechanism. Because of the special characteristic of WSN, there is a trade-off between tracking accuracy and power consumption. Our aim is to improve tracking accuracy as well as provide energy-efficient solution by integrating the concept of clustering and prediction techniques. This paper presents Energy-Efficient Constant Gain Kalman Filter based Tracking (EECGKFT) algorithm to optimize the energy usage and to increase the tracking accuracy. There is also a need to collect data from network having a mobile Base Station (BS). Hence, performance of proposed algorithm is analyzed for a static BS and also for mobile BS. The results depict that proposed algorithm performs better compared to the existing algorithms in energy efficiency and prediction accuracy. Analysis of results validates that EECGKFT increases energy efficiency by reducing transmission of unnecessary data in the sensor network environment and also provides good tracking results.

scholarly journals Enhance the Performance of Wireless Sensor Network based on Software Define Network and Kalman Filter

Webology ◽  
2021 ◽  
Vol 18 (Special Issue 04) ◽  
pp. 1436-1448
Author(s):  
Jumana Suhail ◽  
Dr. Khalida Sh. Rijab
Keyword(s):  
Kalman Filter ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Average Density ◽  
Wireless Sensor ◽  
Buffer Memory ◽  
Leach Protocol ◽  
Subsequent Step ◽  
Random Node ◽  
Software Define Network

The paper proposes a methodology for estimating packet flowing at the sensor level in SDN-WSN based on the partial congestion controller with Kalman filter. Furthermore, the actual purpose of proposing such methodology for predicting in advance the subsequent step of packet flow, and that will consequently contribute in reducing the congestion that might happen. The model proposed (SDN with Kalman filter) is optimized using congestion controller, the methodology of proposed work, the first step random distributed of random node, the apply the Kmean cluster of select the head cluster node in, the connected the network based on LEACH protocol. in this work proposed SDN with Kalman filter for control on network and reduce error of data, where achieve by add buffer memory for each nodes and head cluster to store the data, and SDN control on transmit ion data and receiver data, before transmit apply the Kalman filter on data to reduce error data. The proposed technique, according to simulation findings, extends the network's lifetime by over 30% more than typical WSNs, the reduce the average density of memory to 20% than traditional WSN, and the increase the average capacity of memory to 20% than traditional WSN.

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