Target Tracking Using Radial Distance and Lucas Cluster-Based Ridge Regression in Wireless Sensor Network

Abstract

Target tracking is one of the most significant concerns in Wireless Sensor Networks (WSNs). In a densely deployed WSN, spatial trajectories in future mission monitoring can provide comprehensive communication but with dynamically changing network, targets were lost, compromising stability. Some approaches pursue minimum overload and energy consumption occurring during tracking at the cost of tracking accuracy, while some other mechanisms aim to achieve the best tracking accuracy without consideration of energy consumption. In this paper, we present a new method called, Radial Distance and Lucas Cluster-based Ridge Regression (RD-LCRR) to detect and track the target in WSN considering both energy efficiency and accuracy. Initially, we sense the object via Radial Distance Edge Propagation Object Sensing model based on the cluster formation and then identify moving objects through additive value. Next, the object to be tracked is obtained by applying Lucas Kanade Cluster-based Ridge Regression model. Here, target detection is first initialized via Lucas Kanade and finally, the tracking of the actual target is performed through Ridge Regression. The performance of the RD-LCRR method is considered for different scenarios and experiments are performed in a simulation environment against state-of-the-art methods. Results of the experiments show that the presented target detection and tracking method enables a significant reduction in the amount of energy consumption, target tracking time with higher accuracy rate.