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Unscented Kalman filter (UKF) is an important part of Kalman filter. UKF can effectively overcome the defects of the extended Kalman filter such as low estimation accuracy and poor stability. However, UKF seldom considers the influence of the gross errors, such as outlier, bias and drift. The measurements of target tracking are often affected by different kinds of gross errors, so it is of great significance to study the detection and compensation of gross errors based UKF for improving the accuracy of target tracking. In this paper, in order to decrease the influence of gross errors in the measurements on the results of target tracking, the measurement information is checked by using the Pauta criterion to detect gross errors. In order to compensate the gross errors, the results of linear fitting of residual errors are used. Through target tracking simulation and comparison, the improved UKF can effectively decrease the influence of gross errors on the state prediction procedures, and can achieve accurate target tracking, improving the stability and accuracy of filtering.