In order to meet the stringent Diesel-engine emission regulations, an aftertreatment system has become more and more popular in many countries. Specifically, to reduce the NOx emissions, a selective catalytic reduction (SCR) system has been successfully applied to many Diesel-engine applications. In the SCR system, NOx sensors are used to monitor the NOx concentration for the control strategies design and fault diagnosis. However, studies show that current NOx sensors are often cross-sensitive to ammonia. Thus, the reading of NOx sensor located at downstream of the SCR catalyst may not be accurate, due to the existing of ammonia slip in the system. Aiming to address this problem, some observers based on Kalman filtering theory are utilized to estimate the cross-sensitivity factor and the actual NOx concentration. Inspired by the fact that the adaptive unscented Kalman filter (AUKF) has the advantages of sample calculation and the capacity to deal with nonlinear system, the AUKF-based observer is investigated to estimate the factor in this paper. In addition, the process noise and measurement noise are usually unknown and time-varying in actual conditions. The AUKF would be effective to update the process and measurement noise covariance matrices online to adapt the practical SCR system. Simulation results through an experimentally-validated full vehicle simulator cX-Emission prove that the performance of designed observer based on AUKF is excellent. (C) 2015 Elsevier Ltd. All rights reserved.