Wireless sensor networks have found rapidly growing applications in areas such as automated data collection, surveillance, and environmental monitoring. One important use of sensor networks is the tracking of a mobile target (point source) by the network. Mobile target tracking has a number of practical applications, including robotic navigation, search-rescue, wildlife monitoring, and autonomous surveillance. Target tracking involves two steps. Our predict target positions from noisy sensor data measurements. Control mobile sensor tracker to follow or capture the moving target. The challenge of target tracking and mobile sensor navigation arises when a mobile target does not follow a predictable path. Successful solutions require a real-time location estimation algorithm and an effective navigation control method. Target tracking can be viewed as a sequential location estimation problem. a number of distributed sensors for location estimation. There exist a number target localization approaches-based various measurement models such as received signal strength (RSS), time of arrival (TOA), time difference of arrival (TDOA), signal angle of arrival (AOA), and their combinations. In wireless environment, signals from transmitters to their receivers may undergo both line-of-sight (LOS) and nonline-of-sight (NLOS) propagations. the problem of tracking a moving target using navigated mobile sensors in wireless sensor networks. With unknown target and mobile sensor locations, we need to estimate the locations of the target and the mobile sensors first. Based on a more general TOA measurement model, convex optimization algorithms through SDP relaxation are developed for localization
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