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The optimized bias estimation model is presented herein to simultaneously estimate radar offset biases and platform attitude biases, the algorithm can estimate both offset radar biases and yaw bias well, but estimation results for roll and pitch biases are still poor. Due to the existence of coupling between attitude biases and radar measurements, the coupling influences are not constants and associated with the target location, which are contrary to the models we use in dynamic equations, so, the estimation results are poor. On the contrary, the models of radar offset biases used in Kalman filter coincide with the true situations, and the estimation performances for them are good. In view of this, in order to improve the estimation accuracy of roll and pitch biases, the non-linear Unsent Kalman Filter  may be a preferable method. In addition, if radar measurements are in an ENU coordinate system, the algorithm does not need to know the true course, pitch, and roll of the platform, which saves much bandwidth and decreases the coupling influences of attitude angles to a great extent. According to the results obtained herein, frequency dependent biases will be discussed in the future.