Target tracking with accelerated maneuver using QDCAC method in large-scale wireless sensor networks

Document Type : Original Article

Authors

1 Assistant Professor, Imam Hossein University, Tehran, Iran

2 Professor, Imam Hossein University, Tehran, Iran

3 Associate Professor, Imam Hossein University, Tehran, Iran

Abstract

Tracking methods based on Bayesian algorithms are the most common methods in wireless sensor networks due to tracking accuracy and appropriate scalability. But, on the other hand, due to the high telecommunication overhead, these methods do not have the necessary efficiency in terms of bandwidth and energy. Due to the limitation of the energy source of each node, in this article a combined method called QDCAC based on dynamic clustering and multimode particle filter for target tracking is proposed. In this method, using the dynamic clustering based on the posterior Cramer-Rao lower band, the extracted position is used as the input of the tracking filter to estimate the position and speed of the maneuvering target and uses the estimated location of the target to determine the next master node and wake up the sensor nodes effective in tracking. It can be seen that in the proposed method, despite the non-linearity of the observation quantization algorithm and reducing the accuracy of the sent samples by 50% (4 bits) in order to reduce the information overhead and reduce the power consumption of the network, the accuracy level in the tracking algorithm is better than 1.7 meters, which is a desirable value in the 8,000 square meter test field.

Keywords


Volume 12, Issue 2 - Serial Number 29
Autumn and winter 2024
December 2024
  • Receive Date: 19 June 2024
  • Revise Date: 04 October 2024
  • Accept Date: 20 October 2024
  • Publish Date: 29 October 2024