An observation-driven method for coordinated standoff target tracking based on Model Predictive Control (MPC) is proposed to improve observation of multiple Unmanned Aerial Vehicles (UAVs) while approaching or loitering over a target. After acquiring a fusion estimate of the target state, each UAV locally measures the observation capability of the entire UAV system with the Fisher Information Matrix (FIM) determinant in the decentralized architecture. To facilitate observation optimization, only the FIM determinant is adopted to derive the performance function and control constraints for coordinated standoff tracking. Additionally, a modified iterative scheme is introduced to improve the iterative efficiency, and a consistent circular direction control is established to maintain long-term observation performance when the UAV approaches its target. Sufficient experiments with simulated and real trajectories validate that the proposed method can improve observation of the UAV system for target tracking and adaptively optimize UAV trajectories according to sensor performance and UAV-target geometry.