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The interacting multiple model (IMM) algorithm has been used in many applications. However, performance analysis of the IMM algorithm is difficult because it uses a set of Kalman filters that are coupled with each other. We present an algorithm to compute the means and cross-covariances of the residuals and state estimation errors of these Kalman filters. Specifically, we derive the cross-covariances, each of which is the covariance of the residuals of two Kalman filters, to account for the mutual interactions. From the means and cross-covariance terms, we then compute the means of the likelihood functions and the mean-squared estimation errors as performance measures of the IMM algorithm. [ABSTRACT FROM PUBLISHER]