Spatial Sensor Network Based Target Tracking By Classification

Abstract

The wide use of sensor networks in the day to day communication in recent trends made tracking a significant feature in monitoring systems. The automated systems capable of detection and tracking of targets is a desirable application in many fields. Firstly, deploy a sensor network with appropriate space between sensors and then introduce targets into the network. As the sensors detect the targets, each sensor communicates with neighborhood sensor nodes and one of those sensors are elected as cell-head which will calculate the position of target from the data and transmit that to sink. This process is repeated iteratively to track the moving target. Feature extraction methods and classification techniques have been studied to classify targets by their type. For the challenging task of Multi-target tracking, the methods of sequential Bayesian filtering and Sequential Monte Carlo-Particle Hypothesis Density filters are sought. Accurate algorithms have been simulated for Localization and tracking of target using the data of sensor strengths which are collaboratively communicated among the sensors. Direction of moving target inside a cell was estimated. Algorithm for Hierarchical multi-hop communication model was established.