Navigation in urban environments with standalone GPS is a challenging task. In urban environments, it is probable that GPS signals are being blocked or reflected by buildings. These factors degrade position accuracy. Therefore, it is desirable to augment GPS with another sensor using a sensor fusion technique. The performance of a sensor fusion technique is highly dependent on covariance matrices and tuning these matrices is time consuming. In case of GPS, the covariance matrices may change with time and may vary in size. The expected noise in GPS measurements in urban environments is different than that of open environments. The number of visible satellites may increase or decrease with time, changing the size of measurement covariance matrix. The time and size variation of covariance matrices makes the position estimation even harder. In this paper, we propose an adaptive filter for GPS-vision fusion that adapts to time and size varying covariance matrices. In proposed method, we assume that the noise is Gaussian in small time intervals, and then use innovation sequence to derive an expression for covariance matrices. The camera image is compared with Google Street View (GSV) to obtain position. We use pseudoranges from GPS receiver and positions obtained from camera as measurements for our Extended Kalman Filter (EKF). The covariance matrices are obtained from innovation sequence and Kalman gain. The proposed filter is tested in GPS-challenged urban environments on the University of Illinois at Urbana-Champaign campus. The proposed filter demonstrates improved performance over the filter with fixed covariance matrices.