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This paper reports the development and experimental evaluation of two in situ least squares techniques for estimating the alignment matrix of Doppler sonars commonly used for precision navigation of oceanographic submersibles. Most previously reported methods addressed the problem of single degree-of-freedom heading alignment using bottom-lock Doppler sonar data and global positioning system (GPS) navigation data. This paper reports and evaluates two techniques for three degree-of-freedom calibration of attitude and Doppler sonar sensors using sensor data available to vehicles at full ocean depth. The first technique provides a general linear least squares estimate of the alignment matrix. The second technique results in a least squares alignment matrix estimate constrained to the group of rotation matrices. The performance of these estimates is evaluated with a laboratory remotely operated vehicle (ROV) and a field-deployed autonomous underwater vehicle (AUV). Experimental results are reported which demonstrate that Doppler navigation employing the reported alignment calibration techniques significantly improves navigation precision. The experiments show that the latter technique provides calibration estimates that improve Doppler navigation precision not only on the calibration data set itself, but also provide improved precision over a wide variety of vehicle trajectories other than the calibration data set.