The disclosure belongs to the technical field of integrated navigation under non-ideal conditions, and in particular relates to a robust filtering method for integrated navigation based on a statistical similarity measure (SSM). In view of the situation that there are normal beam measurement information of the DVL and beam measurement information with a large error simultaneously in a SINS/DVL tightly integrated system, and aiming at the problem that the existing robust filters of an integrated navigation system process the measurement information in a rough manner and are likely to lead to loss of normal measurement information, the disclosure proposes a novel robust filtering method based on decomposition of multi-dimensional measurement equations and the SSM. The disclosure introduces the SSM theory while decomposing the multi-dimensional measurement equations of the SINS/DVL tightly integrated navigation system, and assists the measurement noise variance of each beam to complete respective adaptive update in case of a large measurement error, finally ensuring independence of processing of the measurement information of each beam. The disclosure can be used in the field of integrated navigation of underwater vehicles under non-ideal conditions.