A navigator in this case aims to control the mobile sensor to get close to the moving target from any initial position. Since the target maneuvers are not known a priori to the controller, solving the problem requires a real-time strategy. The joint problem of mobile sensor navigation and mobile target tracking based on a TOA measurement model. Our chief contributions include a more general TOA measurement model that accounts for the measurement noise due to multipath propagation and sensing error. Based on the model, we propose a min-max approximation approach to estimate the location for tracking that can be efficiently and effectively solved by means of semidefinite programming (SDP) relaxation 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. Typically, target tracking involves two steps. It needs to estimate or predict target positions from noisy sensor data measurements. Then needs to control mobile sensor tracker to follow or capture the moving target. We study the problem of mobile target positioning in a sensor network that consists of stationary sensors and a mobile sensor. The goal is to estimate the target position and to control the mobile sensor for tracking 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.