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The strength and integrity of a structure depends not only on the quality of its materials but also the health condition of its different components. In some cases, damage cracks inside the structural will be generated from excessive loads, and if these damage cracks go undetected for a long period, catastrophic failure of structures may occur. However, most of the damages at early stages are minor and difficult to detect by visual inspection. Therefore, this paper proposes a damage estimation algorithm based on unscented Kalman filter (UKF), which can identify and locate the damage parameters in real time with a limited number of sensors. Meanwhile, this algorithm is also applicable to the structural system with unknown external inputs, and can make the joint force-damage estimation. For most structures, the damage parameters are sparse in space, therefore, the sparsity of the damage parameter vector is introduced to UKF as an l1-norm constraint by the pseudo- measurement (PM) technique. Thus, unconstrained optimization of the damage parameter estimation is transformed into an l1-norm constrained optimization problem. With such improvement, the process of damage parameter estimation converges faster and the false damage parameters are effectively restrained. Moreover, in order to solve the force drift problem during force identification when only acceleration data is used, the sparse constraint of the force vector is also introduced to UKF framework by the PM technique. Finally, numerical simulations of a ten-story shear building and experiments of a three-story shear structure are used to demonstrate the performance of the proposed algorithm. The results indicate that this algorithm can achieve an accurate estimation of damage and can successfully resolve the common force drift problem.